Explorations in LEXT Image and Profile Capture for Dental Enamel Surface Morphology
Bibliographic record
Abstract
Abstract Bioarchaeology as a field of study can contribute important insights to our understanding of how stress-related phenomena experienced in childhood influence later life conditions. One area that is especially effective is looking at the dental enamel surface microstructures reflecting patterns of growth and growth disruption. Since dental enamel grows incrementally, and because it does not remodel once formed, a record of growth disruption (formed during childhood) is preserved for the rest of an individuals' life. Enamel surface defects are commonly observed macroscopically as enamel hypoplasia. However, this method does not capture the smaller defects reflecting a disruption in only a few of the growth lines visible on the tooth surface. Previous approaches to the assessment of these structures have included scanning electron microscopes and polarized light microscopes with photomontaging and z-stacking capacity. This paper presents the application of the Olympus LEXT 3D Laser Measuring Microscope OLS4000 and Olympus LEXT analytical software to capture and examine dental enamel surface microstructures. The use of the LEXT for these purposes is critically assessed, and the strengths and challenges discussed. Recommendations are made for future application of this instrument to bioarchaeological research.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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".