Assessment of a Baited Remote Underwater Video Method to Evaluate American Lobster ( <i>Homarus americanus</i> ) Response to Baits in Nature
Bibliographic record
Abstract
ABSTRACT The American Lobster ( Homarus americanus ) is the target of an extensive fishery in the Northwest Atlantic, yet there is no systematic method for evaluating relative performance of baits for the fishery. We used Baited Remote Underwater Video (BRUV) to assess lobster attraction to commercial baits versus natural prey. Significantly higher counts of lobsters in BRUV with commercial baits (herring, mackerel, and rock crab) matched expectations from catches in the fishery. Although location, substrate type, month, and year of deployment also affected lobster counts, the effect of bait type did not depend on any of these potentially confounding factors. Thus, we infer that BRUV can be used to compare lobster responses to different bait types regardless of when or where deployed, providing a useful tool for future initial testing of alternative baits before validation in the fishery.
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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 | 0.001 | 0.002 |
| 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".