Deer management in Ontario (1980-1997): Implications for future management.
Bibliographic record
Abstract
The Ontario of Ministry Natural Resources spends considerable effort each year managing white-tailed deer (Odocoileus virginianus ). A selective harvest system, which regulates kill by issuing a restricted number of either-sex harvest permits (called tags), has been the main tool employed to manage populations. Using harvest and biological data from 1980 to 1997, we reviewed the historical ability to regulate both harvest and population size in Ontario, and assessed our ability to predict future population size. Under the selective harvest system, when more than 40% of hunters hold tags, kill can only be significantly increased by increasing hunter numbers and even below 40% tags, the most effective regulation of kill involves regulating both the number of tags and the number of hunters. The ability to regulate harvest may also be limited by hunter behaviour. Deer population size, as indicated by deer seen controlling for effort, was mainly regulated by density-dependence, with limited effects from summer and winter weather conditions. Harvest exhibited no effect on population size, except in a few, select areas where relatively large kills produced limited down-regulation. The best model created to predict changes in population, for management purposes, had poor predictive ability and barely outperformed randomly choosing a number. These results indicate an inability to regulate deer populations through a sport harvest. Kill, at historical levels, does not appear to regulate population density. Further, based on currently collected data, we are unable to make accurate predictions of future population size on which to base management decisions. We suggest a new management system, in recognition of the limited predictive and management ability, in which management actions are untaken only when estimated populations exceed broad limits.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".