Tools for the assessment of hydrological ensemble forecasts obtained by neural networks
Why this work is in the frame
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Bibliographic record
Abstract
The increasing demand for uncertainty assessment in streamflow forecasts has drawn the hydrological community's interest toward ensemble forecasting techniques. The widespread deterministic hydrological forecasting point of view focuses to a great extent on the search for a hydrological model that would come as close as possible to “perfection” (i.e. the aim is to implement a model that produces a point forecast that is as close as possible as the observed outcome). On the other hand, ensemble forecasting departs from the deterministic point of view by avoiding the assumption that the “perfect” model exists and instead focuses on issuing a type of forecast that accounts explicitly for the uncertainty inherent to the forecasting process as a whole. In this paper, one-day-ahead hydrological ensemble forecasts obtained by stacked neural networks are presented and analysed. To do so, three simple performance assessment criteria are presented. Those criteria were originally developed in the meteorological and statistical communities to accommodate the need for a quality assessment methodology that is coherent with the probabilistic nature of ensemble weather forecasts. It will be shown that, even though the ensemble forecasts suffer from underdispersion, they outperform point forecasts.
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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 | 0.001 | 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 | 0.001 |
| 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