A method for measuring the three-dimensional orientation of cortical canals with implications for comparative analysis of bone microstructure in vertebrates
Bibliographic record
Abstract
The orientation of vascular canals in cortical bone can reveal information about the growth rate and loading environment of a bone. For example, in birds it has been proposed that a high proportion of circumferential canals (a laminar cortex) is related to fast growth or torsional loading related to active flight. In this paper we present a method to measure the three dimensional (3D) orientation of vascular canals. Image data are obtained from micro-CT and two angles are measured: phi, determining how longitudinal a canal is; and theta, determining whether a canal is radial or circumferential. This method can measure the orientation of each canal contained in the scanned images. Here we demonstrate the approach on two samples - a rat tibia and a hawk humerus. This method offers a direct (3D) method for quantifying features of canal orientation, such as the degree of laminarity, and can be applied easily and non-destructively to multiple species and bones. The growth and development of the cortical canal network and its impact on factors such as bone strength and bone quality remains relatively unexplored. Our method provides a new tool to examine the impact of the orientation of cortical bone canals on bone and explore the origins of cortical canals formed during modelling and remodeling. This method has applications in comparative bone biology, small animal models, and human bone studies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".