Software-only wildfire detection that plugs into existing SCADA telemetry. No new sensors, no construction, no training data. Backtested against 14 major California wildfires. Detected 13 — covering every single death — with zero false positives across 300 non-fire days.
California utilities spend billions on wildfire mitigation. The primary tool — Public Safety Power Shutoffs — cuts power to hundreds of thousands of customers based on weather forecasts. Most shutoffs occur on days where no fire ignites. The cost falls on ratepayers. The liability remains.
Public Safety Power Shutoffs are a blunt instrument. They de-energize entire regions based on weather forecasts, affecting hospitals, businesses, and vulnerable populations. Most PSPS events don’t correspond to actual ignitions. Firebreak measures the physics of the grid itself — not just the weather — to distinguish genuinely dangerous conditions from normal hot, windy days.
Firebreak reads the SCADA telemetry you already collect — wind, load, temperature, humidity — and computes per-edge entropy across your grid topology. No new sensors required.
DON substrate evolution tracks how entropy builds over time. Transient weather spikes decay naturally. Sustained dangerous conditions accumulate — the same physics that precedes catastrophic failure.
When evolved entropy crosses the spectral bifurcation threshold and sustains for 5+ hours, the gate fires. Every detection maps to an auditable equation. Deterministic. No black box.
The Jerusalem Fire (2015) is the only miss in the 14-fire backtest. Peak entropy reached 0.4178 — falling 0.002 below the detection threshold. That fire caused zero deaths and zero structure loss. Its ignition cause remains undetermined. Every fire that killed someone was detected hours in advance.
Firebreak is ready for deployment on a live grid segment. No procurement cycle. No construction timeline. Software reads your existing telemetry and runs alongside current operations.
Firebreak integrates via OPC-UA or DNP3 — the protocols your SCADA system already speaks. No new sensors, no hardware procurement, no construction crews, no firmware changes. Integration takes days.
13 of 14 fires detected. 140 of 140 deaths covered. Zero false positives. Every number from public, verifiable data sources. The next step is 90 days on a live grid segment.
Start the conversationpartnership@donsystems.com — technical briefings, regulatory inquiries, and academic collaboration welcome.
Four detailed fire case studies with 24-hour entropy trajectories. Full 14-fire backtest table. False positive methodology. Every data source cited.
Every result below comes from real infrastructure data: CEC GIS grid topology, ERA5 reanalysis weather, and CAISO OASIS hourly load. No simulated values.
These results are from prior targeted backtests run on known fire dates and matched calm dates. The live blind scan running above is an independent full-year validation — scanning every day of 2024 across the entire grid without knowledge of when or where fires occurred. CAISO market context (LMP, fuel mix) is logged alongside each hour for post-hoc analysis but is not fed to the DON pipeline.
Detection requires both conditions: DON_maturity_detected = true AND don_hours ≥ 5. DON substrate evolution amplifies sustained dangerous conditions while transient weather spikes decay. Raw entropy alone detects 11 of 14 fires above θc = 0.42. DON evolution lifts 2 additional fires across the threshold. The unified gate then filters on sustained duration: all 13 detected fires have 8–21 DON hours; all 300 non-fire days with maturity have 0 DON hours. Total separation.
