Bibliographic record
Abstract
OBJECTIVE: To determine the effect of altered lip line on attractiveness and to find preferred lip line for vertical face types in both genders. STUDY DESIGN: Cross-sectional analytical study. PLACE AND DURATION OF STUDY: The Aga Khan University Hospital, Karachi, from May to July 2009. METHODOLOGY: Photographs of two selected subjects were altered to produce three face types for the same individual with the aim of keeping the frame of the smile constant. Lip line was then altered for both the subjects as: both dentitions visible, upper incisors visible, upper incisors and 2 mm gum and 4 mm gum visible. The pictures were rated by different professionals for attractiveness. Descriptive statistics for the raters and multiple factor ANOVA was used to find the most attractive lip line. RESULTS: The total number of raters was 100 with the mean age of 30.3 ± 8 years. The alterations in the smile parameters produced statistically significant difference in the attractiveness of faces, whereas the perception difference was found to be insignificant amongst raters of different professions. Preferred lip line was the one showing only the upper incisors in dolico and mesofacial male and female genders whereas 2 mm gum show was preferred in brachyfacial subjects. CONCLUSION: The variability in lip line showed significant difference in the perceived attractiveness. Preferred lip lines as the one showing only the upper incisors in dolico and mesofacial male and female genders whereas 2 mm gum show was preferred in brachyfacial subjects.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".