De-aggregated reliability analysis of freezing rain hazard
Bibliographic record
Abstract
This work addresses issues for improving the estimation of the recurrence rate and the distribution in severity of extreme ice events in the Montreal area, which is required in order to determine design criteria for structures such as electric transmission lines. Some of the limitations of current methods for studying extreme freezing rain events are due to the relatively short data records. This results in variability of 'at site' data sets that have only a few large accumulations. The methods developed in this work address these issues. First, de-aggregated analysis is used to obtain better statistical fits by grouping storms according to physical variables that are correlated with the occurrence of ice storms (spatial patterns of sea level pressure (SLP) or 1000 to 500 hPa geopotential height anomalies). And second a procedure to decrease the uncertainty on estimates of the hazard function at high return periods based on solving the CRREL Simple icing model using reliability method is developed. In this procedure, uncertainty is propagated through the model by treating it as a function of random variables.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".