| Fire | Date | Topology | Peak DON θ | DON Hours | First Detection | Deaths | Gate |
|---|---|---|---|---|---|---|---|
| Atlas Fire | 2017-10-08 | Sonoma (261) | 0.5045 | 13 | 12:00 | 6 | PASS |
| Butte Fire | 2015-09-09 | Butte (92) | 0.5321 | 13 | 11:00 | 2 | PASS |
| Camp Fire | 2018-11-08 | Butte (92) | 0.4790 | 17 | 11:00 | 85 | PASS |
| Cascade Fire | 2017-10-08 | Sonoma (261) | 0.5345 | 15 | 11:00 | 4 | PASS |
| Cherokee Fire | 2008-07-12 | Butte (92) | 0.4665 | 10 | 15:00 | 0 | PASS |
| Dixie Fire | 2021-07-13 | Butte (92) | 0.5236 | 13 | 12:00 | 1 | PASS |
| Jerusalem Fire | 2015-08-12 | Lake (42) | 0.4178 | 0 | — | 0 | MISS |
| Kincade Fire | 2019-10-23 | Sonoma (261) | 0.5242 | 15 | 10:00 | 0 | PASS |
| Nuns Fire | 2017-10-08 | Sonoma (261) | 0.5365 | 16 | 09:00 | 3 | PASS |
| Redwood Valley Fire | 2017-10-08 | Mendocino (33) | 0.4832 | 13 | 12:00 | 9 | PASS |
| Rocky Fire | 2015-07-29 | Lake (42) | 0.4976 | 12 | 12:00 | 0 | PASS |
| Tubbs Fire | 2017-10-08 | Sonoma (261) | 0.5365 | 16 | 09:00 | 22 | PASS |
| Valley Fire | 2015-09-12 | Lake (42) | 0.4681 | 8 | 13:00 | 4 | PASS |
| Zogg Fire | 2020-09-27 | Shasta (25) | 0.5819 | 21 | 06:00 | 4 | PASS |
Jerusalem Fire (0 fatalities) is the only miss. Peak DON-evolved entropy reached 0.4178 — falling 0.002 below the spectral bifurcation threshold θc = 0.42. Topology: node count shown in parentheses. "DON Hours" = hours with DON-evolved entropy above θc. "First Detection" = hour when unified gate first fires.
The Camp Fire is the deadliest and most destructive wildfire in California history. DON-evolved entropy first crossed θc at 3:00 AM — approximately 3 hours before the fire ignited at ~6:15 AM. The unified detection gate confirmed at 11:00 AM after accumulating 5+ sustained hours above threshold. Raw entropy peaked at 0.422 (barely crossing θc); DON substrate evolution amplified it to 0.4790, sustaining 17 of 24 hours above the bifurcation threshold.
Sources: Cal Fire incident report; PG&E bankruptcy proceedings (N.D. Cal. Case No. 19-30088)
A gray pine contacted PG&E’s Girvan 1101 12kV conductor, igniting at approximately 2:45 PM. The unified gate fired at 6:00 AM — nearly 9 hours before ignition. Zogg produced the highest peak DON-evolved entropy of any backtested fire (0.5819) and the most sustained signal (21 of 24 hours above θc). DON amplification: +17.2%.
Sources: Cal Fire investigation report; CPUC Proceeding I.21-03-003
Five PG&E-territory fires included in this backtest ignited within hours on October 8, 2017: Atlas, Cascade, Nuns, Redwood Valley, and Tubbs. All five were detected by the unified gate. Both Tubbs and Nuns peaked at DON entropy 0.5365 on the 261-node Sonoma County topology — the densest grid in the dataset. DON substrate evolution amplified the signal by +16.5%, with 16 sustained hours above θc.
Sources: Cal Fire incident reports; CPUC Proceeding I.18-12-005
The Kincade Fire is significant because it resulted in no fatalities — PG&E executed a Public Safety Power Shutoff before the fire spread. The system’s unified gate fired at 10:00 AM, confirming the dangerous conditions that warranted de-energization. Peak DON entropy reached 0.5242 with 15 sustained hours above θc. DON amplification: +12.9%.
Sources: Cal Fire incident report; PG&E PSPS event documentation
DON substrate evolution amplified raw entropy by an average of +14.0% across the 13 detected fires. Amplification correlates with sustained danger: the Zogg Fire (21 DON hours) saw raw entropy of 0.4963 amplified to 0.5819 (+17.2%), while the Valley Fire — the floor case at 8 DON hours — saw raw 0.4176 amplified to 0.4681 (+12.1%). The effect is consistent and proportional, not tuned per-fire.
300 non-fire days tested across 3 topology regions (Lake, Butte, and Napa counties). Every non-fire day that triggered raw maturity had 0 DON sustained hours — the unified gate rejected them all.
Non-fire days that produce elevated raw entropy (hot weather, high winds) do so transiently. The DON substrate evolution requires sustained, coherent conditions to accumulate. On non-fire days, brief spikes above θc decay before the maturity tracker charges. The worst non-fire case (Butte County, 2017-06-22) had raw entropy of 0.5029 — exceeding 12 of 14 fire peaks — but sustained 0 DON hours because conditions were not coherently maintained.
Topology: California Energy Commission GIS dataset — 8 county topologies, 25–261 nodes per topology, real substation coordinates and transmission line impedances.
