A metapopulation model for<scp>C</scp>anadian and<scp>W</scp>est<scp>G</scp>reenland narwhals
Bibliographic record
Abstract
Abstract A model of the metapopulation structure of narwhalsMonodon monocerosinBaffinBay,HudsonBay and adjacent waters is proposed based on satellite telemetry data collected over two decades from six coastal aggregations of narwhals in the easternCanadian highArctic,HudsonBay andWestGreenland. In addition, data on seasonal catches of narwhals in 11Inuit communities are used to provide information on the occurrence of narwhals. The tracking data suggest that disjunct summer aggregations of narwhals are, to some extent, demographically independent subpopulations, with minimal or no exchange with other summering aggregations. We propose that these should be considered separate stocks for management purposes. Year‐round satellite tracking of individuals demonstrates that whales return to the same summering areas the following year, suggesting inter‐annual site fidelity. We propose that the narwhals inCanada constitute five separate stocks, with limited exchange between three of the stocks.Coastal summer aggregations inGreenland constitute two stocks in addition to two fall and winter aggregations supplied by narwhals from several summering areas. Several narwhal stocks mix on the wintering areas inBaffinBay, but the metapopulation structure is likely maintained through a combination of life‐history traits and migratory routes, as mating most likely occurs after the initiation of the return migration toward summering areas. The metapopulation structure inBaffinBay narwhals will be impacted differentially byInuit subsistence hunting, depending on the migratory schedule of narwhals and dates at which whales occur in different seasonal hunting areas. It is therefore important to identify which narwhal stocks contribute to which subsistence hunts in order to assess the sustainability of those hunts. This paper proposes a preliminary stock model for this purpose.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".