Megafires and thick smoke portend big problems for migratory birds
Bibliographic record
Abstract
In 2020, the fire season affecting the western United States reached unprecedented levels. The 116 fires active in September consumed nearly 20,822 km2 (https://inciweb.nwcg.gov/accessible-view/ Accessed 2020-09-29) with 80% of this footprint (16,567 km2) from 68 fires occurring within California, Oregon, and Washington. Although the 2020 fire season was the most extreme on record, it exemplified patterns of increased wildfire size, number, timing, return frequency, and extent, which are linked to climate-driven changes in precipitation and temperature affecting fire ignition and severity (Westerling 2016, Goss et al. 2020, Weber and Yadav 2020). In addition, wildfire smoke and particulate pollution have expanded greatly in recent decades throughout western North America, posing a threat to both human and ecological health (Burke et al. 2021). Wildfires have increasingly coincided with the start of fall migration (Westerling 2016, Goss et al. 2020) and may present a growing risk to migrating birds in the Pacific Flyway. Migrating birds across several western states were observed dead and dying in 2020. Within the Central Flyway, starvation of insectivorous birds that were recovered in Arizona, Colorado, and New Mexico was linked to a record cold-weather storm in the Rocky Mountains (Fox 2020). But causes of the nearly simultaneous bird mortalities of larger granivorous species (Fig. 1) further west in the Pacific Flyway, where fires were occurring, remain unclear.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".