A novel approach to compare pinniped populations across a broad geographic range
Bibliographic record
Abstract
We utilized aerial images and employed photogrammetric methodologies to collect standardized lengths of Steller sea lions (Eumetopias jubatus) terrestrially hauled out. We conducted comparisons among all site types and separately for rookery and haulout site-types between the two distinct population segments (DPSs; eastern and western) and two broad regions within the western DPS experiencing contrasting population abundance trends. An observed adult female index was created from measurements of reproductive females — in the presence of a pup or juvenile — and was applied as a model constraint for “adult females”. We fitted a finite mixture distribution model to the length-frequency data to estimate the proportion population for three delineated age–sex classes (juveniles, adult females, and adult males) and mean length for juveniles and adult males. Estimated proportions reflected what we expected; however, the broad region within the western DPS exhibiting substantial population declines had greater proportion of all age–sex classes on rookery sites than increasing broad region. Adult sea lions were significantly shorter in the eastern DPS than the western area, providing further evidence of morphological differences between the DPSs. We also introduce a less resource-demanding method for estimating population demographics, and potentially vital rates, for pinnipeds across a vast geographic range.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".