Modelling Erosion and Deposition of Cohesive Sediments from Hay River, Northwest Territories, Canada
Bibliographic record
Abstract
A large volume sample of river-bed cohesive sediment and water from Hay River, Northwest Territories, Canada was collected during a spring field program in 2000 as part of a study on under-ice movement of sediment just before breakup. Controlled laboratory experiments were subsequently conducted on the Hay River water/sediments in a rotating annular flume at Burlington, Ontario, Canada to better understand the deposition and erosion processes of cohesive sediment transport. The deposition experiments in the rotating flume confirmed that the Hay River sediment is cohesive and the critical shear stress for deposition and the rates of deposition are a function of bed shear stress and the initial concentration of the sediment in suspension. The erosion experiments provided quantitative data on the critical shear stress for erosion and the rates of erosion as a function of bed shear stress and the age of the sediment deposit. The erosion experiments also indicated that the growth of the biofilm had an influence on the erosion characteristics of the Hay River sediment. Based on the data from the rotating circular flume experiments, a modelling strategy is proposed for calculating the under-ice transport of the cohesive sediments in the Hay River.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".