Why do fish strand? An analysis of ten years of flow reduction monitoring data from the Columbia and Kootenay rivers, Canada
Bibliographic record
Abstract
Abstract Stranding of fish due to flow reductions has been documented in the near shore of the Columbia and Kootenay Rivers, Canada, and can result in sub‐lethal or lethal effects on fish. Ten years (1999–2009) of monitoring data have been collected at sites below two hydro‐electric dams (Hugh‐L‐Keenleyside and Brilliant Dam) following flow reductions. A generalized linear mixed effects model analysed the probability of a stranding event in relation to environmental and operational variables including the rate of change in the water levels, the duration of shoreline inundation prior to a reduction (wetted history), the river stage, the magnitude of the reduction, distance downstream from the dam, time of day, day of year (season) and whether a site had been physically altered to mitigate stranding. The results demonstrated statistically significant effects on stranding risk from minimum river stage, day of the year and whether a site had been physically re‐contoured. The combination of investigated factors giving the highest probability of stranding was a large magnitude reduction completed in the afternoon in midsummer, at low water levels when the near shore had been inundated for a long period. This research is significant in its approach to assessing years of ecosystem scale monitoring data and using the modelling results to determine ways for these findings to be applied in regulated river management to minimize fish stranding. It also highlighted data gaps that require addressing and provides ecosystem scale results to compare with stranding studies carried out in mesocosms. © 2014 The Authors. River Research and Applications published by John Wiley & Sons Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".