Role of a 'combination rule' in hybrid short-term prediction of hydrological events
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
Data-driven hydrological predictions based on supervised classification have recently gained momentum. This technique supports the classification of waterbodies and flood events that occur at different watersheds, predictions of a class of a hydrological event, e.g., 'high-' or 'low-flow', as opposed to forecasting magnitudes of streamflow characteristics generated by ANNs, regression models or other modelling tools. Flood management teams declare a state of emergency and/or take mitigation measures based on a set of business rules reflecting water level exceedance of an established threshold. Therefore, predicting a class of a hydrological event, e.g. 'flood' or 'no-flood', carries even more important information for operational flood managers than projected magnitudes of streamflow characteristics. When predictions of a class of an event are obtained based on data available in real-time, they can be easily deployed in flood management. Scientific literature has demonstrated the usefulness of various classification algorithms (inducers) in applied hydrology. The performance of these inducers, however, deviated notably on different data sets. To alleviate these deviations and generate forecasts with reduced generalization error, an ensemble of classifier can be constructed.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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