Integrated, age-structured, length-based stock assessment model with uncertain process variances, structural uncertainty, and environmental covariates: case of Central Baltic herring
Bibliographic record
Abstract
We developed a generic, age-structured, state-space stock assessment model that can be used as a platform for including information elicited from stakeholders. The model tracks the mean size-at-age and then uses it to explain rates of natural and fishing mortality. The fishery selectivity is divided to two components, which makes it possible to model the active seeking of the fleet for certain sizes of fish, as well as the selectivity of the gear itself. The model can account for uncertainties that are not currently accounted for in state-of-the-art models for integrated assessments: (i) The form of the stock–recruitment function is considered uncertain and is accounted for by using Bayesian model averaging. (ii) In addition to recruitment variation, process variation in natural mortality, growth parameters, and fishing mortality can also be treated as uncertain parameters. The use of the model is exemplified in the context of participatory modelling where stakeholders have specified how environmental variables affect the stock dynamics of Central Baltic herring (Clupea harengus membras).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".