Automated resolution independent method for comparing in vivo and dry trabecular bone
Bibliographic record
Abstract
Abstract Objectives Variation in human trabecular bone morphology can be linked to habitual behavior, but it is difficult to investigate in vivo due to the radiation required at high resolution. Consequently, functional interpretations of trabecular morphology remain inferential. Here we introduce a method to link low‐ and high‐resolution CT data from dry and fresh bone, enabling bone functional adaptation to be studied in vivo and results compared to the fossil and archaeological record. Materials and methods We examine 51 human dry bone distal tibiae from Nile Valley and UK and two pig tibiae containing soft tissues. We compare low‐resolution peripheral quantitative computed tomography (pQCT) parameters and high‐resolution micro CT (μCT) in homologous single slices at 4% bone length and compare results to our novel Bone Ratio Predictor (BRP) method. Results Regression slopes between linear attenuation coefficients of low‐resolution pQCT images and bone area/total area (BA/TA) of high‐resolution μCT scans differ substantially between geographical subsamples, presumably due to diagenesis. BRP accurately predicts BA/TA (R2= .97) and eliminates the geographic clustering. BRP accurately estimates BA/TA in pigs containing soft tissues (R2= 0.98) without requiring knowledge of true density or phantom calibration of the scans. Discussion BRP allows automated comparison of image data from different image modalities (pQCT, μCT) using different energy settings, in archeological bone and wet specimens. The method enables low‐resolution data generated in vivo to be compared with the fossil and archaeological record. Such experimental approaches would substantially improve behavioral inferences based on trabecular bone microstructure.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".