Restoring aquatic ecosystem connectivity requires expanding inventories of both dams and road crossings
Bibliographic record
Abstract
A key challenge in aquatic restoration efforts is documenting locations where ecological connectivity is disrupted in water bodies that are dammed or crossed by roads (road crossings). To prioritize actions aimed at restoring connectivity, we argue that there is a need for systematic inventories of these potential barriers at regional and national scales. Here, we address this limitation for the North American Great Lakes basin by compiling the best available spatial data on the locations of dams and road crossings. Our spatial database documents 38 times as many road crossings as dams in the Great Lakes basin, and case studies indicate that, on average, only 36% of road crossings in the area are fully passable to fish. It is therefore essential that decision makers account for both road crossings and dams when attempting to restore aquatic ecosystem connectivity. Given that road crossing structures are commonly upgraded as part of road maintenance, many opportunities exist to restore connections within aquatic ecosystems at minimal added cost by ensuring upgrade designs permit water flow and the passage of fish and other organisms. Our findings highlight the necessity for improved dam and road crossing inventories that traverse political boundaries to facilitate the restoration of aquatic ecosystem connectivity from local to global scales.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".