Characterization of X-ray CT Data to Assessment of Geomaterials
Bibliographic record
Abstract
X-ray CT image is a digital image composed of so called CT-value that is a preliminary output. This value is proportional to a material density. As far as the research on the application of X-ray CT in fields of engineering is concerned, not only qualitative study but also quantitative one are expected. And then, it is necessary to know the relationship between the CT-value and material property such as its density. In general, a proper processing technique produces alternative results by converting CT-value to the material density and by extracting spatial distribution of the density or air voids in geomaterials. There are three key issues for this purpose, which are calibration of CT-value, beam hardening effect, and partial volume effect. The purpose of this paper is to clarify the basic properties of X-ray CT data with respect to geomaterials. Here, artifacts due to aging levels of x-ray tube under different power voltage and size of the specimen are examined. The relationship between size of soil particles and that of resolution on X-ray CT is also clarified in order to evaluate the partial volume effect. Finally, a suggested test method in terms of the selected scanning conditions is proposed.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".