A comparison of 2D vs. 3D thresholding of X-ray CT imagery
Bibliographic record
Abstract
Non-destructive soil constituent analysis has advanced from resin impregnation of intact samples to the utilization of non-destructive imaging devices (i.e., CT scanners) for 3D composition. A difficulty of CT scanning is the finite resolution of such devices and the resulting non-definitive boundaries. Isolation of voxels that contain a single constituent (i.e., low variance voxels) allows for improved segregation of 3D data by histogram thresholding. The results from proposed 2D and 3D low variance voxel segmentation techniques were compared to establish whether or not 3D consideration should be used when analyzing CT and intact soil columns. It was determined that 2D processing in single orientation and as a multiplicative orthogonal process produced dissimilar results to 3D processed data. The authors encourage further exploration of 3D investigation in soil science, particularly related to soil composition and arrangement. Key words: Computed tomography, threshold, segmentation, soil, 3D
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".