Quantitative 3D analysis of the canal network in cortical bone by micro‐computed tomography
Why this work is in the frame
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Bibliographic record
Abstract
Cortical bone is perforated by an interconnected network of porous canals that facilitate the distribution of neurovascular structures throughout the cortex. This network is an integral component of cortical microstructure and, therefore, undergoes continual change throughout life as the cortex is remodeled. To date, the investigation of cortical microstructure, including the canal network, has largely been limited to the two-dimensional (2D) realm due to methodological hurdles. Thanks to continuing improvements in scan resolution, micro-computed tomography (muCT) is the first nondestructive imaging technology capable of resolving cortical canals. Like its application to trabecular bone, muCT provides an efficient means of quantifying aspects of 3D architecture of the canal network. Our aim here is to introduce the use of muCT for this application by providing examples, discussing some of the parameters that can be acquired, and relating these to research applications. Although several parameters developed for the analysis of trabecular microstructure are suitable for the analysis of cortical porosity, the algorithm used to estimate connectivity is not. We adapt existing algorithms based on skeletonization for this task. We believe that 3D analysis of the dimensions and architecture of the canal network will provide novel information relevant to many aspects of bone biology. For example, parameters related to the size, spacing, and volume of the canals may be particularly useful for investigation of the mechanical properties of bone. Alternatively, parameters describing the 3D architecture of the canal network, such as connectivity between the canals, may provide a means of evaluating cumulative remodeling related change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it