Characterization of soil micromorphology using X-ray computed tomography for predicting saturated hydraulic conductivity
Bibliographic record
Abstract
New and cross-disciplinary analytical methods are developed and tested for investigation of intact soil micromorphology, the results of which are applied in established fluid models that, classically, rely on indirect measurement of soil microstructure. The developed methods establish a set of requirements for legitimate application of X-ray Computed Tomography (CT) to intact soil, wherein the acquired CT digital image volumes were validated as representing soil structure through a comparison to classic thin section optical spectrum analytic techniques. The established requirements include: three dimensional (3D) post-acquisition processing of CT imagery, which is demonstrated to retain spatial relationships between discrete soil structures; an objective method of segmenting CT imagery into discrete structures that incorporates both the numerical digital number Hounsfield Unit (HU) and the spatial context; and, the use of 3D quantification methods for measurement of discrete soil structures. Application of the developed processing, segmentation and quantification methods to CT data is utilized in the fulfillment of a series of established and novel saturated hydraulic conductivity (Ks) models. A correlation between the laboratory measured Ks and the CT derived prediction of Ks, via the novel methodology presented, indicates that the direct quantification of soil micromorphology has potential application in future pedologic and fluid dynamics research.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".