Validation of a hidden Markov model for the geolocation of Atlantic cod
Bibliographic record
Abstract
Models developed to geolocate individual fish from data recorded by electronic tags often require major modification to be applied to new regions, species, or tag types due to variability in oceanographic conditions, fish behavior, and data resolution. We developed a model for geolocating Atlantic cod (Gadus morhua) off New England that builds upon an existing hidden Markov model (HMM) framework and addresses region- and species-specific challenges. The HMM framework contains a likelihood model that compares tag-recorded environmental data (depth, temperature, tidal characteristics) with those derived from an oceanographic model and a behavior model that constrains the horizontal movement of the fish. Validation experiments were performed on stationary tags, double-electronic-tagged fish (archival and acoustic tags), and simulated tracks. Known data, including fish locations and activity metrics, showed good agreement with those estimated by the modified approach and improvements in performance of the modified method over the original. The modified geolocation approach will be applicable to additional species and regions to obtain valuable movement information that is not typically available for demersal fishes.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".