Harvest dynamics of mustelid carnivores in Ontario, Canada
Bibliographic record
Abstract
Virtual population analysis (cohort analysis) was used to reconstruct past dynamics of a harvested population of martens Martes americana in the Bracebridge District of southern Ontario. Harvests in the Bracebridge District were managed using a quota system set by regional authorities. Quotas changed from year to year, apparently on the basis of past trapping success and variation in the proportion of young‐of‐the‐year among harvested animals. The proportion of young in the harvest was a sensitive indicator of the annual rate of increase, whereas trapping success tended to be linked most strongly, in inverse fashion, with marten harvesting quotas. The proportion of martens harvested each year was constant, averaging 34%, despite 3‐fold variation in marten abundance. This proportion was very close to the maximum sustainable yield (36%) for the population, suggesting that the management policy in the administrative unit was effective in the past in sustaining the source population as well as yielding high trapping returns. Monte Carlo simulation showed that proportionate harvesting, such as the policy in the Bracebridge District during 1972–1991, should be considerably less likely to lead to overharvesting than a constant quota policy, particularly at high average yields.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".