Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Although broadleaf tree species of the boreal biome have a lower flammability compared to conifers, there is a period following snow melt and prior to leaf flush (i.e., greenup), termed the "spring window" by fire managers, when these forests are relatively conducive to wildfire ignition and spread. The goal of this study was to characterize the duration, timing, and fire proneness of the spring window across boreal Canada and assess the link between these phenological variables and the incidence of springtime wildfires. We used remotely sensed snow cover and greenup data to identify the annual spring window for five boreal ecozones from 2001 to 2021 and then compared seasonality of wildfire starts (by cause) and fire-conducive weather in relation to this window, averaged over the 21-year period. We conducted a path analysis to concomitantly evaluate the influence of the spring window's duration, the timing of greenup, and fire-conducive weather on the annual number and the seasonality of spring wildfires. Results show that the characteristics of spring windows vary substantially from year to year and among geographic zones, with the interior west of Canada having the longest and most fire-conducive spread window and, accordingly, the greatest springtime wildfire activity. We also provide support for the belief that springtime weather generally promotes wind-driven, rather than drought-driven wildfires. The path analyses show idiosyncratic behavior among ecozones, but, in general, the seasonality of the wildfire season is mainly driven by the timing of the greenup, whereas the number of spring wildfires mostly responds to the duration of the spring window and the frequency of fire-conducive weather. The results of this study allows us to better understand and anticipate the biome-wide changes projected for the northern forests of North America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.047 | 0.021 |
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 it