Mapping Polar Bear Maternal Denning Habitat in the National Petroleum Reserve–Alaska with an IfSAR Digital Terrain Model
Bibliographic record
Abstract
The National Petroleum Reserve–Alaska (NPR-A) in northeastern Alaska provides winter maternal denning habitat for polar bears (Ursus maritimus) and also has high potential for recoverable hydrocarbons. Denning polar bears exposed to human activities may abandon their dens before their young are able to survive the severity of Arctic winter weather. To ensure that wintertime petroleum activities do not threaten polar bears, managers need to know the distribution of landscape features in which maternal dens are likely to occur. Here, we present a map of potential denning habitat within the NPR-A. We used a fine-grain digital elevation model derived from Interferometric Synthetic Aperture Radar (IfSAR) to generate a map of putative denning habitat. We then tested the map’s ability to identify polar bear denning habitat on the landscape. Our final map correctly identified 82% of denning habitat estimated to be within the NPR-A. Mapped denning habitat comprised 19.7 km2 (0.1% of the study area) and was widely dispersed. Though mapping denning habitat with IfSAR data was as effective as mapping with the photogrammetric methods used for other regions of the Alaskan Arctic coastal plain, the use of GIS to analyze IfSAR data allowed greater objectivity and flexibility with less manual labor. Analytical advantages and performance equivalent to that of manual cartographic methods suggest that the use of IfSAR data to identify polar bear maternal denning habitat is a better management tool in the NPR-A and wherever such data may be available.
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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 | 0.000 |
| 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".