Improving the Consistency of Joint Roughness Estimation in Drill Core with a Visual Guide
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Bibliographic record
Abstract
ABSTRACT: Describing joint roughness is a key step in rock mass characterization. Geotechnical logging of drill core is a common method for collecting discontinuity data that may be used to estimate joint roughness. This activity is commonly completed by practitioners with limited engineering geology or rock mechanics experience. Results within a single project or campaign may be poorly calibrated across practitioners. Inconsistent results between projects also occur as different methods may be employed without an understanding of how the semi-quantitative indexes and qualitative terms correlate with each other. We present a visual photographic reference tool that has been developed to improve the estimates of joint roughness when logging core. The tool includes correlations between JRC, Jr, and qualitative descriptors. Photographs capture the range of conditions that are commonly encountered across multiple rock types, rendering this tool applicable to a variety of different project settings.
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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 it