Cloud-to-ground lightning in Yukon, Canada during a season of extreme wildfire activity
Bibliographic record
Abstract
Cloud-to-ground (CG) lightning from thunderstorms initiating forest fires is a typical summer hazard in the rugged terrain of the Yukon Territory of northern Canada. The lightning season usually starts in May, peaks during July and is usually over in September. This region typically experiences 141 wildfires annually which burn over 160,000 hectares between June and August. An annual average of over 20 thousand CG flashes account for about 54% of these ignitions. During the summer of 2004, a persistent upper level ridge dominated throughout the Yukon. Maximum temperatures were above normal for much of the season and precipitation amounts were about 60% of normal. The number of CG flashes, lightning-initiated forest fires and extent of the area burned surpassed historic records. Specifically, over 40 thousand CG flashes accounted for 88% of the 282 wildfires consuming over 1.7 million hectares of the territory. (Green, 2004; WFRP, 2005). The 2004 fire season in Alaska burned about 2.7 million hectares.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".