INITIAL USE OF MOOSE CALF HUNTS TO INCREASE YIELD, ALASKA
Bibliographic record
Abstract
In 2002 the Board of Game authorized Alaska's first permit hunts specifically for calf moose (Alces alces). We promoted these calf hunts to help stabilize a high-density, food-stressed moose population and to compensate for declining harvests of bulls. Low harvest rates of cows (= 1% of the prehunt cow population, 1996-2001) were tightly controlled by the public. High harvest rates of bulls (21-26% of the prehunt bull population, 1995-1999) resulted in bull:cow ratios declining below the management objective of 30:100. To conserve bulls, the previous bag limit of any bull was changed to bulls with specific antler configurations. Simultaneously, 300 calf drawing permits were made available in 7 different hunt areas with the allocation of permits based on estimated moose densities within individual hunt areas. We issued 274 permits, but 61% of the permittees did not participate, in part to protest the hunt. Of 108 hunters, 33 reported taking a calf. The harvest accounted for about 1.3% (33/2,500) of the estimated prehunt calf population and 7% (33/471) of the total reported harvest. The calf harvest contributed only marginally to meeting the Game Management Unit 20A harvest mandate of 500-720 moose. We observed decreasing acceptance of calf hunts and increasing acceptance of cow hunts during 2002 and 2003. In 2004 we expect to substantially increase the harvest of cows and calves using registration and late season hunts and continuing education programs. We deem gaining public acceptance of cow and calf hunts in increasing, food-stressed Alaska moose populations to be a long-term, challenging, yet worthwhile endeavor. ALCES VOL. 40: 1-6 (2004)
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".