Measured swelling, hydraulic and thermal properties of MX-80 bentonite: Distinguishing between material variability and measurement limitations
Bibliographic record
Abstract
The Canadian Nuclear Waste Management Organization (NWMO) is investigating Sedimentary and Crystalline media as potential hosts for a Deep Geological Repository (DGR) for permanent isolation of used nuclear fuel and fuel wastes. In NWMO's concept, densely compacted bentonite clay is used to surround the used fuel containers and to backfill the placement rooms. Development of sealing concepts and performance prediction models requires many materials properties inputs. These inputs are important to the evaluation of hydraulic conductivity ( k ), swelling pressure ( P s ) and heat transfer. Evaluation of these and other properties is complicated by factors such as unidentified variability in material composition (mineralogy) and differences in test methodologies. In order to eliminate the effects of technique, data obtained using similar methods are used in this study. This should result in data scatter being more related to materials variability and limitations than measurement method. In an effort to assess the variability in the reported Ps, k and thermal properties and determine representative values or relationships for input into performance models, sufficiently large databases are needed to allow for statistical evaluation for a full range of potential repository conditions. Unfortunately, in many cases these databases are not extensive, particularly for materials in a highly saline environment. This paper presents statistical evaluations of the relationships between density, k , P s and thermal properties using a database generated from literature sources for a well-known bentonite (MX-80) under freshwater and saline porefluid conditions. Also included is previously unpublished work by NWMO where repository-relevant groundwaters were used in testing. To limit uncertainty, the data used were obtained from sources reporting use of MX-80 bentonite and providing at least basic mineralogical information. From these data, best-fit, confidence and prediction bands and intervals were generated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".