Bibliographic record
Abstract
Abstract. The hydrology and hydrography of the Canadian Prairies are complex and difficult to represent in hydrological models. Recent studies suggest that runoff velocities in the Canadian Prairies may be much smaller than are generally assumed. Times to peak, basin-scale flow velocities and roughnesses were derived from hourly streamflow hydrographs from 23 basins in the central Alberta Prairies. The estimated velocities were much smaller than would be estimated from most commonly used empirical equations suggesting that many existing methods are not suitable for estimating time to peak or lag times in these basins. Basin area was found to be a poor predictor of basin-scale rainfall-runoff flow velocity. Estimated velocities generally increased with basin scale, indicating that slow basin response at small scales could be related to predominance of overland and/or shallow subsurface flow over the very level topography. Basin-scale Manning’s roughness parameters, commonly used in hydrological models, were found to be orders of magnitude greater than values commonly used for streams in other parts of the world. The very large values of roughness call into question whether the Manning equation should be used for modelling runoff on the Prairies. These results have important implications for modelling rainfall-runoff in this region since using widely published values of roughness will result in poor model performance. It is likely that the Darcy-Weisbach equation, which is applicable to all flow regimes, may perform better in hydrological models of this region. Further modelling and field research will be required to determine the physical causes of these very small basin-scale velocities.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.036 | 0.032 |
| Insufficient payload (model declined to judge) | 0.109 | 0.090 |
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".