Morphodynamics of diversion channels in Northern Manitoba, Canada
Bibliographic record
Abstract
The 2-Mile and 8-Mile diversion channels in Northern Manitoba help to maintain the efficiency of Manitoba Hydro’s hydroelectric generating stations located downstream on the lower Nelson River and also assist with flooding control on Lake Winnipeg. Erosion within the channels has been consistently monitored for several decades to better understand these processes to ensure the future performance of the channels. Morphodynamic studies in these channels are complicated due to the high variability of the bed and bank material, the effect of severe cold weather on the erodibility of the channel banks, and the effect of the surrounding lakes on the hydrodynamic conditions of these channels. The present study includes field measurements, experimental testing, and hydrodynamic and thermal numerical modelling to quantify morphological changes within the channels. Moreover, 30 years of monitoring data were analyzed to validate the results of the study. Simple graphs were presented to estimate average applied shear stress over the channel banks and beds based on results of the calibrated and validated hydrodynamic models. Moreover, the effects of wave action on the total applied shear stress were investigated within the 2-Mile Channel under different flow conditions. Comparison between historical cross-sectional survey and results of the numerical models and experiments showed that subaerial processes, mostly freeze–thaw, could be a major eroding factor.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".