Toward a non‐invasive inuit polar bear survey: Genetic data from polar bear hair snags
Bibliographic record
Abstract
Abstract We explore the feasibility of collecting microsatellite genotypes of non‐invasively collected hair from free‐ranging polar bears ( Ursus maritimus ) in M'Clintock Channel, Nunavut, Canada. We estimate the minimum number of individuals in a region of the M'Clintock Channel population and evaluate potential sampling biases associated with corral sampling stations. We optimized 6 variable microsatellite loci for genotyping hair‐snag DNA with low genotyping error (mean allelic dropout and false allele error rates <5%). In May of 4 sequential years (2006–2009), we collected 595 hair‐snag samples from 145 baited corral sampling stations, from which 319 hair snags were used to detect 59–82 individuals using 4–6 microsatellite loci; we also genetically sexed these individuals. Although genetic sex estimates of matching genotypes are generally in agreement, the estimated sex ratio differs from that previously reported from aerial mark–recapture, which suggests a potential male bias in our sampling stations. These noninvasive methods of identifying individual and sex of bears hold promise for frequent and inexpensive estimates of polar bear population activity informed by Inuit hunters. © 2013 The Wildlife Society.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".