Reviewing Ontario's moose management policy - 1980-2000 - targets achieved, lessons learned.
Bibliographic record
Abstract
We examine progress made in meeting the 1980, 20year Ontario Moose Management Policy (MMP) directive. Specific interim (1985, 1995) and final (year 2000) provincial program targets, including population, harvest, hunting, and viewing opportunities, particularly those in the NW Region, are reported. In addition to MMP guidelines, other management policy achievements and shortfalls pertaining to harvest control, population management, enforcement, habitat management, inventory and assessment, research, and hunter education are discussed. Provincially, moose numbers have increased only 7-20% throughout the 1990s plateauing at 100,000120,000 while the number of adult tags has almost been halved. Hunter numbers during this period have increased by about 4% and total harvest has remained fairly constant. Adult tag draw success has declined and success in filling a tag has increased while harvest remained similar in absolute numbers. This suggests that factors other than hunting pressure are limiting further population growth. Knowledge gained since 1980 suggests overall population and harvest targets are unattainable and should be revised using adaptive management principles, to more closely reflect land capability and societal demands. Reduced hunter reporting rates in recent years have jeopardized the quality of harvest estimates and diminished overall hunter confidence. Recommendations for policy changes, including revisions to program direction and targets, are made based on lessons learned. ALCES VOL. 38: 11-45 (2002)
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".