Economic diversity of Maine's American lobster fishery
Bibliographic record
Abstract
Maine's coastal communities critically depend on the American lobster fishery, which is now exposed to ocean warming. There is uncertainty about the future robustness of the stock and the economic performance of the fleet appears vulnerable. This research characterizes economic heterogeneity in Maine's fishing fleet using latent class stochastic profit frontier analysis. We explore the diversity of business models and examine how they are associated with the economic performance of the fleet in the prewarming period. The study uses unique firm-level data that capture the operational and economic information of the harvesters in the year 2010, the year before the reported environmental change in the Gulf of Maine. Our findings indicate that economic efficiencies differ based on their choice of business models and it was found that technical upgrades generally contribute to improved economic performance in the prewarming period. Reported societal benefits associated with employment levels have characterized the lobster production environment over firm-level efficiency. This research establishes a critically important baseline for future comparison and quantification of policy reforms within the US 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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".