Future declines of the binational Laurentian Great Lakes fisheries: the importance of environmental and cultural change
Bibliographic record
Abstract
It is increasingly clear that future long‐term environmental challenges (eg climate change) are being driven by economic and cultural choices, as well as by physical and biological mechanisms. We looked at the extent to which these apply to potential future changes in fisheries in the Laurentian Great Lakes. These fisheries rank among the most valuable freshwater fisheries in the world, but have declined markedly in recent decades. To investigate how these fisheries might develop in the future, we elicited projections from experts in fisheries and related fields. Experts provided assessments on variables relating to US and Canadian commercial (pounds landed) and sport (participation and expenditures) fisheries for the years 2006 and 2025. We measured each expert's ability to quantify their uncertainty, producing performance‐weighted combinations of expert estimates. All experts expected commercial fisheries to decline from 2006 to 2025, with greater declines in the US (25%) than in Canada (9%). Expectations for sport fishing differed more between lakes and less between countries, with median expected declines ranging from 1% to 13%. Experts attributed expected declines primarily to changes in economic market demands and shifts in societal interests. Increased attention to social and economic trends could aid Laurentian Great Lakes fishery policy and management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".