The Effects of the Non-Climatic Inhomogeneities in Surface Weather Station Records on Long Term Trends in Canadian Fire Weather Index System Codes.
Bibliographic record
Abstract
With a wide interest in the effects of climate change on forest fire regimes, there has been a proliferation of studies analyzing the past fire weather trends using daily surface weather stations data. However, not all studies take account of such data's inherent inconsistencies (inhomogeneities) from non-climatic influences. This study shows their influence on the linear trends in the Canadian Forest Fire Weather Index System (CFFWIS) codes, commonly used as a fire weather proxies worldwide. Trend significance of up to 50\% of the Canadian weather stations considered was affected if computed using the homogenized data instead of the raw inhomogeneous records. Duff Moisture Code (DMC) is the most resistant to weather record inhomogeneities, followed by the Drought Code (DC). Monthly Drought Code (MDC) is recommended as an optimal fire weather proxy for long term analysis, as monthly data homogenization produces results that are superior to daily data homogenization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".