Integrating tagging and fisheries data into a spatial population dynamics model to improve its predictive skills
Bibliographic record
Abstract
SEAPODYM is a model developed for investigating spatiotemporal dynamics of fish populations under the influence of both fishing and the environment. The model simulates age-structured population dynamics using advection–diffusion–reaction equations describing movement, recruitment, and natural and fishing mortality. The dynamic processes are constrained by environmental data and distributions of prey species. Model parameter estimation using fishing data was implemented earlier based on a maximum likelihood estimation (MLE) approach and adjoint technique. Here, we describe the integration of tagging data into the existing MLE approach with application to skipjack tuna (Katsuwonus pelamis) in the Pacific Ocean. We find that tagging data improve estimates of species habitat parameters and movement rates and hence allow better representation of spatial dynamics of fish population. Due to estimated lower diffusion and higher advection rates, the model predicts less non-observed “cryptic” biomass, which leads to the stock sizes being closer to those estimated by stock assessment models commonly used by tuna commissions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".