Abundance and mtDNA differentiation of humpback whales (<i>Megaptera novaeangliae</i>) in the Shumagin Islands, Alaska
Bibliographic record
Abstract
Despite extensive research on humpback whales (Megaptera novaeangliae (Borowski, 1781)) in parts of the North Pacific, little research has focused on the whales feeding in coastal waters west of Kodiak Island in the Gulf of Alaska. To extend research westward in the North Pacific, small-boat surveys were conducted near the Shumagin Islands during the summers of 1999–2002. Photographs of the natural markings of humpback whales were collected, representing 413 sightings of 171 individual whales. Small samples of skin tissue were collected from 20 individuals, including two mother–calf pairs, for sex identification and comparison of mtDNA haplogroups with previously published results from surveys in other regions of the North Pacific. Individual identification photographs were used in mark–recapture analysis to estimate abundance for the Shumagin Island region. The best estimate was given by a modified Jolly–Seber method: N = 410 (95% CI: 241–683) for 2002. Comparison of photographs with archived photographs from throughout the North Pacific revealed four migratory destinations for 13 of the Shumagin Islands whales: Hawai'i, Japan, offshore Mexico, and coastal Mexico. The frequencies of mtDNA haplogroups differed significantly from those in three other sampled feeding grounds: California, southeastern Alaska, and Prince William Sound. The haplogroup frequencies and migratory destinations of individuals suggested an affinity with the Hawaiian wintering ground but data are insufficient to associate whales off the Shumagin Islands with any surveyed breeding ground.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".