Bibliographic record
Abstract
Frequently, an assessment of the mean water velocity in a stream is necessary to estimate the discharge associated with a particular flow depth or, conversely, the mean depth associated with a particular discharge. In the absence of a direct measurement of flow velocity, a flow resistance approach, which establishes the relation between depth and velocity, can be applied. Two approaches have been used in the past: traditional approaches based on the use of a resistance coefficient (e.g., Darcy‐Weisbach) or dimensionless hydraulic geometry approaches. To examine if one approach is more appropriate for steep streams, data from 31 flume experiments conducted to examine flow resistance in self‐formed cascade channels were analyzed. A dimensionless hydraulic geometry approach developed using at‐a‐station data to characterize the q * exponent and between‐site data to characterize the exponent on the channel slope term was more accurate than more traditional approaches. The developed relation was similar to the established rational relation ( v α g 0.2 q 0.6 s −0.4 S 0.2 ), suggesting that the rational relation has merit. The approach does not utilize a flow partitioning approach since, in steep streams with small relative depths, the grains themselves generate form and spill resistance. The observation that a single dimensionless hydraulic geometry flow resistance relation can describe measurements across a range of grain sizes and bed slopes (3–21%) suggests that steep streams may follow a single scaling relation similar to the regime equations associated with lowland rivers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".