Using hidden Markov models to infer vessel activities in the snow crab (<i>Chionoecetes opilio</i>) fixed gear fishery and their application to catch standardization
Bibliographic record
Abstract
Tracking vessel movements has become increasingly important in fisheries research to identify fishing grounds, monitor responses to area closures, and other actions for fishery managers. Vessel monitoring systems (VMS) have given fishery managers and researchers the ability to study vessel interactions by automated tracking of vessels throughout fishing seasons. The high spatial and temporal resolution obtained from VMS records in the Gulf of St. Lawrence snow crab (Chionoecetes opilio) fishery provides information on movement patterns and fishing locations. With the use of hidden Markov models (HMM), we inferred behaviours exhibited by the fishermen during the course of fishing trips and related these behaviours to catch rates across years with varying abundance estimates. The HMM classified three behavioural states in the VMS data that were identified with travelling, setting traps in novel locations (new sets), and retrieving previously set traps (resets). Catches within a trip were modeled by combining VMS-based estimates of these behaviours with logbook information in a generalized linear model. Our model demonstrates that behavioural variables can contribute to the standardization of catch similar to classical trip and vessel variables used in constructing abundance indices.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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".