Experimental program on mechanical properties of large rock fractures
Bibliographic record
Abstract
Abstract Predictions of fracture displacements are required to support the safety assessments of a deep geological repository for nuclear spent fuel. Laboratory and in-situ experiments are used to estimate these properties. Despite significant contributions in the last decades, there is a knowledge gap in terms of the impact of high normal stresses on the mechanical properties of large-scale fractures under Constant Normal Stiffness (CNS) boundary conditions. Within the framework of the POST project, a cooperative effort was made by SKB (Sweden), NWMO (Canada), and Posiva from Finland (in phase 1) to study these questions. In the second phase of the POST project, a first of a kind direct shear testing machine was manufactured and calibrated that can accommodate samples up to 400 × 600 mm under normal stresses up to 10 MPa, for both CNS and Constant Normal Load (CNL) conditions, with the ability to shear the sample up to 50 mm. Several best practice procedures were developed for fracture characterization pre-, syn-, and post-shear test which utilize high resolution optical scanning, contact pressure measurements, Digital Image Correlation (DIC) measurements, and acoustic emission measurements during the shear test. Natural and tensile-induced fractures of a granitic rock as well as replicas of the hard rock fractures, at three different fracture sizes of 35×60 mm, 70×100 mm, and 300×500 mm, are now being tested. It is hoped that this program will provide a set of high-quality data which will help reduce the knowledge gap in the understanding of fracture behavior.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".