Bibliographic record
Abstract
Lobster fishing is one of Maine’s most famous and important industries. In order for the industry to thrive, it is necessary that the lobster stock continue to be bountiful. One way to achieve a bountiful stock of lobster is to place limits on the amount of lobster that can be fished in any given year. The legal world offers a number of ways to achieve this end. Some mechanisms that have been employed in various jurisdictions include minimum and maximum legal sizes, v-notching, and trap limits. Although these laws can be very effective in reducing the number of lobsters caught and therefore increasing the number of lobster in the ocean, they may paint with too broad a brush. More selective laws that are crafted based on lobster biology could lead to an increased lobster stock while also allowing for a large, profitable harvest from year to year. Through insights gained by a survey of selected laws governing lobster fishing from Maine, New Hampshire, Massachusetts, and Canada, and a review of the biology of the American lobster, this article suggests new laws and a new approach to drafting the law, both aimed at increasing the stock of Maine lobster and maintaining large harvests.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".