Monitoring surface flow velocities at hydraulic barriers in a bedrock canyon
Bibliographic record
Abstract
Several species of Pacific Salmon migrate upstream every year in the Fraser River (British Columbia, Canada) to reach their spawning grounds. A large portion of the fish get delayed in specific sections of the river, where the morphology and the flow create sections with high flow velocity, high turbulence and jumps in bed elevation that constitute hydraulic barriers. Several fish populations migrate along the Fraser River and any barrier for their passage (created by the river morphology, the flow structure or a localized landslide) can severely endanger the survival of such species, while also impacting indigenous communities for which fish is a fundamental element. In the Fraser River those hydraulic barriers have been identified but not yet thoroughly studied. Here we present results from an extensive monitoring campaign conducted in the last 3 years to measure surface flow velocities in previously identified hydraulic barriers. We selected segments of the Fraser Canyon where distributions of surface flow velocities due to specific river morphologies are potentially impacting fish passage. These include areas where series of constriction-pool-widenings create plunging flows, bedrock step rapids and overfalls. In such areas we collected video of surface flows (with fixed field cameras and drones) at high frequency during the freshet season and obtained surface flow velocity maps using Large Scale Particle Image Velocimetry (LSPIV) for different values of flow discharge. Using this extensive dataset, we can detect how stable coherent flow structures are for different flow depths and flow discharges and we are also able to identify which areas are most problematic for fish passage (and for which values of flow), helping fish management agencies and public authorities to better protect the survival of vital species in British Columbia.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".