Using River2D Morphology to Predict Salmon Redd Survival during High Flow Events from Hydroelectric Dam Operations
Bibliographic record
Abstract
Flooding related to hydroelectric dam operations may be the result of increased inflows to reservoirs, natural events, and operational or structural failures and can cause significant impacts to downstream fish populations and their habitats. River2D Morphology (R2DM), a 2D hydrodynamic, morphology and gravel transport model is adapted to predict the survival rate of salmon redds during high flow events using a unique algorithm based on depth of redd burial. For a study of a reach downstream of the John Hart Dam on Campbell River in Vancouver Island, British Columbia of both controlled and uncontrolled operational high flows the predicted losses are 13, 42, 45, and 50% for events with return periods of 2 –8 years, 20-year, 100-year and 200-year, respectively. R2DM is shown to be useful as a tool in predicting salmon egg loss in gravel bed rivers.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".