Identifying the Interconnection Between Maine's Lobster Industry and the North Atlantic Right Whale Population in Order to Guide Federal Action
Bibliographic record
Abstract
North Atlantic Right Whales (NARW) are on the verge of going extinct because of human activity. Entanglements in fixed fishing gear and vessel strikes are killing NARW at such a rate that their extinction is inevitable unless human-caused deaths are significantly reduced. The National Marine Fisheries Service (NMFS, a branch of the National Oceanic and Atmospheric Administration) has subjected Maine’s lobster fishery to regulations aimed at protecting whales since 1997 as part of the Atlantic Large Whale Take Reduction Plan. Recently proposed changes to the plan would effectively regulate Maine’s lobster industry out of existence. Lobstering is a cultural and economic staple of the state, so losing the lobster industry would be devastating to both the economy and identity of many coastal Maine communities. This thesis examines how Maine’s lobster fishery threatens NARW, the historical measures that have been taken by the NMFS to mitigate those threats, and examines a number of possible solutions that could protect both Maine lobstermen and NARW moving forward. This project also highlights the controversy that has arisen as a result of the newly proposed rules, and looks at fishery management strategies utilized by the state of Maine and Canada to inform decisions made at a federal level. The analysis is based on the input of the scientific community, lobstermen, lawmakers, regulators, and conservation activists in the form of professional interviews and the review of relevant rules, laws, and scientific literature. The analysis culminates in several suggestions as to how fishermen, lawmakers, and regulators can cooperate to improve conditions for NARW without crippling Maine’s lobster industry.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".