Effects of weather and snow on habitat selection by American martens (<i>Martes americana</i>)
Bibliographic record
Abstract
To better understand how a species known to thermoregulate behaviorally switches among microsites and habitats in response to weather and snow, we studied influences of weather and snow conditions on resting by the American marten, Martes americana. Vertical location of resting sites varied with air temperature and snowfall during the previous 24 h. Subnivean resting was most likely when air temperature was low and when recent snowfall had been heavy, and tended to be in stands dominated by spruce-fir. Supranivean resting tended to occur when weather was warmer and when recent snowfall had been light, and tended to occur in stands dominated by lodgepole pine (Pinus contorta), the predominant conifer in the study area. Fidelity of martens to resting sites varied with season; martens reused sites more in winter than in spring, hypothetically a result of trading-off increased energy savings accrued from using a few especially efficient sites against longer distances traveled to reach them. Such travel may not be rewarded in warm weather. Stand characteristics associated with resting in cold snowy winter periods are typical of low disturbance frequencies, including old-growth conditions in the Rocky Mountains.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".