AN EXAMINATION OF THE ABSENCE OF ESTABLISHED MOOSE (ALCES ALCES) POPULATIONS IN SOUTHEASTERN CAPE BRETON ISLAND, NOVA SCOTIA, CANADA
Bibliographic record
Abstract
An analysis was performed on habitat-related factors for the southeastern side of Cape Breton Island, Nova Scotia to investigate the continued absence of moose (Alces alces) from the region. Temperature and snow depth, at times, reach levels that could cause thermal stress or impede movement of moose; however, it is unlikely that these factors dictate the absence of moose. No clear relationships were established between environmental concentration levels of the heavy metals molybdenum, cadmium, copper, and lead and moose distribution; however, high concentration levels of molybdenum in the Cape Breton study area warrant further investigation. Road density assessments showed that the study area has a higher level of road density compared to 2 mainland control sites; however, higher road density occurs in other areas in which moose persist. Anthropogenic factors such as poaching were not considered influential enough to exclude moose. A forest habitat comparison analysis was performed to identify habitat features that were statistically correlated with moose presence, and then were applied in a probability model to predict moose presence in the study area. The logistic regression model used to predict the probability of moose presence was composed of positively associated forest inventory variables (softwood average maturity, hardwood average maturity, % mixed hardwood, % non-forested area, total wetland area) that best fit the data. The model identified 43% of the Cape Breton study area as having a high-probability weighting for moose presence. Overall, this study did not reveal a clearly identifiable cause for the continued absence of moose in southeastern Cape Breton Island.
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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) | 0.000 | 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".