Bibliographic record
Abstract
Most marine fish are migratory, and some species exhibit complex movement patterns. Knowledge of fish movement can improve the understanding of interactions between populations and stock identification, assessment, and management. Geolocation is increasingly employed to reconstruct the movements of fishes using data retrieved from electronic archival tags, but such methods often require substantial modification to be applied to new regions, species, or tag types due to variability in oceanographic conditions, fish behavior, and data resolution. Existing geolocation methods also commonly suffer from limitations such as low horizontal resolution of locations, flawed land boundary treatment, and extensive computation time. To address these issues, a geolocation method that builds upon an existing hidden Markov model (HMM) framework, and a state-space geolocation approach based on the particle filter (PF) were developed. Both frameworks contain a likelihood model which compares tag-recorded environmental data (depth, temperature, tidal characteristics) with quantities derived from an oceanographic model and a behavior model which constrains the horizontal movement of the fish. Validation exercises using stationary mooring tags and double-electronic-tagged Atlantic cod resulted in <10 km median errors of the estimated tracks. Acceleration of the PF method using graphics processing units (GPUs) resulted in significantly decreased wallclock time compared with the single threaded central processing unit (CPU) implementation, enabling rapid geolocation using consumer grade computer hardware. The HMM method was applied to a geolocation study of Atlantic halibut, a "Species of Concern" in U.S. waters. Halibut were tagged off Massachusetts and Maine using both pop-up satellite and fixed data storage tags. A preprocessing routine was implemented to address the data limitations of the satellite transmitted data. Based on a limited number of individuals, several with short deployments, the geolocation results indicate that most tagged halibut stayed in the vicinity of the tagging locations (<60 km), while some underwent long horizontal displacements up to 440 km. Estimated movement tracks of two individuals may suggest that they reached likely spawning habitat. The results suggest finer-scale spatial population patterns of Atlantic halibut, and provide information on vertical and horizontal movement behavior that can inform stock assessment and management decisions. The developed geolocation tools are generalizable and can be applied to different groundfish species with minimal modifications, and can be further adapted for other species, regions, and tag types.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.005 | 0.004 |
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".