Relationship between wadi drainage characteristics and peak-flood flows in arid northern Oman
Bibliographic record
Abstract
Relationships between watershed characteristics and mean wadi flood peaks in arid regions are investigated. Mean flood peak discharge was derived for 12 watersheds ranging from 64 to 1730 km2 in Oman using 270 flood events from 10 years of record. Fourteen watershed characteristics were automatically extracted from a digital elevation model, and multiple regression was used to investigate the effect of these characteristics on wadi mean peak flow (Q MPF) and 5-, 10-, 20-, 50- and 100-year return period flood peaks. Drainage area (DA), wadi slope (WS), watershed mean elevation (BE) and agricultural/farm area (FR) were found to be the key variables affecting flood flows. As return period increased, the influence of BE on flood-peak estimation decreased. In addition, urbanization is increasing alongside increasing agricultural areas, and the inclusion of FR in the relationship improved the variance explanation by 11% over models using only traditional variables such as DA and BE. Citation Al-Rawas, G. A. & Valeo, C. (2010) Relationship between wadi drainage characteristics and peak flood flows in arid northern Oman. Hydrol. Sci. J. 55(3), 377–393.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".