Bone microstructure: quantifying bone vascular orientation
Bibliographic record
Abstract
Bone microstructure often preserves a temporal record of the life history of the animal to which it belongs. Previously used bone microstructure metrics to differentiate between primary bone types are reviewed and tested with a broad sample of bone types. Two new metrics, the radial index and the longitudinal index, are developed to quantitatively differentiate bone types based on bone vascular orientation in three dimensions. All previously used metrics described the bone microstructure in a nonlinear pattern and were unable to separate bone types satisfactorily. The radial index and longitudinal index effectively differentiated bone types and described bone microstructure within a linear continuum. The continuous nature of the range of vascular orientation in bone microstructure necessitates a quantitative approach rather than the commonly used qualitative classifications. The radial index and longitudinal index, which objectively detect small differences in vascular orientation in three dimensions, are therefore preferable to other metrics for inter- and intra-specific comparisons of bone microstructure. These metrics offer novel methods to facilitate examinations of the relationship between primary bone type and ontogeny, biomechanics, and phylogeny.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".