Application of micro‐FTIR mapping and SEM to study compositional heterogeneity of siltstones: Example from the Late Devonian–Early Mississippian Middle Bakken Member
Bibliographic record
Abstract
This paper explores the applicability of micro-FTIR mapping to study heterogeneity of organic matter-lean siltstones. Closely spaced samples of Late Devonian dolomitic siltstones of the Middle Bakken Member were analysed with micro-FTIR, powder X-ray diffraction, and scanning electron microscopy (SEM) to explore the distribution and chemical properties of organic matter (OM), muscovite/feldspar/clay group, carbonates, and quartz, and their influence on porosity and permeability of these rocks. Our results show that quartz is the dominant component of the samples, and the main mineralogical differences between the samples are reflected in the abundance of carbonate minerals. Organic matter content is usually far below 1 wt. % and dominantly represented by terrestrially derived vitrinite and inertinite. Micro-FTIR mapping demonstrates that the more spatially connected quartz and muscovite/feldspar/clays become, the larger permeability in the rock develops, and these correlations are especially strong for planes parallel to bedding. In contrast, carbonate connectivity shows a strong negative correlation with permeability. No correlations between connectivity of components and porosity have been detected. These observations suggest that micro-FTIR not only can document compositional heterogeneity of siltstones, but also has potential to help understanding their permeability systems.
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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 | 0.000 |
| 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".