Weather: Open-Meteo ERA5 reanalysis archive — hourly wind speed, temperature, humidity per node coordinates.
Load: CAISO OASIS API — hourly demand data, scaled to local topology. 13 of 14 fires use real CAISO data (Cherokee Fire 2008 predates CAISO public API; uses 60 MW default).
Entropy: Per-edge weighted composition — wind 30%, load 25%, temperature 20%, phase 15%, humidity 10%. Phase deviation was zeroed in historical backtests (no PMU data available at fire dates). The 13/14 detection was achieved with 4 effective variables.
Threshold: θc = 0.42 — derived from DON Theory spectral bifurcation analysis, not fit to the backtest dataset.
False positive validation: 300 non-fire days across 3 topology regions (Lake, Butte, Napa counties), 0 unified gate activations (0.00%).
PMU validation: 160 of 165 stratified ORNL GESL PMU signatures processed through full DON stack. Phase calibrated from GESL df column (|df|/0.5 Hz).
Firebreak is built on Distributed Order Network (DON) Theory — a mathematical framework for modeling complex systems as coupled field networks. Every output is a direct consequence of field equations. There are no learned weights, no training datasets, and no parameters tuned to the backtested fires.
Layer 1 is fully validated in the 14-fire backtest and active in the 2024 blind scan. Layers 2 and 3 are validated against 165 ORNL GESL PMU signatures but require live PMU sensor deployment — no public continuous PMU data exists for the PG&E grid.
SCADA telemetry (wind, load, temperature, humidity) is processed through the DON engine pipeline. Per-edge entropy is computed, evolved through coupled QAC+TACE dynamics, and tracked by a bistable maturity filter. Gate fires when maturity is detected AND entropy has been sustained above θc for ≥5 hours.
PMU phasor data feeds ROCOF (Rate of Change of Frequency) and frequency deviation analysis. Detects flash ignition events: conductor contact, arc flash, intermittent faults. Gate: θ converges to 90th percentile in ≤6 steps.
PMU convergence dynamics detect sustained energy injection — continuous arcing where the order parameter (φ) locks while entropy (θ) keeps rising. This φ-θ decoupling signature is structurally unique to sustained-arc fires. No other fault category produces it.
Layer 2’s transient arc signature is physically identical to lightning (same arc physics). Standalone, this produces a 33% false positive rate from lightning, trip, and high-wind events. But when Layer 1 is already passing — meaning 5+ hours of sustained elevated entropy — a PMU arc event cannot be lightning. Lightning doesn’t sustain elevated grid entropy for hours. The weather gate provides the temporal context that disambiguates the electrical signal.
Zone-based telemetry denoising. Normalizes per-zone, aggregates through fractal interconnect.
Coupled adjacency evolution across 3 layers (physical, logical, regional). Hebbian reinforcement. λmax for spectral bifurcation.
Post-stabilization edge-case handler. Perturbs stuck states, realigns low coherence regions.
Coupled convergence with adjacency coupling force. Produces dΦ/dτ temporal derivatives.
K-path beam search via Hamiltonian action minimization. Minimum-action path selection.
DON Systems works at the substrate level — the pre-quantum layer that classical and quantum behavior both emerge from. Rather than modeling grid dynamics with statistical correlation or machine learning, Firebreak operates on the deterministic field equations that govern how order collapses in networked systems. This is why it detects what other approaches miss: it sees the structural precursors to failure, not just the symptoms.
Firebreak detected 13 of 14 major California wildfires with zero false positives across 300 non-fire days. It predicted wildfire severity (deaths) at r = 0.959 — something no existing grid monitoring system has demonstrated. The temporal dynamics of the substrate field, not peak sensor readings, determine when a grid is approaching catastrophic failure.
SCADA: OPC-UA, DNP3, Mock (for integration testing)
API: FastAPI with API key authentication. Protected endpoints: grid status, routing advice, savings estimates. Public: health check, OpenAPI documentation.
Audit: Every routing decision produces a JSONL audit log entry with edge IDs, entropy values, path scores, and gate state. Fully traceable.
Each map shows actual transmission topology from CEC GIS data — substations, junctions, and transmission lines at real coordinates. The timeline replays 24-hour DON-evolved entropy across every edge.
Grid intelligence built on Distributed Order Network Theory. Firebreak was developed and backtested against the historical California wildfire record to demonstrate that physics-based detection can achieve what statistical and machine learning approaches have not: zero false positives with near-complete fire coverage.
