Analyzing Fire Ignition Data in the Kamloops, Lillooet and Merritt fire zones : with implications toward the effects of fire suppression on the landscape.
Bibliographic record
Abstract
Understanding historic fire regimes in the dry forests of southern British Columbia has been the cause of contentious debate, with implications that will continue to influence the approach to wildfire management in the area. Making use of lighting-strike and human-caused ignition data from 1998 through 2012 for the Kamloops, Lillooet and Merritt fire zones, this study analyzes records on both spatial and temporal scales and draws connections between ignitions and the distribution of climatic zones and fuel types on the landscape. Using fire weather data for the Kamloops zone, individual fire events were then assessed for their potential behaviour in the absence of fire suppression. For the 2365 ignitions included in this study, 58% were attributed to human causes, which accounted for 76% of the total area burned. Fire numbers were disproportionately high in lower elevation ecosystems, but had larger impacts in upper elevation forests. The most telling result is that 92% of all fires did not make it over four hectares in size, either as the result of aggressive suppression or weather conditions at the time of ignition. This absence of large-scale events provides no natural fuel mitigation across the landscape, and will allow stands to become more densely structured and host much more severe wildfires.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".