Regional flood estimation for ungauged basins in Sarawak, Malaysia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Design flood estimation is an important task that is required in the planning and design of many civil engineering projects. In this study, the flood records of more than 23 gauged river basins in Sarawak, Malaysia, are examined using an index-flood \nestimation procedure based on L-moments. Two homogeneous regions were identified and the Generalized Extreme Value and the Generalized Logistic distributions are found to describe the distribution of extreme flood events appropriately within the respective regions. A regional growth curve is subsequently developed for each of the regions. These curves can be used for the estimation of design floods in ungauged basins in Sarawak within the limitations identified for the method. The results \npresented herein are useful for practicing engineers in Sarawak while the general methodology may be used in any other regions, provided flood records are available.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it