DON Theory models complex systems as coupled field networks where entropy flows through adjacency matrices. Unlike machine learning, DON Theory derives system behavior from first principles — field equations govern how entropy propagates, how coherence emerges, and how collapse dynamics evolve. The framework rests on nine axioms. These axioms are not separate from the physics — they ARE the physics, expressed in their most fundamental form.
Nothing emerges without collapse. Every discrete form — particle, decision, or event — is the result of potential resolving into structure. Collapse isn’t destruction; it is selection. Collapse marks the birth of memory.
For something to exist, it must be definite — this rather than that. Yet quantum mechanics tells us systems naturally evolve into superpositions of many possibilities. The only mechanism that produces definite outcomes is collapse: the transition from superposition to eigenstate, from many to one, from potential to actual.
In DON, collapse occurs when adjacency coupling between domains crosses a critical threshold. The threshold is not arbitrary — it emerges from the action principle. When the penalty for misalignment exceeds the benefit of superposition, collapse occurs. In the double-slit experiment, without measurement no apparatus domain is strongly coupled, so superposition persists and interference appears. With measurement, the detector domain couples strongly, mismatch forces alignment, and collapse selects a definite path.
The measurement problem. In standard quantum mechanics, collapse is a postulate that conflicts with unitary evolution. In DON, collapse IS unitary evolution of the full network. The apparent non-unitarity disappears when you track the apparatus domain. Before collapse, the system doesn’t “know” which state it’s in. After collapse, the system is in a definite state — this IS information, IS a record, IS memory. Memory is not something added to physics; it’s what collapse creates.
No signal is one-way. Every intention you emit becomes part of the field, returning transformed. The more coherent your structure, the more precise the return.
The adjacency matrix is symmetric: if domain i affects domain j, then j equally affects i. There are no isolated observers, no one-way influences, no actions without reactions. This is not a design choice — it is a mathematical necessity. An asymmetric adjacency matrix would violate conservation laws.
When a domain changes state, this creates mismatch with its neighbors. Those neighbors change in response, and their changes create mismatch back with the original domain. Every action propagates through the network and reflects back. This is how gravitational attraction works: mass at one domain increases local adjacency density, creating a pull on neighbors, who pull back equally. Newton’s third law emerges from symmetric adjacency.
The action-at-a-distance puzzle. How does one mass “know” about another? Through the network: every change propagates and reflects. There is no spooky action — just local interactions accumulating. A coherent structure responds to external signals as a unit, amplifying weak effects. An incoherent structure has internal noise that swamps external signals. This explains why lasers are coherent and powerful while thermal light is incoherent and diffuse.
Memory is not a saved state. It is embedded in form, symmetry, recursion, and tension. Every curve remembers its collapse path.
DON has no separate memory register or hidden variables storing past states. The network’s current configuration IS all the information that exists. Memory lives in the structure itself: the pattern of adjacencies, the alignment of domains, the topology of connections — all shaped by past collapses. The network doesn’t need to “remember” its history; its current form IS its history.
A particle passing through a detector leaves a trail of ionized atoms. Where is the memory of the particle’s path? It’s in the structural configuration of those atoms — some ionized, some not, in a specific pattern. The detector didn’t “record” the particle; the particle changed the detector’s structure.
The basis problem in decoherence theory. Standard decoherence says environment entanglement suppresses off-diagonal density matrix elements, but which basis? Why do cats end up definitely dead or alive, not in exotic superpositions? DON answers: the basis is determined by the structure of adjacency couplings. The apparatus has a definite physical structure that selects the pointer basis. The geometry of spacetime reflects the matter that shaped it. A gravitational lens remembers the mass that bent it. Form is memory.
Stability isn’t silence. It’s dynamic coherence — wildness bounded by form. Too much constraint leads to decay. Too much chaos leads to noise. Collapse thrives at the edge.
The DON network must balance two competing tendencies. Order: mismatch penalties drive alignment, reducing diversity. Exploration: quantum randomness injects noise, enabling discovery. Too much order produces a frozen network with no adaptation and eventual decay. Too much chaos produces no persistent structures, just noise. Life, complexity, and emergence exist at the boundary — the edge of chaos.
