Using high-resolution bedload transport tracer measurements to investigate the characteristics of bedload transport over a large urban flood event
Bibliographic record
Abstract
Channel morphological change is often evaluated by employing sediment transport models since field data during high magnitude low frequency events is rarely available. However, sediment transport rate estimates are heuristic at best to within 1 - 3 orders of magnitude. Mimico Creek is an urban gravel-bed channel in Southern Ontario, Canada that has undergone intensive event-based sediment transport sampling and inter-event bed material particle tracking over a three-year period. A HEC-RAS model was developed of the study reach and calibrated to a series of discharge events where in-situ bedload sampling occurred. Both step-wise discharge and unsteady flow simulations were evaluated to compare sediment transport rates for a range of transport models which included the Meyer-Peter Müller and the Wilcock-Crowe. Calibration curves were developed to estimate sediment discharge in Mimico Creek. The results of the calibrated model were used to calculate the mean travel distance of bed material using the expression for the volumetric rate of bed material transport. Results from the modelling exercise found mean travel distances were similar and in some cases larger than those observed from field measurements, considering both mobile and immobile particles.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".