Modeling fish numbers dynamically by age and length: partitioning cohorts into "slices"
Bibliographic record
Abstract
Fishery processes of selectivity and recruitment to legal size vary with fish length and are mediated by fish growth. Yet most fishery models are age-based. To model length-dependent change within each cohort, fish numbers must vary dynamically with length as well as with age in the model population array. The fishery model formalism described here achieves this by a partition of the continuous length-at-age distribution. This method is computationally efficient and cleanly differentiates legal from sublegal fish. Fish numbers within each cohort are partitioned into length bins, called slices. A slice is defined and calculated as the fish in each cohort length-at-age distribution that have grown into legal size since the start of the previous time step. When growth is estimated from catch length and age samples separately from the stock assessment, biases result from the implicit assumption that catch samples are representative of the population and from ignoring length-dependent change within cohorts. These biases are avoided by integrating recruitment, growth, and selectivity estimation into a stock assessment likelihood that represents changing population numbers by both age and length. Size dependence also permits a natural extension of fishery models to trophic interactions with the surrounding ecosystem.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".