Inferential-Statistical Reevaluation of Spent Fuel Zircaloy Cladding Integrity
Bibliographic record
Abstract
From the published results of experiments investigating the effects of delayed hydride cracking (DHC) on spent fuel Zircaloy cladding integrity, relevant data have been extracted and re-analyzed, taking advantage of inferential statistics and an information-theoretic model selection criterion. Statistical tolerance intervals, the method of maximum likelihood estimation, and the Akaike information criterion, corrected for small sample size, were applied to a small sample of measured values of the threshold stress-intensity factor KIH. The purpose was to create a well-grounded probability density function for use in a mathematical model correlating random variates of KIH with important conditions for the initiation of crack growth by DHC, specifically, cladding hoop stress and the depth and shape of surface flaws. A selection criterion purposely designed for small sample sizes and the robust nature of inferential statistics were ideally suited for the intended reevaluation. The fidelity of the mathematical model was protected by the exclusion of any simplifying approximations, e.g., substitution of constants or single-valued descriptive statistics for variables. The probabilistic effect of the random variable KIH was thereby precisely mapped onto the linearly correlated variable, threshold cladding hoop stress, as a function of surface flaw depth and shape. Contour plots of the results constitute significant improvements over previous quantitative single-point estimates of the effects of DHC on spent fuel Zircaloy cladding integrity.
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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 | 0.040 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".