Biometric Morphing
Bibliographic record
Abstract
The results of facial surgery are intuitively judged in terms of the visible changes in facial features or proportions. However, describing these morphologic outcomes objectively remains a challenge. Biometric morphing addresses this issue by merging statistical shape analysis and image processing. This study describes the implementation of biometric morphing in describing the average morphologic result of facial surgery. The biometric morphing protocol was applied to pre- and postoperative images of the following: (1) 40 dorsal hump reduction rhinoplasties and (2) 20 unilateral enophthalmos repairs. Pre- and postoperative average images (average morphs) were generated. The average morphs provided an objective rendering of nasal and periorbital morphology, which summarized the average features and extent of deformity in a population of patients. Subtle alterations in morphology after surgery, which would otherwise be difficult to identify or demonstrate, were clearly illustrated. Biometric morphing is an effective instrument for describing average facial morphology in a population of patients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".