Each fire was backtested on real CEC transmission topology with ERA5 weather and CAISO hourly load data. Entropy is calculated per-edge from weighted physical measurements, then evolved through DON substrate dynamics. The system routes power through lowest-entropy paths via DON-native beam search. 13 of 14 fires detected. 140 of 140 fatalities covered. Zero false positives across 300 non-fire topology-days.
Raw entropy measures grid conditions at each hourly snapshot. DON substrate evolution tracks how entropy builds and persists over time through spatially-coherent feedback — edges in high-entropy neighborhoods amplify each other, while isolated spikes decay. This recovers 3 additional fires (Cherokee, Valley, Jerusalem) whose raw peaks fell just below the bifurcation boundary (θc = 0.42) but showed sustained spatial coherence that DON evolution identified as genuine collapse risk. Mean DON boost: +14.1% across all 14 fires.
Sorted by fatalities. Green = detected by raw entropy. Amber = recovered by temporal evolution. Gray = not detected. Four fires with detailed charts are marked below.
Detection threshold (θc): 0.42. Green tiles detected by raw entropy alone. Amber tiles recovered by DON substrate evolution (raw peak below θc, DON-evolved peak above). All 14 fires detected after DON evolution. 140/140 fatalities covered. Full detection data and entropy charts for four featured fires shown below.
The deadliest wildfire in California history. 85 people killed. 18,804 structures destroyed. $16.5 billion in damage. PG&E transmission equipment ignited the fire at Pulga at 6:15 AM. The backtest ran the system with ERA5 reanalysis weather fetched at each of the grid's 104 node coordinates on a 104-node grid topology derived from California Energy Commission GIS data covering the Butte County transmission network.
DON-native beam search evaluates per-edge Hamiltonian action across K candidate paths, selecting the minimum-action route through the network. The routing engine continuously steers power through lowest-entropy paths — the decision is deterministic: same inputs, same topology, same result every time. The Camp Fire's raw peak (0.4220) barely crosses θc, but DON substrate evolution amplifies it to 0.4790 through spatially-coherent feedback — the signature of a network entering collapse.
Four people killed. 204 structures destroyed. PG&E equipment caused the ignition. Backtested on a 33-node Shasta County CEC transmission topology — the sparsest grid in the dataset. DON substrate evolution amplified entropy to 0.5819 peak (+17.2% above raw) — the strongest DON boost in the 14-fire corpus. Nearly the entire 33-node network crossed θc simultaneously.
The Zogg Fire represents near-total network collapse — so many edges are simultaneously above θc that the DON beam search has limited safe alternatives. 16 of 24 hours were flagged as high-risk. On sparse grids, the system signals that de-energization may be the only safe response. The +17.2% DON boost (raw 0.4963 → DON 0.5819) reflects the entire network entering a coherent collapse state.
374 structures destroyed. PG&E transmission equipment caused the ignition near Geyserville. Backtested on a 261-node Sonoma County CEC topology — the densest grid in the dataset. DON-evolved entropy above θc for 15 of 24 hours with first detection at 9:00 AM. The dense grid provided many alternative paths for the DON beam search.
The 261-node Sonoma County grid has 546 edges — many possible paths between any two points. The DON beam search evaluates K candidate paths per routing cycle, selecting minimum-action routes through the network. The dense topology provides enough alternatives that elevated entropy is navigated around rather than through.
The largest single fire in California history. 1,329 structures destroyed. PG&E equipment caused the ignition. Backtested on the same 104-node Butte County CEC topology as the Camp Fire — sharing the same transmission corridors. A summer fire with lower winds than the autumn Diablo wind events. DON-evolved peak entropy 0.5236 with 13 hours above θc.
The Camp Fire (November 2018) and Dixie Fire (July 2021) occurred on the same 104-node Butte County grid. Running independently — different weather data, different date, different conditions — the DON beam search routes around the same structural vulnerabilities both times. This is physics finding the same collapse-susceptible corridors in the grid, not a trained model remembering a pattern.
Most fires show diurnal entropy oscillation — rising with afternoon heat and wind, falling overnight. The Camp Fire did not. Its entropy trajectory was flat, sustained at elevated levels around the clock. This is the signature of a system trapped at the bifurcation boundary — decoupled from the solar cycle that normally drives weather patterns.
Flatness = avg/peak ratio. A value of 0.953 means the average entropy was 95.3% of the peak — virtually no variation. The coefficient of variation (CV) of 0.04 is the lowest in the 14-fire corpus. The Camp Fire is the only fire classified as a PLATEAU morphology. All other fires show MULTI-PEAK (diurnal oscillation) or SPIKE (rapid onset/recovery) patterns.
Peak entropy does not predict fire severity (r = 0.009 — effectively zero correlation). But LDEE, a composite measure of how entropy crosses and persists at the bifurcation boundary, shows strong correlation with fire outcomes (r = 0.923, p = 0.000002). This is not a death predictor — it is a severity indicator derived from collapse dynamics.
LDEE = crossings × persistence × dash_hours. It captures three physical properties: how many times entropy crosses the bifurcation boundary (crossings), what fraction of above-threshold time is contiguous (persistence), and total hours above threshold (dash_hours). A high LDEE means the system is repeatedly entering and sustaining collapse conditions — not just briefly spiking above the boundary.
Left number: fatalities. Right number: LDEE value. The Camp Fire is a massive outlier (LDEE = 56.2) — it crossed the bifurcation boundary 5 times, with 62.5% persistence, sustaining 18 hours above threshold. Four fires with zero LDEE (Valley, Butte 2015, Jerusalem, Rocky) are omitted — they never crossed the boundary in raw analysis.
The Tubbs Fire (22 deaths, LDEE = 0.7) is the notable outlier in the correlation. Its high death toll resulted from population density and evacuation dynamics in the Santa Rosa urban interface — not from sustained grid collapse conditions. LDEE measures grid field dynamics, not population exposure. This distinction is important: the indicator reflects collapse severity, not community vulnerability.
Grid topology: Three regional CEC transmission networks — Butte County (104 nodes, 224 edges), Shasta County (33 nodes, 68 edges), and Sonoma County (261 nodes, 546 edges) — derived from California Energy Commission GIS public data (6,839 features). Weather: ERA5 reanalysis hourly data fetched at each node's geographic coordinates via Open-Meteo archive API. Load: CAISO historical demand data. Load: CAISO OASIS API hourly data (2009+), scaled to local grid capacity. Detection threshold θc = 0.42 is the DON Theory spectral bifurcation boundary, empirically validated across 14 fires. DON substrate evolution amplifies spatially-coherent entropy. Mean boost: +14.1% across all 14 fires. False positive rate: 0% (0/300 topology-days) with unified detection gate, tested across 100 non-fire reference dates on all three topologies.
CEC Transmission Lines GIS Dataset • Open-Meteo ERA5 Archive • CAISO Open Access • Backtest run: February 2026The backtest demonstrates detection capability on historical data. The next step is a controlled pilot on a live grid segment to validate performance under operational conditions. We are seeking utility and regulatory partners for that pilot.
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