VICTOAIA. • IC-X_ll1" " IllS A USER GUIDE TO NATIONAL STANDARDS AND PRACTICES
Bibliographic record
Abstract
Weather elements affecting the calculation of the Canadian Forest Fire Weather Index (FWI) are described. How to choose an adequate weather station site for fire danger rating observations, how to expose each weather instrument correctly, and the ronsequences of errors in weather data on the FWI are outlined. Weather instrument standards of accuracy and required precision in taking fire weather readings are described. Adjustment procedures are provided to users for such things as anemometers exposed in clearings too small to give representative wind speeds for danger rating calculations, and for making correc-tions to spring Fire Weather Indices when over-winter precipitation has been insufficient to replen-ish forest fuel mo isture. The Authors Jack Turner retired in 1974 from the Canadian Forestry Service, after 8 years as meteorologist with the fire research group of the Pacific Forest Research Centre in Victoria, B.C. During this time he was instrumental in developing, testing, calibrating and introducing the Fire Weather Index. Prior to 1967, he was fire weather specialist with the Protection Division of the B.C. Forest Service, having begun his career as a weather forecaster with the Atmospheric
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.717 | 0.628 |
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".