Link Between Enhanced Pore Surface Relaxivity and Mineral Alteration in Basalts
Bibliographic record
Abstract
Abstract Hydrothermal alteration significantly affects the mineralogical and geochemical composition of subsurface rocks. This research utilized a combination of low‐field time‐domain nuclear magnetic resonance, gas adsorption‐desorption isotherms, and scanning electron microscopy (SEM) with energy dispersive spectroscopy to characterize the pore systems of a range of flow top and flow interior basalt samples from Newberry Volcano drill core. A power‐law relationship between hydrothermal mineral alteration and magnetic susceptibility of pore‐facing minerals is revealed, suggesting a bulk method for quantifying degree of mineral alteration from core or wellbore data. Transverse relaxation time ( T 2 ) distributions, combined with gas adsorption‐based and SEM image‐based pore size distributions, yield faster T 2 relaxation or enhanced surface relaxivity values within sample micro‐ and macropores facing or lined with secondary minerals. This relationship can be used to evidence increased paramagnetic metal ion (e.g., Fe, Mn, Cr, Co, V) accessibility for subsurface engineering applications such as in situ carbon mineralization and critical minerals extraction.
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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 | 0.000 | 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) | 0.001 | 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".