Improving assessment of <i>Pandalus</i> stocks using a seasonal, size-structured assessment model with environmental variables. Part II: Model evaluation and simulation
Bibliographic record
Abstract
Integrated, size-structured stock assessment models are now being used widely for assessment and management of hard-to-age species. However, few studies have attempted to evaluate their performance. A seasonal, size-structured assessment model with environmental covariates has been developed for hermaphroditic Pandalidae. We conducted simulations to evaluate its sensitivity to model configuration and performance with various misspecifications. Ignoring the seasonal fishing pattern (half-year closure) led to risk-prone assessment results of overestimating spawning stock biomass (SSB) and recruitment (R) and underestimating fishing mortality (F). Failure to incorporate environmental signals when the recruitment dynamics was environmentally driven led to bias in recent estimates of SSB, R, and F in the simulation. Ignoring annual variability in growth resulted in large estimation bias. Failing to account for time-varying natural mortality (M) led to strong biases; however, misspecifying size-specific M produced even stronger estimation bias. This result may depend on the variation of M among size classes. Under no model misspecifications, an unbiased estimate of M could be obtained by taking advantage of the seasonal fishery closure. Annual growth parameters were also estimable, but the large number of parameters with annual growth made it difficult for the model to converge.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".