The Utility of Harvest Recoveries of Marked Individuals to Assess Polar Bear (<i>Ursus maritimus</i>) Survival
Bibliographic record
Abstract
Management of polar bear (Ursus maritimus) populations requires the periodic assessment of life history metrics such as survival rate. This information is frequently obtained during short-term capture and marking efforts (e.g., over the course of three years) that result in hundreds of marked bears remaining in the population after active marking is finished. Using 10 additional years of harvest recovery subsequent to a period of active marking, we provide updated estimates of annual survival for polar bears in the Baffin Bay population of Greenland and Canada. Our analysis suggests a decline in survival of polar bears since the period of active marking that ended in 1997; some of the decline in survival can likely be attributed to a decline in springtime ice concentration over the continental shelf of Baffin Island. The variance around the survival estimates is comparatively high because of the declining number of marks available; therefore, results must be interpreted with caution. The variance of the estimates of survival increased most substantially in the sixth year post-marking. When survival estimates calculated with recovery-only and recapture-recovery data sets from the period of active marking were compared, survival rates were indistinguishable. However, for the period when fewer marks were available, survival estimates were lower using the recovery-only data set, which indicates that part of the decline we detected for 2003 – 09 may be due to using only harvest recovery data. Nevertheless, the decline in the estimates of survival is consistent with population projections derived from harvest numbers and earlier vital rates, as well as with an observed decline in the extent of sea ice habitat.
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 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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".