Using Magnetic Resonance Imaging and Petrographic Techniques to Understand the Textural Attributes and Porosity Distribution in <i>Macaronichnus</i> -Burrowed Sandstone
Bibliographic record
Abstract
Abstract Magnetic resonance images are paired with petrographic data to evaluate the textural characteristics of rocks dominated by Macaronichnus segregatis, a trace fossil that is commonly associated with rocks deposited in shallow, marginal marine sedimentary environments. MRI techniques used revealed the three-dimensional geometry of the trace fossil. Burrows are typically horizontal and in plan view range between straight, sinuous, meandering, and spiral geometries. Changes in burrow morphology may be related to population density and patchy resource distribution. The pairing of MRI and petrographic data helped map the distribution of porosity in the burrowed rock. Because MRI images represent complex composites of nuclear spin density and MR relaxation times, each of which is related to pore size, stronger MR signals must be calibrated to known porous zones by integrating petrographic data with MR data. The complex distribution of porosity and its relationship to the matrix show that this fabric represents a dual porosity-permeability system and may affect the resource (reservoir or aquifer) quality of similarly burrowed sedimentary rocks. Future research should elaborate upon the porosity-permeability model for this and similar fabrics.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".