Technical note: A landmark‐based approach to the study of the ear ossicles using ultra‐high‐resolution X‐ray computed tomography data
Bibliographic record
Abstract
Previous study of the ear ossicles in Primates has demonstrated that they vary on both functional and phylogenetic bases. Such studies have generally employed two-dimensional linear measurements rather than three-dimensional data. The availability of Ultra- high-resolution X-ray computed tomography (UhrCT) has made it possible to accurately image the ossicles so that broadly accepted methodologies for acquiring and studying morphometric data can be applied. Using UhrCT data also allows for the ossicular chain to be studied in anatomical position, so that it is possible to consider the spatial and size relationships of all three bones. One issue impeding the morphometric study of the ear ossicles is a lack of broadly recognized landmarks. Distinguishing landmarks on the ossicles is difficult in part because there are only two areas of articulation in the ossicular chain, one of which (the malleus/incus articulation) has a complex three-dimensional form. A measurement error study is presented demonstrating that a suite of 16 landmarks can be precisely located on reconstructions of the ossicles from UhrCT data. Estimates of measurement error showed that most landmarks were highly replicable, with an average CV for associated interlandmark distances of less than 3%. The positions of these landmarks are chosen to reflect not only the overall shape of the bones in the chain and their relative positions, but also functional parameters. This study should provide a basis for further examination of the smallest bones in the body in three dimensions.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".