Quantification of the 3D Orientation of Vascular Canals in Cortical Bone
Bibliographic record
Abstract
The orientation of vascular canals in cortical bone is thought to reflect the loading experienced by a bone during the time when those canals were formed. This orientation is also thought to be related to the growth rate of cortical bone, creating different patterns such as longitudinal, laminar, or radial. The presence of these different patterns would then reflect faster or slower growth in that region of the cortex. Most of our understanding of vascular canal orientation comes from studies using histology: a method which relies on 2D sections, and which can only calculate the orientation of canals relative to the plane of sectioning. In this poster we present a new method, using micro computed tomography and custom developed software, to quantify the orientation of vascular canals in three dimensions. Using this method, two independent angles are measured: the orientation of the vascular canal relative to the long axis of the bone (phi), and relative to a plane of section perpendicular to the long axis (theta). These two angles together can be used to classify each vascular canal in the cortex into categories such as longitudinal, circumferential, or radial, or their oblique variant. This method is the first that can measure both theta and phi. Measuring these angles provides information on the processes that shape cortical bone microarchitecture, and provides the potential to extract biological information on the growth rate and loading of the bone. Disentangling the connection between growth rate, loading, and the organization of cortical porosity is a complex problem, and this method has the potential to help investigate and aid our understanding of these relationships through both natural and controlled experiments. Support or Funding Information Support for this research was provided by NSERC via a Discovery grant to DMLC and a post‐graduate scholarship to IVP.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".