An inverse relationship between typical alcohol consumption and facial symmetry detection ability in young women
Bibliographic record
Abstract
The relationship between monthly alcohol consumption over the past 6 months and facial symmetry perception ability was examined in young sober women with typical college-age drinking patterns. Facial symmetry detection performance was inversely related to typical monthly alcohol consumption, r (41) = -0.57, p < 0.001. Other variables that were predictive of facial symmetry detection included alcohol-related hangover and blackout frequency over the past 6 months, number of alcoholic drinks over the past week, early adolescent alcohol consumption and frequency of drug use. The relationship between alcohol use and symmetry detection could not be explained by individual differences in personality, family alcoholism history or other drug use. These findings suggest the possibility of a neurotoxic effect of alcohol on facial symmetry perception ability in female undergraduate students. As similar results did not emerge for a test of dot symmetry detection, the findings appear specific to facial symmetry. No previous studies have examined the effect of alcohol history on symmetry detection. The findings add to a growing literature indicating negative visuospatial effects of early alcohol use, and suggest the importance of further research examining alcohol and drug effects on sober facial perception in non-alcoholic populations.
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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 | 0.001 | 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) | 0.003 | 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".