Preliminary importance analyses on model for pH in the presence of organic impurities in the aqueous phase for a severe accident of a nuclear power plant
Bibliographic record
Abstract
In this paper, a model is developed for calculating pH in the presence of organic impurities due to dissolution of paint and/or continuous injection of organic impurities in the sump. The model is implemented in the AnCheBi code for the analysis of chemical behaviors of the iodine in the containment when the pH changes during a severe accident. Validation of the model is performed with P10T2 and P11T1 experiments carried out by AECL in Canada under the BIP project. Importance analyses of the pH calculation model in the AnCheBi code are then performed with the aforementioned experimental data via Latin hypercube sampling on the reaction coefficients, sensitivity analyses of AnCheBi, and calculation of the correlation coefficients between the reaction coefficients and figure of merits (the pH and the concentrations of the various iodine species). From the importance analyses, we provide the sensitivity of the pH calculation model to the change of pH and the concentrations of the various iodine species and the reaction coefficients related with the dominant phenomena underlying the change of pH and the concentrations of the species.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".