Technical note: Comparing dental topography software using platyrrhine molars
Bibliographic record
Abstract
OBJECTIVES: There remain many idiosyncrasies among the values calculated for varying dental topography metrics arising from differences in software preferences among research groups. The aim of this work is to compare and provide potential conversion formulae for dental topography metrics calculated using differing software platforms. METHODS: Three software packages: ArcGIS, Surfer Manipulator, and molaR were used to calculate orientation patch count rotated (OPCR), Dirichlet normal energy (DNE), occlusal relief (OR), slope (m), and angularity (a) on platyrrhine second upper molars. Values derived from the various software packages were compared for distributional consistency and correlation. Where appropriate, formulae for conversion between like measures calculated on different software platforms were developed. RESULTS: When compared with the same measurement across software, OPCR, OR, and slope were all highly correlated. However, only OR demonstrated distributional consistency (i.e., nearly consistent mean, median, max, and min). Slope and OPCR were both higher when calculated by molaR as compared to Surfer Manipulator and ArcGIS calculations, conversion formulae are provided for these measures. DNE is only weakly correlated with angularity; but is correlated with orientation patch count across taxa. DISCUSSION: We explore why there is variation in the dental topography values calculated among the various software packages. The conversion formulae provided in this work will make possible direct comparisons among studies conducted across multiple research groups.
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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 | 0.026 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".