Groundwater Assessment in a Coal Measures Sequence Using Borehole Magnetic Resonance
Bibliographic record
Abstract
Hydraulic behaviour of an aquifer is defined in terms of the volumes of water present, both producible and not (specific yield and specific retention), and the productivity of the water (hydraulic conductivity). These parameters are typically evaluated using pumping tests, which provide zonal average properties, or more rarely on core samples, which provide discrete point measurements. Both methods can be costly and time-consuming, potentially limiting the amount of characterisation that can be conducted on a given project, and a significant measurement scale difference exists between the two.Borehole magnetic resonance has been applied in the oil and gas industry for the evaluation of bound and free fluid volumes, analogous to specific retention and specific yield, and permeability, analogous to hydraulic conductivity, for over twenty years. These quantities are evaluated continuously, allowing for cost-effective characterisation, and at a measurement scale that is intermediate between that of core and pumping tests, providing a convenient framework for the integration of all measurements.The role of borehole magnetic resonance measurements in hydrogeological characterisation is illustrated as part of a larger hydrogeological study of a coal measures unit and associated overburden. Borehole magnetic resonance has been used for aquifer and aquitard identification, and to provide continuous estimates of hydraulic properties. These results have been compared and reconciled with pumping test and core data, considering the scale differences between measurements. Finally, an integrated hydrogeological description of the target rock units has been developed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".