Hierarchical Classification of Narwhal Subpopulations Using Social Distance
Bibliographic record
Abstract
ABSTRACT Effective wildlife management and conservation require knowledge of distribution, sex composition, and age structure of a population. We explored the distribution of the Baffin Bay narwhal (Monodon monoceros) population in August 2013 by documenting sex and age distribution across the Canadian Arctic Archipelago covering 2,317,152 km2. For 6,314 narwhals identified in 3,393 aerial images taken across the Eastern Canadian Arctic, we calculated a matrix of swimming distances between all individuals. We then used a quantitative clustering approach to partition our dataset (partitioning around the medoids). The clusters obtained from the analysis supported the delimitation of the 5 narwhal management stocks currently used by the Department of Fisheries and Oceans but did not support the hypothesized division of Jones Sound and Smith Sound stocks. Across the 5 clusters, male:female ratios varied between 0.72 and 1.44 and the proportion of newborns relative to the number of females varied between 0.07 and 0.18. As a highly detailed snapshot of narwhal distribution across a very large region, our study is a step toward better documentation of the basic population information required for stock assessment, sustainable harvest, and habitat protection of narwhals in an era of rapid Arctic change. © 2019 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".