Usefulness of core logging for the identification of conductive fractures in bedrock
Bibliographic record
Abstract
To characterize conductive fracture networks, geologists use judgement to categorize their observations of geological features into sets. To test this judgement, we propose mathematical models which relate, via parameters, tallies of sets of observations in core to transmissivity measurements made in boreholes. We show that if these models are applied to aquifers in which groundwater flow is predominantly horizontal, the major sources of error are the misalignment of core relative to hydraulic test intervals and the erroneous categorization of observations. We tallied up (1) core observations that are categorized on the basis of a descriptive code given at time of drilling and (2) breaks in core that were later categorized on the basis of their “probability of being permeable,” and we use these tallies to find least squares parameter estimates from the models applied to a set of measured transmissivities. We evaluated the success of each method to distinguish the most transmissive fractures from the least from the goodness of fits as well as from the consistency of the parameter estimates with the understood hydrogeologic role of the constituent members of each set. It was found that highly transmissive fractures in bedrock could be identified by inspection of core and that the skilled judgment used by geologists for this purpose was better encapsulated in permeability rankings than in descriptive codes and written comments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".