Using Minimally Invasive Sampling Methods to Evaluate Polar Bear (Ursus maritimus) Visitation in Response to Environmental and Anthropogenic Stimuli
Bibliographic record
Abstract
Polar bears (Ursus maritimus) are spending more and more time on land in an increasingly warming Arctic, raising concerns for both human safety and polar bear conservation. This research aimed to investigate the ecological and anthropogenic influences on polar bear visitation to infrastructure in western Hudson Bay, and how these factors drive the growing risk for human-polar bear conflict. More specifically, examining the links between sea ice breakup and freeze-up dates, the length of the onshore season, polar bear body condition, human presence, and polar bear visitation to infrastructure shed light on valuable insights into the complex interplay between environmental conditions, human activities, and the potential for conflict between humans and polar bears, ultimately informing management efforts. The data for this project were collected via a combination of camera trap surveys, evaluation of sea ice charts, and human use observations in the western Hudson Bay region. \nUsing mixed effects logistic regressions, generalized linear models, and negative binomial regressions, I validated camera trap efficacy for monitoring polar bear populations, evaluated the relationship between changing sea ice, polar bear body condition, and patterns in polar bear visitation to infrastructure, and determined the effects of body condition and levels of human presence on polar bear visitation frequency. In western Hudson Bay, lone female bears were in the best body condition, while females with young, especially cubs of the year, tended to be in the worst body condition, suggesting a potential paradigm shift away from subadults being the bears most involved in human-polar bear interactions and conflict. Moreover, polar bears in this region were observed more frequently at infrastructure during years where the ice-free season is the longest, increasing chances of interactions with people. However, neither the Churchill Northern Studies Centre nor the remote camps in Wapusk National Park were serving as attractants to polar bears, and the group size or duration of human presence did not play an important role in this. Lastly, I determined camera trapping surveys are a valid method of monitoring polar bear populations, but multi-camera setups are encouraged for highest efficacy. \nFurther monitoring is warranted. While camera traps are a reliable method for monitoring polar bear populations, especially for Arctic communities, an expansion of the camera array on the landscape and collaboration with Indigenous communities will help to create a more robust data set with higher statistical power and from which to make better inferences in the future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".