Bibliographic record
Abstract
Length-based selection curves define the relative catchability of fish to specific types of fishing gear, with catchability often highest at intermediate fish lengths. Distributions such as the normal, lognormal, or gamma are often used to define “peaked” selection curves, but these have limited capabilities to describe strongly asymmetric selection relationships, such as those sometimes observed for hooks or gillnets. Another, more flexible, peaked selection curve is proposed, which is derived by combining multiple logistic distributions. While the logistic distribution is frequently used to describe monotonic selection curves, incorporating multiple logistic equations (that describe either the increasing or decreasing catchability) can define a large range of asymmetric peaked selection curves. This “peak-logistic” curve also allows nonzero asymptotic selection for the largest size classes, which may be the selection occurring in some hook-and-line fisheries. We demonstrate examples of selection in hook, haul net, and mixed hook fisheries, for which the peak-logistic curve is more appropriate than comparative lognormal and binormal selection curves. We also promote an alternative to the peak-logistic: the two-sided normal curve.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".