Soft tissue facial resemblance in families and syndrome-affected individuals
Bibliographic record
Abstract
We investigated soft tissue facial resemblance among relatives with or without syndromes and among related and unrelated individuals diagnosed with the same syndrome. Using correlation coefficients, we compared facial landmark (i.e., three-dimensional coordinate) positions and measurements gained by photogrammetry in various combinations of normal and syndrome-affected individuals. There were fewer significant correlations for the three-dimensional coordinates and measurements between the normal parent-normal child pairs than for the normal sib pairs. There was no discernible pattern for the single measurements in the parent-child pairs, whereas all of the midline vertical measurements were significantly positively correlated in the normal sib pairs. Significant correlations were always positive in all sib comparisons, but ranged from negative to positive in all parent-child correlations. The shared environment of sibs was a possible explanation for their greater resemblance in comparison with parent-child pairs. We also had measurements from 11 subjects (related and unrelated) diagnosed with one of four syndromes, and we used these to compare individuals with the same syndrome by calculating correlation coefficients based on all available pairs of measurements. The highest significant positive correlations were found for related individuals with the same syndrome (0.72 to 0.83). Unrelated individuals with the same syndrome also had significant positive correlations, but they were lower (0.35 to 0.65). We therefore inferred that the genetic similarities between unrelated individuals with syndromes played a role in the resemblance between them, and that common genes and environment in related individuals further contributed to the high correlations found for them.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".