Numbers or mass? Comparison of two theoretically different stage-based stock assessment models and their ability to model simulated and real-life stocks
Bibliographic record
Abstract
Stage-based assessment models are a type of fisheries stock assessment model that offer an alternative middle ground between aggregate and compositional models. We compare the capabilities of two theoretically different stage-based assessment approaches: an implementation of a biomass-based delay-difference model first described in a theoretical paper by Schnute in 1987, and an implementation of the well-known numbers-based two-stage model Catch-Survey Analysis (CSA). Models were tested within a simulation framework as well as on the real stock of white-bellied anglerfish ( Lophius piscatorius) in the Celtic Seas and Northern Bay of Biscay. For the simulated stocks, estimates from the biomass-based two-stage models were close to the true values in certain scenarios, but were sensitive to selectivity assumptions and configuration of growth within the model. CSA was more robust to selectivity assumptions, performing well in all simulated stock scenarios. Overall, results indicated that CSA was a robust stock assessment model but with relatively low precision, whereas the Schnute model was precise but required growth and mean fish weight data unaffected by selectivity.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".