Quantitative magnetic resonance imaging methods for core analysis
Bibliographic record
Abstract
Abstract The majority of sedimentary rocks have significant paramagnetic impurities, which lead to magnetic resonance signal lifetimes too short to be detected by clinical magnetic resonance imaging (MRI) methods. Quantitative information is the ultimate goal for rock-core analysis. The SPRITE (single-point ramped imaging with T 1 enhancement) imaging technique has proven to be a very robust and flexible method for the study of a wide range of systems with short signal lifetimes. As a pure phase-encoding technique, SPRITE is largely immune to image distortions generated by susceptibility variations, chemical shift and paramagnetic impurities, unlike clinical magnetic resonance imaging methods. It enables systems with transverse lifetimes as short as tens of microseconds to be successfully visualized. Our experimental results show that most sedimentary rocks have a single exponential transverse magnetization decay for T * 2 , which suggests that quantitative imaging of local fluid content can be easily obtained. Some examples of MRI techniques are represented that reveal internal sedimentary characteristics and heterogeneity. In addition, the application of quantitative MRI techniques to examine flow mechanisms in rock cores is outlined.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".