A Bayesian regression analysis of in situ stress using overcoring data
Bibliographic record
Abstract
Abstract Characterising the state of in situ stress at a target depth is crucial for all underground engineering projects. Consequently, on critical projects such as nuclear waste repositories extensive campaigns are implemented with the goal of estimating the in situ stress state. These campaigns often comprise both direct measurement and indirect estimation methods, but the data obtained across a project volume may exhibit significant variability. This poses significant challenges in both quantifying the variability and uncertainty of in situ stress, and determining the stress state to be used for design purposes. It is often assumed that the state of in situ stress increases linearly with depth, and thus linear regression of principal stress magnitude against depth are often found in the literature. As such methods not honouring the tensorial nature of stress are, strictly, incorrect. To show how this limitation may be overcome, here we present a Bayesian regression analysis of in situ stress with depth that uses the Cartesian stress tensor. The analysis is performed using over 100 overcoring data obtained at the SKB Forsmark site in Sweden. A comparison between the customary and Bayesian approaches is presented, which shows the superiority of the tensorial technique.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".