Considering aquatic connectivity trade-offs in Great Lakes barrier removal decisions
Bibliographic record
Abstract
Globally, construction of dams has led to challenges for fishery managers and decision makers. Hundreds of thousands of dams, many of which no longer serve their intended purpose, are in need of repair. Resources to make those repairs are limited, and dam removal often seems like the most logical solution from an economic perspective. However, dams on the Laurentian Great Lakes tributaries often serve more than one purpose, with nearly 500 considered important to the continued success of controlling sea lamprey (Petromyzon marinus), blocking other invasive species, isolating native from non-native salmonids, stopping disease transfer, and holding back contaminated sediments. Removing dams can have unintended consequences at the local, regional, or basin-wide scale. Here, we explain the importance of considering potential fishery management trade-offs of barrier removals at those scales. We also suggest an organizational framework that, when supported by modeling, could improve communication and cooperation among partners, streamline the decision process, and provide a consensus-driven perspective about the highest priority projects to address. Consistent communication among and between management agencies, indigenous peoples, and local governments, along with an objective and proactive approach to barrier removal decisions could allow for greater success in habitat restoration and funding procurement while reducing the risk of barrier failures and the unintended spread of injurious invasive species, environmental contaminants, and fish disease.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".