Adaptive capacity of the Maine lobster fishery: insights from the Maine Fishermen’s Climate Roundtables
Bibliographic record
Abstract
The Gulf of Maine in the northwestern Atlantic Ocean is one of the world’s fastest warming marine regions. Changes in ocean conditions are affecting growth, survival, and distribution of American lobster ( Homarus americanus), which supports iconic fisheries along the coast of Maine, USA. In this study, we analyzed 15 years of oral records from the Maine Fishermen’s Climate Roundtables to explore fishermen’s observations of and responses to social–ecological changes. Fishermen reported an overall shift in lobster biomass further east and offshore, resulting in strategic expansion of fishing seasons and areas. Biomass shifts were thought to be connected to increases in temperature, decreases in salinity, a shift in ocean currents, and a loss of predator species. Fishing strategies were categorized according to five domains of adaptive capacity, but the majority of fishers’ responses fell into two domains: “access to assets” and “diversity and flexibility”. Strategies within these domains included increased expansion into federal lobster fisheries and extension of fishing seasons. Fishermen highlighted data gaps that need to be addressed to meet the challenges of climate change. Fisheries learning exchanges, such as the Climate Roundtables, create social networks that foster knowledge sharing to support the continued viability of local livelihoods.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".