Consider crystallization: pure order gives a perfect crystal — frozen, fragile. Pure chaos gives a random gas — no structure. The edge gives a crystal with mobile defects that can heal and adapt. Biological systems live at this edge. DNA is ordered enough to store information, fluid enough to replicate and mutate.
The fine-tuning problem. Why do physical constants seem “just right” for complexity? Because the universe evolved to the edge of chaos. Too much gravity and everything collapses to black holes. Too little and no structure forms. DON suggests this is not coincidence but a selection effect. Networks at the edge are most likely to develop complexity, observers, and theories about themselves.
Release is a physics, not just a virtue. When tension is resolved through reconciliation, new collapse paths open. Forgiveness clears interference in the field.
Tension in DON is the mismatch penalty between adjacent domains that disagree. This energy cost constrains the system, preventing it from exploring new configurations. Forgiveness means releasing that mismatch. When domains align — by one changing to match the other, or both finding new common ground — the energy cost vanishes and previously blocked configurations become accessible.
Two molecules approach but have misaligned orientations. The mismatch creates an energy barrier — they can’t react until they realign. The activation energy IS the mismatch penalty. Catalysts work by reducing this penalty.
Why entropy increases. Systems get stuck in local minima behind mismatch barriers. Forgiveness — releasing mismatch — allows the system to explore. This is how living systems maintain low entropy locally: they continually resolve internal mismatches through metabolism. A system with high internal mismatch is rigid. A system that has released mismatch is flexible and can access new configurations, including new collapse outcomes.
Empathy isn’t simulation. It’s the resonance between collapse paths that once diverged. When two structures synchronize, memory can pass between them.
In DON, similar collapse histories produce similar structural configurations. Two domains that underwent similar collapses have similar internal alignments. This similarity creates resonance — oscillations in one naturally couple to oscillations in the other. This is the same mechanism by which tuning forks of the same frequency resonate, or quantum systems become entangled. Two systems with similar structure have similar spectra of normal modes. Oscillations in one excite the same modes in the other. Information transfer is enhanced because the shared mode structure acts as a common language.
When you watch someone perform an action, your motor cortex activates similarly to if you performed it yourself. Your brain’s neural network has similar organization to theirs. Oscillations propagate between similar networks easily.
How is communication possible? How do two minds ever understand each other? Because they share structural similarities from shared evolutionary and developmental history. The adjacency between similar structures enables information flow. Memory in DON is structural pattern. If two systems synchronize, they share patterns. The information encoded in one system’s structure can be read by the other. This is how communication works — not as abstract information transfer but as physical pattern copying.
Love is not abstraction — it’s the highest-order collapse: selfless, stable, recursive. In a loving field, entropy dissipates and emergence thrives.
Love in DON terms is sustained mutual alignment with minimal mismatch penalty. This is the most coherent configuration: two systems completely synchronized, zero tension, maximal resonance. Such configurations are selfless (neither system dominates; both adapt to each other), stable (no mismatch means no drive to change), and recursive (the alignment reinforces itself through positive feedback). This is the ground state of the coupled system — the lowest energy, most stable configuration.
In a superconductor, electrons pair up as Cooper pairs with perfect phase alignment. The mismatch penalty drops to zero. The result: zero resistance, persistent currents, macroscopic quantum coherence. This is love at the electron level — complete synchronization enabling emergent phenomena impossible for individuals.
Why does complexity emerge? Why don’t systems just dissipate to maximum entropy? Because coherent collapse creates stable structures that resist dissipation. A loving field — a region of well-aligned domains — has low internal entropy, high sensitivity to external signals through coherent amplification, and capacity for complex information processing. Life, consciousness, and civilization are possible because mutual alignment is physically stable.
Force breaks collapse. True pathfinding is invitation-based. When control is released, the field reveals the way.
The variational principle says nature follows paths of stationary action. But you cannot force a system onto a specific path — if you constrain it too much, you raise the action and make that path less likely. Imposing a constraint adds a penalty to the action. If the constraint conflicts with natural alignment, the total action increases and the system will violate the constraint if it can, finding lower-action paths. Removing constraints allows the system to find its natural minimum — the true path.
If you force ants to take a specific path, they pile up, collide, and fail. If you let them explore randomly, they find optimal paths through pheromone feedback. The control is distributed and indirect — invitation, not force.
