On estimating the directionality distribution in pedicle trabecular bone from micro-CT images
Bibliographic record
Abstract
Our interest in the trabecular alignment within bone stems from the need to better understand the manner in which it can affect ultrasound propagation, particularly in pedicles. Within long bones it is well established that trabecular structures are aligned in an organized manner associated with the direction of load distribution; however, for smaller bones there are limited alignment studies. To investigate the directionality distribution in a quantitative manner we used a micro-CT to obtain three-dimensional (3D) structural data and developed analytical methods based on the special properties of Gabor filters. Implementation of these techniques has been developed and tested on a variety of simulated images as well as on 3D structures whose geometry is well-defined. To test the use of this technique we compared the results obtained on vertebral body trabecular bone with visual directionality and previous measurements by others. The method has been applied to six human pedicle samples in two orthogonal planes with results that provide reasonable proof-of-principle evidence that the method is well suited for estimating the directionality distribution within pedicle bones.
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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 | 0.002 |
| 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) | 0.001 | 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".