Replicating necropsy data without lethal collections: using ultrasonography to understand the decline in northern fur seals
Bibliographic record
Abstract
Summary 1. Many valuable contributions to the biology and conservation of harvested or previously harvested species have come from examination of specimens obtained by lethal collections. The northern fur seal Callorhinus ursinus on the Pribilof Islands, Alaska, has a long history of exploitation, including a large (>320 000) experimental harvest of females from 1955 to 1968 when the population was at a peak (∼2 million seals). The decline caused by this harvest was followed in 1977 by another major decline, apparently unrelated to harvest, that has recently accelerated. 2. To obtain current reproductive data that could be compared directly with historic estimates, we used imaging ultrasonography to estimate pregnancy rate in 171 adult fur seals captured on St. Paul Island, Alaska, in November, near the end of embryonic diapause. A modified logistic regression of pregnancy by date was used to estimate asymptotic pregnancy rate; a Bayesian hierarchical model based on date and size of embryonic vesicle was also used to account for pregnancies that were not detectable on the date of examination. 3. Pregnancy rate was high [0·85 (SE = 0·05), 0·88 (SE = 0·05) or 0·92 (SE = 0·04), depending on method] and there was little statistical support for the hypothesis that the current pregnancy rate is lower than the pre‐decline rate (0·84, SE = 0·012) or contributing significantly to the present decline. 4. Synthesis and applications. Further study on intrauterine losses and pupping rates is necessary and ongoing, but reproductive ultrasonography provided an early comparative assessment important for the conservation management of this fur seal stock. It narrows the search for demographic and ecological causes of the population decline and allows research priorities to evolve in response to the likelihood of those causes. The field and analytic methods described have application to population assessments of other mammalian species, including those considered threatened or serving as ecosystem indicators.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".