Validation of the New Interpretation of Gerasimov's Nasal Projection Method for Forensic Facial Approximation Using <scp>CT</scp> Data<sup>,</sup>
Bibliographic record
Abstract
The most common method to predict nasal projection for forensic facial approximation is Gerasimov's two-tangent method. Ullrich H, Stephan CN (J Forensic Sci, 2011; 56: 470) argued that the method has not being properly implemented and a revised interpretation was proposed. The aim of this study was to compare the accuracy of both versions using a sample of 66 postmortem cranial CT data. The true nasal tip was defined using pronasale and nasal spine line, as it was not originally specified by Gerasimov. The original guidelines were found to be highly inaccurate with the position of the nasal tip being overestimated by c. 2 cm. Despite the revised interpretation consistently resulting in smaller distance from true nasal tip, the method was not statistically accurate (p > 0.05) in positioning the tip of the nose (absolute distance >5 mm). These results support that Gerasimov's method was not properly performed, and Ullrich H, Stephan CN (J Forensic Sci, 2011; 56: 470) interpretation should be used instead.
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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 | 0.029 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".