Bibliographic record
Abstract
There have been numerous fluvial studies of flow resistance in alluvial channels during the last few decades. Significant progress has been made towards predicting flow resistance (and therefore velocity) for a given discharge. These past applications rely heavily on the characterization of particle sizes and the effects of changing relative submergence on flow resistance estimates. Different types of equations have been shown to provide reasonably good estimates in specific environments. Major difficulties arise from characterizing mobile beds, bed topography and its evolution and how these factors control rates of change of average velocity as discharge rises along a given river reach. Different issues can be recognized as a function of the spatial and temporal scales of investigation. A case can made that more emphasis should be placed upon reach-scale investigations. Detailed studies of bed topography, its maintenance, its evolution (at the reach scale) and its interactions with macroturbulence structure and sediment transport would ultimately provide valuable information and improved knowledge on both flow resistance processes and applications (predictions). Moreover, technological means now allow detailed characterization of bed topography and flow fields of large river systems. Such promising avenues should be further pursued with the goal of providing not only a better understanding of flow-bed-sediment transport interactions in large river systems but also a better understanding of flow stage variations, flood hazards, flow resistance estimates and therefore partitioning of depth and velocity as discharge rises along major river systems.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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".