Consequences of changing water clarity on the fish and fisheries of the Laurentian Great Lakes
Bibliographic record
Abstract
Human-driven environmental change underlies recent changes in water clarity in many of the world’s great lakes, yet our understanding of the consequences of these changes on the fish and fisheries they support remains incomplete. Herein, we offer a framework to organize current knowledge, guide future research, and help fisheries managers understand how water clarity can affect their valued populations. Emphasizing Laurentian Great Lakes findings where possible, we describe how changing water clarity can directly affect fish populations and communities by altering exposure to ultraviolet radiation, foraging success, predation risk, reproductive behavior, or territoriality. We also discuss how changing water clarity can affect fisheries harvest and assessment through effects on fisher behavior and sampling efficiency (i.e., catchability). Finally, we discuss whether changing water clarity can affect understudied aspects of fishery performance, including economic and community benefits. We conclude by identifying generalized predictions and discuss their implications for priority research questions for the Laurentian Great Lakes. Even though the motivation for this work was regional, the breadth of the review and generality of the framework are readily transferable to other freshwater and marine habitats.
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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 | 0.002 |
| 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".