Bibliographic record
Abstract
Abstract Channel roughness, often described by Manning's n, is used to represent the amount of resistance that flow encounters, and has direct implications on velocity and discharge. Ideally, n is calculated from a long‐term record of channel discharge and hydraulic geometry. In the absence of these data, a combination of photo references and a validated qualitative method is preferable to simply choosing n arbitrarily or from a table. The purpose of this study was to use United States Geological Survey (USGS) streamflow data to calculate roughness coefficients for streams in the mountains of North Carolina. Five USGS gage stations were selected for this study, representing drainage areas between 71.5 and 337 km2. Photo references of the study sites are presented. Measured discharges were combined with hydraulic geometry at a cross‐section to calculate roughness coefficients for flows of interest. At bankfull flow, n ranged between 0.039 and 0.064 for the five study sites. Roughness coefficients were not constant for all flows in a channel, and fluctuated over a large range. At all sites, roughness was highest during low‐flow conditions, then quickly decreased as flow increased, up to the bankfull elevation.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".