Heterogeneity and the markrecapture assessment of the Scotian Shelf population of northern bottlenose whales (<i>Hyperoodon ampullatus</i>)
Bibliographic record
Abstract
A population of northern bottlenose whales (Hyperoodon ampullatus) inhabits the waters along the edge of the Scotian Shelf. The most important habitat of this population is the Gully, a large submarine canyon, where animals were photographically identified between 1988 and 2003. Open markrecapture models, including mixture models that allow for heterogeneity in identifiability and (or) mortality among individuals, were fitted to identification-history data. Models without heterogeneity in identifiability had poor fit to the data and underestimated population size. The population is estimated to contain about 163 animals (95% confidence interval 119214), with no statistically significant temporal trend. About 12% of the population has a high probability of being identified within the Gully in any year. Many of them are mature males. The remainder is less likely to be identified in the Gully during any year, spend generally shorter periods in the Gully even in years when they are found, and are more likely to be female. This and other work indicate a poorly mixed population inhabiting the canyons and other deeper waters off the Scotian Shelf. Non parametric bootstrap methods were used to validate the estimation procedure and to estimate the efficiency of future fieldwork scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".