Analysis of hydraulic and tracer response tests within moderately fractured rock based on a transition probability geostatistical approach
Bibliographic record
Abstract
A transition probability and Markov chain geostatistical approach is applied to synthesize the discrete permeability structure of moderately fractured rock. The approach can infuse either hard or subjective categorical information that is consistent with geological interpretations. The methodology is tested using data collected from the Moderately Fractured Rock (MFR) experiment area of the Underground Research Laboratory (URL) in southeastern Manitoba, Canada. Attributes pertaining to fracture location, frequency, and orientation along an array of boreholes intersecting the MFR experiment area, taken together with results from hydraulic response tests within packed‐off intervals along the boreholes, are used to produce conditional stochastic realizations of hydraulic conductivity and effective porosity. Using the generated hydraulic conductivity and porosity realizations, we compare predicted tracer concentrations to the results of measured breakthrough data in a stochastic framework. The results show that solute migration behavior in moderately fractured rock can be successfully characterized and reasonably predicted upon careful error analysis of the results obtained from the various medium realizations synthesized from the conditional categorical descriptions of the fractured crystalline rock.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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, 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".