Defining Calving Ground Selection For The Bathurst Herd Of Barren-Ground Caribou
Bibliographic record
Abstract
Barren-ground caribou are a threatened sub-species of Arctic ungulate defined by their gregarious behaviour and expansive migrations across thousands of kilometres. The most critical part of these migrations are their calving grounds, the areas in which they birth and nurture their calves. As the Arctic continues to experience rapid changes, it is vital to understand and protect these critical areas. This research identifies what constitutes preferred calving habitat for barren-ground caribou cows, using the Bathurst herd of Nunavut and Northwest Territories as a case study. Using GPS collar data from 1996-2014, we developed a use-availability resource selection function (RSF) to compare Bathurst cows on their calving grounds to random points generated in a 100km buffer around the grounds. Specifically, we conducted Manly’s selectivity index and a multivariate binomial logistic regression model. We found that Bathurst cows select for herbaceous, sparsely vegetated, and low shrub land cover classes, higher elevation, steeper slopes, less terrain roughness, lower June temperatures, and further distances from water. We also found that cows avoid evergreen forest and water land cover classes. Together, these results suggest that food availability and predator and parasite avoidance are the key drivers of calving ground selection for barren-ground caribou. We also conducted a landcover pattern analysis on the currently used calving grounds (1990s-present) and on a previously used calving range (1960s-1980s). Of the 103 variables quantified, we found that 93 were significantly different between the two areas. This would suggest that landcover pattern, and especially configuration, is a less important factor influencing calving ground selection. However, definitive conclusions regarding pattern are challenging due to limitations associated with the time frame of data availability. The results of this research provide valuable insights on calving ground selection by barren-ground caribou and can be used to inform long-term conservation strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".