EVIDENCE OF CARRYING CAPACITY EFFECTS IN NEWFOUNDLAND MOOSE
Bibliographic record
Abstract
Newfoundland moose (Alces alces americana) increased following 1904, the year of successful introduction, to peak numbers in 1958. The population subsequently decreased to record low numbers by 1973, when an area-quota management system was instituted throughout the island (112,000 km 2 ) in 38 moose management areas, in part, to respond to issues related to habitat and accessibility for hunting. Subsequent quota-management manipulations permitted the island- wide population to increase in accessible areas to record high numbers by 1986, after which populations again decreased, to a 1999 estimate of 125,000 animals (post-hunt). We hypothesise that, unlike most studied irruptions of cervid populations, moose populations in Newfoundland, and subsequently habitat carrying capacity (K), decreased on inaccessible range following 1958 to very low density, from which both have never recovered. Decreases in relative numbers of young moose seen while hunting and during winter classifications are consistent with increases in the number of moose seen during increase phases during 1966-99. These observations are less obvious for less accessible management areas. We explore other recruitment and density relationships as they have been developed in association with our estimate of K in moose for Newfoundland. We illustrate that, although some decrease in moose numbers following 1958 and 1986 was the result of management, changes to population size and to K also resulted in reduction in productivity, such that density dependence explains > 10% and up to 76% of hunter-observed recruitment. ALCES VOL. 38 : 123-141 (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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".