Distribution and Abundance of the Eastern Canada – West Greenland Bowhead Whale Population Based on the 2013 High Arctic Cetacean Survey
Bibliographic record
Abstract
The hunting of bowhead whales (Balaena mysticetus) is an integral part of Inuit culture. An up-to-date abundance estimate of the entire Eastern Canada – West Greenland (EC-WG) bowhead population is necessary to support sustainable management of this harvest. The High Arctic Cetacean Survey (HACS) was conducted in August 2013, primarily to update abundance estimates for known stocks of Baffin Bay narwhal (Monodon monoceros). As the ranges of narwhal and bowhead largely overlap, the survey area was expanded to cover the summer range of bowhead whales. Bowhead whale abundance was estimated using 3 aircraft to cover the large survey area within a short time frame. Distance sampling methods were used to estimate detection probability away from the track line. Double platform with mark-recapture methods were used to correct for the proportion of whales missed by visual observers on the track line (perception bias). Abundance in Isabella Bay, an area known for high bowhead density, was estimated using density surface modelling to account for its complex shape and uneven coverage. Estimates were corrected for availability bias (whales that were not available for detection because they were submerged when the aircraft passed overhead) using a recent analysis of satellite-linked time depth recorders transmitting information on the diving behaviour of bowhead whales in the study area in August of the same survey year. The fully corrected abundance estimate for the EC-WG bowhead whale population was 6,446 (95% CI: 3,838–10,827). Possible sources of uncertainty include incomplete coverage and the diving behaviour of bowhead whales. These results confirm earlier indications that the EC-WG stock is continuing to recover from past overexploitation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".