Estimating the mortality rate of humpback whale calves in the central North Pacific Ocean
Bibliographic record
Abstract
Sighting histories of individually identified female humpback whales (Megaptera novaeangliae) in their winter and summer ranges were used to investigate mortality of North Pacific humpback whale calves. We compiled records collected between 1979 and 1995 by eight independent research groups, which yielded 29 cases where 25 different mothers sighted in Hawai'i were identified later the same year in Alaska. In 7 of 29 cases, a calf sighted with its mother in Hawai'i was missing from its mother's Alaska sighting(s). After investigating many factors, we determined that the largest potential bias would occur in late-autumn observations, when calf absences might indicate weaning or temporary mothercalf separation rather than calf mortality. Our minimal and most robust estimate excluded all mortalities and survivals based on sightings of the mother after October 31; 3 of 20 cases or 0.150 (95% confidence intervals (CI) = 0.032, 0.378). The maximal calf mortality rate, derived from all the available data, was 7 of 29 cases or 0.241 (95% CI = 0.103, 0.434). An intermediate estimate that excluded all cases based on single Alaska sightings and omitted late-season sightings (2 of 11 cases or 0.182; 95% CI = 0.023, 0.518) is perhaps closest to the actual first-year mortality rate for humpback whale calves, although it is compromised by its small sample size. Our results demonstrate both the value and the limitations of using longitudinal data to determine the life-history parameters that are essential for documenting the recovery of endangered populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".