Forecasting the water inflow into the Krasnoyarsk and Sayano-Shushenskoe reservoirs in the second quarter of the year
Why this work is in the frame
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Bibliographic record
Abstract
We consider the various methods of constructing models intended to forecast the average water inflow, in the second quarter of the year, into two reservoirs on the Yenisei river. To solve modeling problems used a new computer technology implemented in the specialized “Stochastic Modeling” software package. Independent data were employed to verify the variants of the models for the formation of variability in quarterly inflow as generated based on different algorithms. A more sophisticated and robust model for forecasting the inflow was constructed as an ensemble of partial models. Based on aggregate results of modeling, we suggest the method of constructing a forecast of the average (for the second quarter) lateral inflow into the Krasnoyarsk reservoir and the inflow into the Sayano-Shushenskoe reservoir by use of observational data accumulated by Srednesibirskoe UGMS (Weather Control and Environmental Monitoring Service), based on an ensemble of partial models. It is established that such an operation reduces the probability of forecasting errors implying an arbitrary selection of models. We constructed forecasts of the aforementioned characteristics using real-time data for 2015. It is stated that the solution of the forecasting problem can be facilitated by using additional information.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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