Fleet-wide stability masks change in the Maine lobster fishery (2008–2022)
Bibliographic record
Abstract
Abstract For most of the past few decades, landings in the American lobster (Homarus americanus) fishery in Maine have been increasing, but a recent downturn in catch suggests the fishery may be at an inflection point. Drawing on multiple datasets associated with the fishery, we use this period of transition to review fleet dynamics in the fishery by analyzing how fishing effort has changed through time (2008–2022). When possible, age, gender, geography, and scale of fishing operation are considered to delimit intra-fleet differences. The results of this review reveal large-scale changes in intra-fleet dynamics that help to explain how there has been the appearance of fleet-wide stability for most of the 15-year study period despite mounting socioeconomic and environmental stressors. Changes in intra-fleet dynamics are most evident across geography and scale of fishing operation. In addition, this study finds that prior research has overestimated a key metric of fishing effort in the Maine lobster fishery by an order of magnitude. This latter insight bears significance because the lobster industry is under mounting pressure to reduce risk of gear interactions with large marine mammals, and future management decisions will likely hinge on estimates about fishing effort and the probability of marine mammal interactions. Continued efforts to understand fishing fleet dynamics and how they differ among segments of the fishery are vital to making well-informed policy decisions in the face of change, including the iconic Maine lobster 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".