ADAPTIVE MANAGEMENT OF MOOSE IN ONTARIO
Bibliographic record
Abstract
Early policy decisions affecting moose (Alces alces) management in Ontario were based on data that were not reliable, but were the only basis available for policy development. As data collection increased in accuracy and reliability, policy decisions have also improved. In the last decade of the 20th century, adaptive management has been discussed and advocated as the best approach to managing natural resources since it was first developed in the early 1970s. The Ontario Ministry of Natural Resources has instituted at least some of the characteristics of adaptive management in managing moose. The 1960s and 1970s were periods of extensive learning and maturation for biologists and wildlife managers with respect to Ontario's moose herd. The experience and knowledge gained from these periods were used to develop goals and objectives which would eventually become Ontario's 1980 moose policy and the first steps of adaptive management. The later phases of the adaptive approach, to evaluate the earlier objectives and learn from them, are reviewed and discussed. The goals established in 1980, probably cannot be achieved, however, the learning associated with the process is important in order to manage adaptively. ALCES VOL. 38: 1-10 (2002)
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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 | 0.000 | 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) | 0.005 | 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".