Perception of different facial characteristics by Saudis
Bibliographic record
Abstract
The aim of the present study was to identify the most attractive lip prominence and lower face height in different facial profiles based on the perception of Saudi Arabian laypersons living in the western region of Saudi Arabia. Lateral photographs of 10 Saudi adult subjects with well proportioned faces were presented to a panel of orthodontists and general dentists to choose the most attractive profiles. Photographs were taken using a standardized method for all subjects and were edited and converted into negatives. A male and a female subject were selected as the supernormal sample. Four sets of normal profiles were generated for each of the supernormal subjects by manipulating lip prominence, chin position and lower face height and then presented randomly to lay people to rank each set of pictures in an order of attractiveness and to fill out a simple questionnaire. The percentages of the most acceptable facial features were calculated, ranked and compared using the chi square test (p<0.05). The most attractive lip prominence was the average (39.2%) in the orthognathic facial type, the protrusive (39.3%) in the prognathic facial type and the retrusive (56.5%) in the retrognathic facial type. The most attractive lower face height was the shortest (39.3%) in female. In male, however, the three lower face heights were almost the same with no clear preference. In conclusion, Saudis seem to prefer average but not protrusive lips in orthognathic faces, retrusive lips in the retrognathic faces and protrusive lips in prognathic faces. Minor changes in the lower face height seem less 15observable by lay people.
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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 | 0.001 |
| 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) | 0.002 | 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".