Estimating abundance from gillnet samples with application to red drum (<i>Sciaenops ocellatus</i>) in Texas bays
Bibliographic record
Abstract
A model of gillnet selection is developed to accommodate the possibility that some catch observations will be known more precisely than others and allow for nonlinear relationships between the selection parameters and mesh size. The model is used to show that gillnet selection for red drum (Sciaenops ocellatus) in Texas bays may be explained as a unimodal process approximating a skewed Laplace distribution, where the optimal length varies in proportion to mesh size and the variance in proportion to the optimal length. It is also suggested that the number of encounters with the net ought to depend on swimming speed of the quarry, which in turn varies predictably with length. This information, along with the estimates of selection, is used to develop indices of abundance for each length-class. The results indicate that the recruitment of year-old red drum to Texas bays has fluctuated markedly since 1975, but without any persistent trends. However, the survival of these and older fish has increased dramatically owing to various regulations promulgated since 1981.
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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 | 0.001 |
| 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) | 0.000 | 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".