Movements of Atlantic halibut in the Gulf of Maine based on geolocation
Bibliographic record
Abstract
Abstract Atlantic halibut are a “Species of Concern” in US waters and little is known about their movements and stock structure. Recent stock assessments drew attention to the paucity of information for assessing and managing this stock. To investigate movement patterns and stock structure, halibut were tagged off Massachusetts and Maine within US waters of the Gulf of Maine region using pop-up satellite archival tags and data storage tags. A hidden Markov model (HMM) geolocation method previously developed for other groundfish species was adapted to estimate the movement tracks of the tagged halibut (n = 25) based on the tag-recorded depth and temperature. Total distance travelled based on geolocation ranged from 36 to 1701 km, whereas straight line distance between tagging and end locations ranged from 0.4 to 440.7 km. Estimated movement rates varied between 2.7 and 10 km day−1. Two tagged halibut made long-distance movements to putative spawning habitat in the Northeast Channel off Georges Bank. Thirteen (13) out of 25 geolocated individuals were estimated to have reached Canadian waters. Geolocation results revealed home range, return movement, and seasonal migration movement patterns exhibited by the tagged halibut. The HMM geolocation method could be a useful tool in providing information on halibut movements that can inform stock assessment and management decisions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".