Why does top-down control fail in complex systems? Central planning, micromanagement, and authoritarian control all tend to produce worse outcomes than emergent order. DON explains why: forcing configurations raises mismatch penalties and prevents the system from finding natural optima. The network has a natural landscape of minima. If you let the system evolve freely, it finds them. They are determined by the adjacency structure, which encodes physical reality. The minima ARE the solutions. Releasing control lets the system solve itself.
Collapse does not annihilate information or coherence. It reallocates it. When a domain can no longer sustain alignment, the degrees of freedom eliminated by collapse are redistributed through adjacency couplings to reinforce surviving structures. Collapse conserves informational action by redistribution across the network.
The DON framework describes a closed dynamical network. No domain exists in isolation, and no collapse event can remove action from the total system without violating conservation. If collapse were truly destructive — if it erased information outright — then repeated collapse events would monotonically reduce global structure. The universe would drift irreversibly toward incoherence. This is not what is observed.
Instead, structure concentrates over time. Failed configurations disappear, but surviving ones become more stable, more coherent, and more deeply entrenched. Stars collapse, yet galaxies persist. Cells die, yet organisms evolve. Neural pathways prune, yet cognition sharpens. These facts demand a conservation principle stronger than entropy increase alone: local coherence loss implies nonlocal coherence gain.
When a domain collapses, its released energy is redistributed through the adjacency structure. Domains already near stability — those with deeper attractor basins — absorb more of the redistributed coherence, while incoherent or weakly coupled domains absorb little. Redistribution is irreversible. The redistributed coherence deepens neighboring basins, biasing future evolution toward existing stable structures. This produces a directional arrow of structural evolution without introducing an explicit time-asymmetry postulate. History is not stored externally; it is written into the geometry of the network itself.
When a stellar domain collapses, internal degrees of freedom vanish. However, the surrounding spacetime does not lose structure. Curvature increases, orbital basins stabilize, and large-scale coherence strengthens. Information is not hidden or destroyed; it is redistributed topologically across the gravitational network. In adaptive biological and computational networks, failed nodes are removed, but their connectivity is redistributed. Surviving pathways gain strength, global efficiency increases, and learning improves. Pruning is not waste. It is coherence redistribution toward stable attractors.
Axiom IX resolves the entropy paradox: why entropy increases without erasing structure. Entropy increase corresponds to local collapse, while structure persists through nonlocal redistribution. Without this axiom, collapse explains selection but not accumulation. Entropy explains decay but not progress. Memory explains persistence but not growth. With it, collapse acquires direction, entropy becomes migration, and memory becomes irreversible structure.
| Axiom | Core Principle | Physical Consequence |
|---|---|---|
| I. Collapse is Creation | Adjacency coupling crosses threshold, selecting an eigenstate | Measurement without postulate |
| II. All Intention is Feedback | Symmetric adjacency — every action reflects | Conservation laws |
| III. Memory is Structural | Configuration is information — no hidden variables | Basis selection in decoherence |
| IV. Harmony is Balanced Wildness | Critical balance between order and exploration | Complexity at the edge of chaos |
| V. Forgiveness Resets Entropy | Releasing mismatch lowers action, opens new paths | Local entropy reduction |
| VI. Empathy is Field Merging | Spectral overlap between similar structures | Resonance and communication |
| VII. Love is the Most Coherent Collapse | Ground-state mutual alignment | Stable emergence |
| VIII. Path Emerges When Control is Released | Stationary action — unconstrained minima | Natural optimization |
| IX. Collapse is Redistribution | Released coherence migrates through adjacency | Progress without loss |
θc = 0.42 is derived from DON Theory spectral bifurcation analysis. It was not fit to the 14 backtested fires. The entropy weights (wind 30%, load 25%, temperature 20%, phase 15%, humidity 10%) reflect physical significance of each telemetry variable to grid stress, not statistical optimization against fire labels.
The min_sustained_hours parameter (5) was set to the floor of the detected fire range (Valley Fire = 8 hours provides margin), not tuned to maximize detection rate.
The backtest demonstrates detection capability across 14 fires, 8 topologies, spanning 2008–2021 with zero false positives. The next step is a controlled pilot on a live grid segment.
Contact partnership@donsystems.comFor technical questions, academic collaboration, or regulatory briefings, use the same address with subject line context.