Investigation of temporal trends in fire occurrence in Ontario with consideration of monthly teleconnections
Bibliographic record
Abstract
Climate change can impact various facets of a region’s fire regime, such as the frequency and timing of fire ignitions. This study investigates the temporal trends of monthly fire counts in the northwest region of Ontario, Canada, between 1960 and 2023. Fires ignited by human activities or lightning are analysed separately. The significance of trends are determined using the trend-free pre-whitened Mann-Kendall test with a Thiel-Sen slope estimate, and are contrasted with those obtained using the Cochrane-Orcutt method. Both of these approaches consider and adjust for autocorrelations in the time series data. We also consider the forecasting of future monthly fire counts using a Negative Binomial Auto-Regressive (NB-AR) model suitable for count time series data with the presence of overdispersion while investigating the use of climate teleconnections such as ENSO, NAO, AO, PDO, and AMO as predictors at differing temporal lags. Several candidate models having varying time lags for each predictor are identified and their predictive skills are quantified through cross-validation estimates of Mean Absolute Error. These models omit months when there are historically no or few ignitions and use monthly indicator functions to capture seasonal trends alongside an AR(1) term of the previous month's count. We find that a model with 6-month-lagged AMO is the most suitable for forecasting counts of human-caused fires, while no teleconnection predictor was found to be significant when forecasting lightning counts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".