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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 it