Mast productivity, red-backed vole productivity and rate of increase, northern saw-whet owl rate of increase, and fisher harvest age ratios for central Ontario, 1994-2016
Bibliographic record
Abstract
Harvest management quotas for fishers (Pekania pennanti) in some jurisdictions are estimated from the previous year’s harvest, and stem from the hypothesis that age ratios in the harvest are largely influenced by ‘top-down’ trapping pressure. The influence of ‘bottom-up’ food supply on fisher harvest age ratios might be underappreciated, which could result in a misallocation of quotas in management planning. We assessed a variety of data sources to test the influence of bottom-up processes on fisher populations in Ontario, Canada. We found evidence that bottom-up trophic effects influence the harvested fisher age structure in some regions of Ontario. Evidence also suggests that harvest pressure had little top-down influence on age ratios over the course of our study, and that basing management strategies on this assumption may lead to unintentional overharvest in years of low productivity. We suggest several trophic linkages with potential to facilitate fisher management, including connections among berry and seed crops, small mammals, and Northern saw-whet owls (Aegolius acadicus).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".