Evaluating the thermal performance of unsaturated bentonite–sand–graphite as buffer material for waste repository using an improved prediction model
Bibliographic record
Abstract
Buffer materials that are used to isolate heat-emitting waste canisters must bear a strong thermal load and have good thermal conductivity and stability. This study investigated the strengthening of internal thermal conduction via bentonite sealing. An admixture of quartz and graphite accelerated the heat transfer into the host rock. Samples containing different bentonite–sand–graphite (BSG) mixtures were prepared. The influence of the volumetric water content, degree of saturation, dry density, porosity, sand content, and particle size on the thermal conductivity of the BSG mixtures was analyzed by conducting a series of thermal needle tests. Electrical resistivity tests were conducted to examine the electrical resistivity of the BSG mixtures, and the dependency of soil thermal conductivity on the volumetric water content based on electrical resistivity data. The results indicated that the dependency of thermal conductivity on the volumetric water content was closely related to electrical resistivity. Based on the thermal conductivity inflection point as determined by the volumetric water content corresponding to the electrical resistivity inflection point, an improved series–parallel thermal conductivity prediction model and the method to determine model parameters for the unsaturated BSG mixtures were proposed. The precision of the proposed prediction model was verified based on laboratory data.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".