Demography and Viability of a Hunted Population of Polar Bears
Bibliographic record
Abstract
We estimated demographic parameters and harvest risks for a population of polar bears (Ursus maritimus) inhabiting Baffin Bay, Canada and Greenland, from 1974 to 1997. Our demographic analysis included a detailed assessment of age- and sex-specific survival and recruitment from 1221 marked polar bears, which used information contained within the standing age distribution of captures and mark-recapture analysis performed with Program MARK. Unharvested (natural) survival rates for females (± 1 SE) from mark-recapture analysis were 0.620 ± 0.095 (cubs), 0.938 ± 0.042 (ages 1–4), 0.953 ± 0.020 (ages 5–20), and 0.919 ± 0.046 (ages 21+). Total (harvested) survival rates for females were reduced to 0.600 ± 0.096 (cubs), 0.901 ± 0.045 (ages 1–4), 0.940 ± 0.021 (ages 5–20), and 0.913 ± 0.047 (ages 21+). Mean litter size was 1.59 ± 0.07 cubs, with a mean reproductive interval of 2.5 ± 0.01 years. By age 5, on average 0.88 ± 0.40 of females were producing litters. We estimated the geometric means (± bootstrapped SDs) for population growth rates at stable age distribution as 1.055 ± 0.011 (unharvested) and 1.019 ± 0.015 (harvested). The model-averaged, mark-recapture estimate of mean abundance (± 1 SE) for years 1994–97 was 2074 ± 266 bears, which included 1017 ± 192 females and 1057 ± 124 males. We incorporated demographic parameters and their error terms into a harvest risk analysis designed to consider demographic, process, and sampling uncertainty in generating likelihoods of persistence (i.e., a stochastic, harvest-explicit population viability analysis). Using our estimated harvest of polar bears in Baffin Bay (88 bears/yr), the probability that the population would decline no more than could be recovered in five years was 0.95, suggesting that the current hunt is sustainable.
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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 | 0.001 |
| 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) | 0.000 | 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".