Informing Watershed Connectivity Barrier Prioritization Decisions: A Synthesis
Bibliographic record
Abstract
Abstract Water resources and transportation infrastructure such as dams and culverts provide countless socio‐economic benefits; however, this infrastructure can also disconnect the movement of organisms, sediment, and water through river ecosystems. Trade‐offs associated with these competing costs and benefits occur globally, with applications in barrier addition (e.g. dam and road construction), reengineering (e.g. culvert repair), and removal (e.g. dam removal and aging infrastructure). Barrier prioritization provides a unique opportunity to: (i) restore and reconnect potentially large habitat patches quickly and effectively and (ii) avoid impacts prior to occurrence in line with the mitigation hierarchy (i.e. avoid then minimize then mitigate). This paper synthesizes 46 watershed‐scale barrier planning studies and presents a procedure to guide barrier prioritization associated with connectivity for aquatic organisms. We focus on practical issues informing prioritization studies such as available data sets, methods, techniques, and tools. We conclude with a discussion of emerging trends and issues in barrier prioritization and key opportunities for enhancing the body of knowledge. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.036 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".