Movement of Atlantic bluefin tuna (<i>Thunnus thynnus</i>) as determined by satellite tagging experiments initiated off New England
Bibliographic record
Abstract
Pop-up satellite archival tags were attached to 35 Atlantic bluefin tuna (Thunnus thynnus) off the New England coast of the United States of America in 1998, 2000, and 2001. The tags provided information on the horizontal and vertical movements and environmental preferences of bluefin tuna. Fish showed movement patterns that can be categorized by age and season. Mature individuals were linked to the Gulf of Mexico breeding grounds based on light level longitude and sea surface temperature latitude estimates of geolocation and a radiosatellite endpoint position. A track based on geolocation indicated that a single individual moved from the tagging release location to the Gulf of Mexico and back to New England waters. Tag-generated water column profiles of depth versus temperature were consistent with fish movement along the shelf and into the Gulf of Mexico. Adolescent fish moved from the New England offshore feeding locations to winter aggregations in the coastal shelf and slope waters of North and South Carolina. Bluefin tuna showed a preference for ambient temperatures that ranged from 14 to 26 °C in autumn and from 18 to 24 °C in winter.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".