A generalized two-step model for predicting thermal conductivity of bentonite-based mixtures in nuclear waste repositories
Bibliographic record
Abstract
Thermal conductivity (TC) of buffer/backfill materials critically governs high-level radioactive waste repository performance. Based on the geometric mean method and effective medium theory (EMT), a “two-step” TC prediction model for bentonite-based mixtures was proposed. A solid-phase correction factor ( c) and water–air balance factor ( z) are introduced to adjust the contribution weights of the solid, liquid, and gas phases to the TC of the matrix (bentonite–water–air). The EMT was then employed to embed additives as a dispersed phase into the matrix, establishing a computational framework for mixture TC. Based on 538 sets of measured TC for bentonite-based mixtures, the predictive performance of the new model and existing prediction models was evaluated. The results indicate that for both single and multi-component bentonite-based mixtures, the new model demonstrates significantly superior predictive accuracy compared to existing models. The R 2 values of existing models remain below 0.6 (e.g., differential effective medium theory model 0.536 for bentonite–graphite mixtures, improved geometric mean model 0.576 for bentonite–graphene oxide mixtures, and up to 0.581 on the entire database), whereas the proposed model achieves R 2 values of 0.836–0.979 across specific mixtures and 0.868 for the entire database, demonstrating a substantial improvement in predictive accuracy. By incorporating parameters c and z, the new model accurately captures the combined influence of individual phases on mixture TC. The model also exhibits enhanced universality for predicting the TC of diverse mixtures and effectively elucidates the influence patterns of key factors on thermal performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".