Polar bear (<i>Ursus maritimus</i>) maternity denning habitat in western Hudson Bay: a bottom-up approach to resource selection functions
Bibliographic record
Abstract
We examined habitat characteristics of 101 polar bear (Ursus maritimus Phipps, 1774) den sites and 83 adjacent unoccupied sites in western Hudson Bay, Canada, between mid-August and early October 2001 and 2002. Bears denned almost exclusively in peat banks (n = 100) along the edges of creeks, rivers, and lakes adjacent to open lichen tundra sites. Den sites differed from unoccupied sites by having greater tree cover (P = 0.002), less moss cover (P < 0.001), and less herbaceous cover (P = 0.005). The presence of tree roots improved substrate stability, providing support to den structures. Den entrance azimuths were weighted toward a southeasterly aspect (P < 0.005), away from the prevailing northwest winds. To identify habitats with the greatest relative probability of having a den, a resource selection function (RSF) model was developed using remote sensing imagery and 1245 known den locations. High normalized difference vegetation index and brightness values derived from Landsat imagery, which were in close proximity to water, corresponded well with polar bear den sites. Identification of critical denning areas through the use of RSF will provide resource managers with a valuable tool for ensuring the protection of denning habitat, and consequently female bears and their young.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".