Accuracy and repeatability of 3D Photogrammetry to digitally reconstruct bones
Bibliographic record
Abstract
Advances in computer hardware and software permit the reconstruction of physical objects digitally from digital camera images. Given the varying shapes and sizes of human bones, a comprehensive assessment is required to establish the accuracy of digital bone reconstructions from three-dimensional (3D) photogrammetry. Five human bones (femur, radius, scapula, vertebra, patella) were marked with pencil, to establish between 9 and 29 landmarks. The distances between landmarks were measured from the physical bones and digitized from 3D reconstructions. Images used for reconstructions were taken on two separate days, allowing for repeatability to be established. In comparison to physical measurements, the mean (±standard deviation) absolute differences were between 0.2±0.1mm and 0.4±0.2mm. The mean (±standard deviation) absolute differences between reconstructions were between 0.3±<0.1mm and 0.4±0.4mm. The 3D photogrammetry procedures described are accurate and repeatable, permitting quantitative analyses to be conducted from digital reconstructions. Moreover, 3D photogrammetry may be used to capture and preserve anatomical materials for anatomy education.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".