Bibliographic record
Abstract
Archer L. C., B. Kirschhoffer, J. Aars, D. K. James, K. M. Miller, N. W. Pilfold, J. Sulich, and M. A. Owen. 2025. Monitoring phenology and behavior of polar bears at den emergence using cameras and satellite telemetry. Journal of Wildlife Management 89:e22725. https://doi.org/10.1002/jwmg.22725. Funding support from the Svalbard Environmental Protection Fund was accidentally omitted from the Acknowledgments. This funding support is now recognized in the updated Acknowledgments section below. Magnus Andersen was involved in all capture work in Svalbard where polar bears were collared in spring. Rolf Arne Ølberg also participated in this field work. Rupert Krap participated in several years of the deployment of camera systems in Svalbard, and several people from the Norwegian Polar Institute further supported that work in different ways. Polar Bears International and the Svalbard Environmental Protection Fund contributed funding to the study, and both PBI and WWF UK contributed funding for fieldwork associated with capture of bears and deployment of collars that made it possible to locate the maternity dens. LCA was funded by a Mitacs Elevate Fellowship and Polar Bears International.
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 | 0.003 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.174 | 0.124 |
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".