Mitigating the Effects of Green Crabs (<i>Carcinus maenus</i>) through Incentives in the Lobster Aquaculture Industry
Bibliographic record
Abstract
To work towards the mitigation, control, and eventual eradication of green crabs in the Gulf of Maine, we propose using the invasive species as a bait for the local lobster fishery. By using green crab as bait, we can simultaneously help eradicate this destructive invasive species, reduce pressure on local bait fisheries, and provide a cheaper source of bait for Maine lobstermen. Scientists in Nova Scotia have successfully experimented with using green crab as bait and showed that by using a special trap, they could catch approximately 2,000 green crabs/trap/night and sell the bait at a profit. As herring populations continue to decline due to overfishing, green crabs have emerged as a potentially more sustainable and economical alternative. We will also investigate the possible policy implications of a transition from herring to green crab baits and what gear changes, if any, would be necessary for such an endeavor.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".