RESPONSE OF A WINTERING MOOSE POPULATION TO ACCESS MANAGEMENT AND NO HUNTING - A MANITOBA EXPERIMENT
Bibliographic record
Abstract
We report on an experiment undertaken in eastern Manitoba beginning in 1996, in which a moose population wintering in 62 km 2 (24.2 mi 2 ) was protected from hunting until September 2003. At the time of closure, it is speculated that about 37 (0.6/km 2 (1.5/mi 2 )) moose wintered in the area based on aerial surveys and considering visibility bias. The closure was supported by the Eastern Region Committee for Moose Management, which is comprised of Manitoba Conservation staff, First Nation representatives from local communities, local hunting organizations, and other interest groups such as Tembec Manitoba Incorporated and the Manitoba Model Forest. Road access to the area was curtailed by using locked gates, millstones, and V-plowing a portion of the road in 2002. The area was surveyed from a helicopter on March 4, 2003, and 107 moose were counted in the closed area and again, based on visibility bias, it is speculated that about 142 moose (2.3/km 2 (5.8/mi 2 )) were present. This experiment clearly demonstrates that moose will respond positively to access management and no hunting, and that V-plowing roadbeds is a useful technique for controlling access. The cost associated with such plowing varies from about $500-$1,500/km depending on material contained in the roadbed. ALCES VOL. 40: 87-94 (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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".