Topographic Influences on Flood Frequency Analyses
Bibliographic record
Abstract
A model that predicts the formation of variable source areas by using the catchment’s topographic index distribution is used to determine the impact of topography, and the variation in contributing area, on the flood frequency distribution of a small catchment in southern Ontario. Log Pearson Type III distributions were fit to data using four different topographic distributions and 1000 randomly-generated storms and antecedent moisture conditions. The four topographic distributions included one for the actual catchment, a second that produced smaller contributing areas, a third that produced larger contributing areas, and a fourth in which the entire catchment contributed to runoff, if runoff was generated during the storm. All data were fitted reasonably well, except for the fourth case which produced the poorest fit. For return periods of less than 10 years, all topographic distributions produced peak flow rates that were roughly similar in magnitude, with the fourth case having the lowest values. However, at return periods of more than 10 years, the third topographic distribution produced higher flow rates than the first and second topographic distributions, while the fourth distribution produced the highest flows of all.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".