Validation using 3D CT of the new interpretation of Gerasimov’s nasal projection method for forensic facial approximation
Bibliographic record
Abstract
Approximating the facial features for forensic facial approximation is challenging, especially the nose. Numerous methods have been published to position the tip of the nose in profile with variable results. Gerasimov’s two-tangent method is the most commonly used. However, a recent article published by Ullrich and Stephan (2011) states that the method was not properly performed and provides new guidelines. This research used a sample of CT scans from a Denmark population (N=66) to determined which of Gerasimov’s literal translation or Ullrich and Stephan’s (2011) new version of the two-tangent method is the most accurate. A combination of the two methods was also evaluated to determine the effect of each tangent independently, and the effect of intraobserver error. It was determine that the new guidelines result in smaller mean difference but no method can accurately position the tip of the nose due to the lack of experience from the practitioner.
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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 | 0.021 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".