The study of stress determination and back calculation in the Canadian shield
Bibliographic record
Abstract
The Deformation Rate Analysis (ORA) technique can be used to estimate the in situ stress from orientated core<br/>obtained from exploration or for example from a block of rock extracted from the side of a drive. In the former case<br/>it doesn't require underground access and in both cases the results are free from influence of anisotropy unlike all<br/>other methods available; it also costs much less than conventional stress measurement methods (e.g. USBM, HI,<br/>HF) as neither special access nor specific drilling are required. However, the nature of this method makes it very<br/>sensitive to the test environment and the rock properties can influence the accuracy of prediction. In order to<br/>achieve a good quality result, extra attention and back calculation are required. <br/><br/>In this paper we discuss a successful case using ORA to predict the in situ stress. The test was done at a location<br/>where the ice sheet was present many thousands years ago. Without the knowledge of the stress condition, the<br/>client supplied information on sample depth below surface, regional structure, and nearby openings prior to the<br/>testing. The rock core was carefully selected and some trial tests were conducted to check the reliability of result.<br/>In the analysis stage, the resuH was examined using available information to eliminate the induced stress or any<br/>artificial stress generated during core extraction. After all procedures been undertaken, the result was compared<br/>with the in situ stress obtain by a different method by the client and the difference between two methods was<br/>confirmed to be minimal.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".