Using electronic tag data to improve mortality and movement estimates in a tag-based spatial fisheries assessment model
Bibliographic record
Abstract
Despite increased deployment of archival tags on exploited fish species, analytical methods for including archival tag data in fishery assessment models are lacking. We present a method for integrating archival tag data into a spatial tag–recapture model for estimating natural mortality, fishing mortality, abundance, and movement. Archival tags provide important information on fish movement not available from conventional tags that facilitates separation of movement from mortality estimates. Using simulations, we evaluate the benefit of including archival tag data in the model using two model formulations: one with a general spatial structure and one with movement and fishery dynamics based on juvenile southern bluefin tuna (SBT; Thunnus maccoyii ). If fish are not tagged in all regions and time periods, then including archival tag data can substantially improve the precision of the fishing mortality and movement estimates. For example, with the general spatial structure and specific scenario presented, standard errors of the fishing mortality estimates decreased by an average of 34% (21%) when 25 archival tags were released in addition to 500 (2500) conventional tags in each region and period of tagging, respectively. Furthermore, with the SBT spatial structure, archival tag data were necessary for all parameters to be estimable.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".