Spatial and Temporal Patterns of Wolf [ <i>Mahihkan</i> (Cree), <i>Tha</i> (Denesuline), <i>Amaruk</i> (Inuktitut), <i>Canis lupus</i> ] Occurrences on the Summer Range of the Eastern Migratory Cape Churchill Caribou Population in the Hudson Bay Lowlands of Manitoba
Bibliographic record
Abstract
Abstract Wolves ( Canis lupus ) function as a top predator across diverse ecosystems including the sub-arctic, and they have been managed in often controversial ways. Communities and scientists are increasingly supporting minimally invasive research and monitoring, including using trail cameras. We employed a network of 15 Reconyx trail cameras at three monitoring areas aimed at detecting the spatial and temporal aspects of wolf occurrences within the summer range of Eastern Migratory Cape Churchill caribou in Wapusk National Park in the Hudson Bay Lowlands of Manitoba, Canada from 2013-2021. In this first peer-reviewed quantitative study of wolves in the region, we found that wolves detection events were generally consistent across years. Wolf distribution was consistently positively skewed toward the southern part of the caribou summer range in all years. Wolves experienced extreme environmental conditions, with a 60°C range in temperature, from a low of −32°C in winter to a high of +28°C in summer and an annual change in day length of >11 hours between summer and winter. Wolves occurred most commonly in spring and summer and occurred at equal frequency during night and day overall but selected for nighttime in September, October, and November as day length shortened dramatically.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".