Movement of american lobster Homarus americanus associated with offshore mussel Mytilus edulis aquaculture
Bibliographic record
Abstract
Bivalve aquaculture sites attract a variety of large benthic species. Previous studies have shown that American lobsterHomarus americanusare more abundant in musselMytilus edulisfarms than in areas outside of them, suggesting that farms provide lobsters with adequate food and shelter. This study used acoustic telemetry to evaluate the influence of longline mussel farms on lobster movement behavior. In 2014, 60 lobsters were acoustically tagged on a boat and released in a mussel farm and at 2 reference sites outside the farm. Most lobsters (92%) left the monitored area within 1 d post-tagging; those released in reference sites moved northeast, whereas those released in the farm moved in random directions. Of the 16 lobsters that stayed or returned to the study area over the course of the 2 mo experiment, 10 displayed nomadic movements, 3 displayed small, local movements—presumably associated with foraging behavior, and 3 displayed both movements. The time lobsters spent within a site, distance travelled, and walking speed did not differ between the farm and reference sites. A second experiment was done in 2017 over 2 mo to evaluate tagging method (‘on boat’ andin situtagging) effects on lobster movement behavior. The experiment followed movements by 50 lobsters, half for each treatment, and showed that tagging method can affect walking speed during the first 24 h, but had no impact on the residence time and the distance travelled by the lobsters.
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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 | 0.000 | 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".