Development of a Statistical Model of Uncertainty in Time-Dependent Channel Power Distribution Predictions
Bibliographic record
Abstract
This paper describes the development of a statistical model of uncertainty in channel powers predicted for a 480-channel CANada Deuterium Uranium (CANDU) reactor. It is expressed as the sum of ripple prediction uncertainty and reactor power uncertainty. Ripples are ratios of instantaneous channel powers (prorated to 100% of full power) to reference channel powers. The ripple prediction uncertainty model is a multivariate normal distribution whose covariance matrix captures a unique variance for every channel as well as a unique covariance between every pair of channels. Reactor power uncertainty is common to all 480 channels.Central to this work is the distinction between apparent uncertainty, measurement uncertainty, and prediction uncertainty. Ripple prediction uncertainty is quantified by removing the contribution of ripple measurement uncertainty to ripple apparent uncertainty (differences between computer code–predicted ripples and measured ripples). This is done because measurement uncertainty causes apparent uncertainty to exceed prediction uncertainty. Measurement uncertainty is quantified using a novel approach referred to as the sister channel approach with time shifting. This approach uses differences between measured ripples in sister channels to quantify actual measurement uncertainty. The time-shifting aspect of the approach accounts for the fact that true ripples in sister channels are not identical at the same time, mainly because sister channels and their neighboring channels are refueled at different times.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".