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Bibliographic record
Abstract
In Canada, fire danger maps are generated daily by the Canadian Forest Fire Danger Rating System from weather station records. Such maps are limited spatially because they are produced from point-source weather measurements. Thus, remote sensing was investigated as an alternative. Thermal infrared NOAA-AVHRR images were used to describe pre-fire conditions of 24 large fires, occurring in 1994 in the Northwest Territories, Canada. Values of daily mean surface temperatures and fire weather index for burned areas were compared with those of surrounding unburned areas during an 11 day period prior to and on the day of fire ignition. It was hypothesized that: (i) mean surface temperature will increase as fire ignition dates approach; (ii) mean surface temperature within burned areas will be greater than within unburned areas; (iii) surface temperature will be positively related to the fire weather index. A positive trend in mean surface temperature was observed as ignition dates approached, but high percentages of cloud contamination made it difficult to follow each fire day to day. Similar trends were observed over unburned areas. A good relationship was found between surface temperatures and fire weather indices. Limitations and possible improvements of this study are also presented. Keywords: NOAA-AVHRR surface temperature, fire weather index, fire danger, Northwest Territories, northern boreal forests 1.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.639 | 0.547 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".