Flow dynamics and bedload sediment transport around paired deflectors for fish habitat enhancement: a field study in the Nicolet River
Bibliographic record
Abstract
Schemes to restore fish habitat in rivers often involve installing instream structures such as current deflectors to create and maintain riffle-pool sequences. However, there is a lack of field studies on the impact of these structures on flow dynamics and bed topography. The objective of this research is to characterize flow dynamics and sediment transport around paired deflectors used to enhance fish habitat in the Nicolet River, Quebec. Bed and bank topography surveys were taken with a total station, and velocity and bed shear stress estimates were obtained from an acoustic doppler velocimeter. Bedload sediment transport was assessed by two methods: tracer rocks (painted "particles" and passive integrated transponder tags) and sediment traps. Results show marked differences in bedload sediment transport patterns between the left bank and the right bank downstream of the deflectors. This is surprising considering that paired deflectors should produce a relatively symmetrical disruption to the flow field on each side. More high-flow dynamics data during overtopping conditions are required to understand the complex interactions between these instream structures and bedload transport. Key words: stream restoration, pool, bedload transport, radio frequency identification (RFID), passive integrated transponder (PIT) tags, fieldwork, deflectors, fish habitat.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".