Evolutionary Optimization of an Ice Accretion Forecasting System
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
Abstract The ability to model and forecast accretion of ice on structures is very important for many industrial sectors. For example, studies conducted by the power transmission industry indicate that the majority of failures are caused by icing on overhead conductors and other components of power networks. This paper presents an extension to the ice accretion forecasting system (IAFS) that is comprised of a numerical weather prediction model, a precipitation-type algorithm, and an ice accretion model. To optimize the performance of IAFS, the parameters of the precipitation-type algorithm are estimated using a genetic algorithm. The system is developed by hindcasting a well-documented freezing-rain event and calibrated using four additional ice storms. Subsequently, the system is tested using three independent storms. The results show a significant improvement in consistency, accuracy, and skill of IAFS. The methodology described in this contribution is not limited to ice accretion modeling—it provides a general approach for setting operational parameters of data-processing algorithms to achieve interoperability of numerical weather prediction models with add-on applications based on empirical observations.
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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