Evaluating fish passage effectiveness through a sequence of modified vortex rock weirs
Bibliographic record
Abstract
Vortex rock weirs (VRW) are often used in natural channel design applications to maintain channel form and function, provide physical channel stability, and contribute to habitat enhancement. A balanced approach is required to achieve conflicting goals of VRWs, which include providing erosion protection while allowing fish passage under various water level conditions. This research evaluated a sequence of asymmetrical rock weirs with 3-dimensional flow. Field assessments completed between June and September 2018 monitored water level, water temperature, and surveyed channel features at 10 rock weirs and 11 adjacent pools under different water level conditions. The structural dimensions and local velocity at each rock weir were compared to the swimming characteristics of local fish species to determine fish ‘passability’ and suggest best practices for rock weir design and construction. Results concluded fish passage occurs through gap and over-weir flow pathways and was most effective under low water level conditions. Further, appropriate design considerations based on rock weir gradient, rock weir width, keystone size, and pool length contributed to 100% fish passage effectiveness under all water level conditions. To address conflicting goals and the impact on fish passage for small-bodied fish species, methodology is provided for predicting local velocity and fish passage effectiveness through rock weir systems, inform best practices for rock weir design and construction while balancing the requirements for channel stability and fish passage, and contribute to fish population management strategies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".