Thermal properties of engineered barriers for a Canadian deep geological repository
Bibliographic record
Abstract
Global energy needs continue to rise along with society’s desire for carbon-reduced energy sources to limit climate change effects. One viable carbon-reduced energy source is nuclear power, which provides more than half the electricity requirements of the province of Ontario. Within Canada there are more than 2.5 million bundles of spent nuclear fuel, which will be stored in a deep geological repository. Efficiency of the repository system depends on dissipation of thermal energy. A comprehensive experimental study is presented on thermal properties of barrier materials. The influence of bentonite type, variability, moisture, and temperature on thermal properties is examined. Results show strong influence of moisture on thermal properties, some influence of temperature on low-density bentonite, minor influence of bentonite type, as well as low variability in the experimental measurements. The extensive database of physical measurements is compared with values from the literature and then used to statistically evaluate thermal property models selected from the literature. Using the base parameters from the literature, thermal property models performed adequately; however, soil-specific calibration of the model inputs improved the fit significantly. These results are now available to perform the numerical models for the proposed Canadian deep geological repository for used nuclear fuel.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".