Using Traditional Ecological Knowledge to Facilitate Non-Invasive Polar Bear Monitoring
Bibliographic record
Abstract
This manuscript-based thesis explores the documentation and application of polar bear (Ursus maritimus) Traditional Ecological Knowledge (TEK) to population monitoring and management in the Canadian Arctic. The thesis is divided into four chapters; an introduction, two standalone research papers, and an overarching conclusion. Chapter II focuses on the collection of TEK through semi-directed interviews with knowledgeable polar bear hunters and Elders in Gjoa Haven, Nunavut. Extensive polar bear data is presented through GIS mapping and discussed. The application of these customizable maps is two-fold: i) They serve as a historical record of polar bear knowledge for the community of Gjoa Haven; and ii) The maps can act as a guide to areas of high polar bear activity for future targeted polar bear monitoring efforts. Chapter III is a pilot study which focuses on the field work associated with locating and collecting non-invasively sourced polar bear genetic samples using TEK from hunters in Coral Harbour, Nunavut. The effort to collect these samples is characterized using GPS data recorded during the thirteen sampling trips conducted by local team members in Coral Harbour. Over the two years of this study, hunters travelled 3247km, 40 polar bear fecal samples and snow samples from 99 footprints belonging to the tracks of 26 individual bears were collected, 8 polar bear dens were located, and 10 polar bears were observed. The results of these two chapters have the potential to inform the progression of polar bear monitoring towards less invasive approaches, more cost-effective field work and most importantly increased Inuit involvement in polar bear monitoring and management in Canada.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".