Bibliographic record
Abstract
Abstract The Fire Weather Index System has been in use across Canada for the past 30 years in the daily operations of fire management agencies. As part of this system, the Drought Code (DC) was developed to act as a daily index of water stored in the soil. A major obstacle to the completion of climate risk analyses on the DC is that lengthy series of daily temperature and precipitation are not available for large portions of the circumboreal forest. Here the authors present a methodological modification to the daily DC to allow its approximation using monthly data. This new Monthly Drought Code (MDC) still retains its ability to capture moisture trends in deep organic layers. On the basis of high-resolution temperature and precipitation data, an analysis of summer moisture availability across Canada over 1901–2002 is presented. The driest periods on record were from the 1920s to the early 1960s, with the driest years being 1955, 1958, and 1961. The wettest period was from the mid-1960s to the 1980s. For the century-long period, drying was statistically significant in northern Canada. Locations south of the Hudson Bay, in the eastern Maritimes, and in western Canada recorded a trend toward decreasing dryness. When analyzed over 1951–2002, trends could hardly be distinguished from the (multi) decadal variability. Annual values of a spatial average of all July MDC grid cells showed an excellent fit against fire statistics: 63% of the variance in the Canada-wide annual area burned from 1959 to 1999 was explained by summer moisture availability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".