Estimating relative abundance of whales from historical Antarctic whaling records
Bibliographic record
Abstract
Catch per unit effort (CPUE) is often the only data available from historical fisheries for inferring distribution and abundance of exploited populations. CPUE underestimates variations in relative abundance when gross effort data are only measured in total operating days. Gross effort includes both searching time and handling time, but only searching time is useful for an index of abundance. A method is developed for estimating searching time by subtracting a maximum likelihood estimate of handling time from the gross effort. An expectation maximization (E-M) algorithm is used to combine maximum likelihood estimates of the handling time with the expected additional operating time due to handling the last catch of each day. Simulation tests show that the estimates of catch per unit of searching time (C/CSW) are much closer to proportionally related to local density than gross CPUE. Estimates of handling time are not unbiased, and some nonlinearity between local density and C/CSW may persist. The methods may be useful for other fisheries where historic gross catch and effort data involve both searching and handling.
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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 | 0.003 | 0.002 |
| 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) | 0.001 | 0.001 |
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".