Evaluating the performance of a multispecies statistical catch-at-age model
Bibliographic record
Abstract
Predation is a substantial source of mortality that is a function of the abundance of predator and prey species. This source of mortality creates a challenge of incorporating species interactions in statistical catch-at-age models in a way that accounts for the uncertainty in input data, parameters, and results. We developed a statistical, age-structured, multispecies model for three important species in the Georges Bank fish community: Atlantic cod (Gadus morhua), silver hake (Merluccius bilinearis), and Atlantic herring (Clupea harengus). The model was fit to commercial catch, survey, and diet data from 1978 to 2007. The estimated predation rates were high, compared with fishing mortality, and variable with time. The dynamics of the three species can be explained by the interplay between fishing and predation mortality. Monte Carlo simulations were used to evaluate the ability of the model to estimate parameters with known error introduced into each of the data types. The model parameters could be estimated with confidence from input data with error levels similar to those obtained from the model fit to the observed data. This evaluation of model performance should help to move multispecies statistical catch-at-age models from proof of concept to functional tools for ecosystem-based fisheries management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.020 |
| 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 | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".