Bibliographic record
Abstract
We developed a stage/sex matrix model for quota-harvest management of moose ( Alces alces ) populations in Alberta, and believe that the model structure has general applicability for harvesting of large mammal populations. The model includes density dependence in stage/sex-based vital rates and allows for estimation of carrying capacity and herd composition at carrying capacity from limited population survey data and harvest data. The model allows a biologist to evaluate optimal harvest strategies with the aim to optimize either the yield of the number of bulls harvested (goal B) or the yield of the total number of moose harvested (goal TY). The model predicted that to optimize yield of bulls, hunting of calves should be avoided because male calves recruit into the bull population the following year. If optimizing total yield, calves should be subject to intense harvest; harvesting for calves was predicted to be more intense than for bulls if female harvesting was not allowed, otherwise less intense. Bull harvest was less intense when trying to optimize yield of bulls than optimizing total yield. Small quotas of females could increase optimal yield substantially. The model also predicted that predation on calves and females reduced long-term optimal harvest intensity and calf predation reduced optimal total yield more than it influenced the optimal harvest of bulls. Reductions in moose abundance caused by predation and stochastic weather events can potentially cause severe consequences to harvest policy, challenging wildlife managers who must balance moose conservation, predator control, and hunter harvests. We believe that our model can facilitate harvest management, but vigilant monitoring of herd population size and composition will be necessary to ensure balance between predation and hunter harvests.
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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 | 0.001 | 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 | 0.000 | 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 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".