Spawning biomass reference points for exploited marine fishes, incorporating taxonomic and body size information
Bibliographic record
Abstract
Surplus production represents the processes that affect sustainable fishery harvest and is central to the ecology and management of marine fishes. Taxonomy and life history influence the ratio of spawning biomass at maximum sustainable yield to average unfished spawning biomass (SBMSY/SB0), and estimating this ratio for individual stocks is notoriously difficult. We use a database of published landings data and stock assessment biomass estimates and determine that process errors predominate in this data set by fitting a state–space model to data from each stock individually. We then fit multispecies process-error models while treating SBMSY/SB0as a random effect that varies by taxonomic order and maximum length. The estimated SBMSY/SB0 = 0.40 for all 147 stocks is intermediate between the values assumed by the Fox and the Schaefer models, although Clupeiformes and Perciformes have lower and Gadiformes and Scorpaeniformes have higher SBMSY/SB0values. Model selection supports the hypothesis that large-bodied fishes for a given taxonomic order have relatively higher SBMSY/SB0. Results can be used to define reference points for data-poor fisheries or as input in emerging assessment methods.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".