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 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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".