A New Digital Method for the Objective Comparison of Frontal Sinuses for Identification*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Use of the frontal sinuses for identification requires an objective method of comparison to meet Daubert standards. Christensen's application of Elliptical Fourier Analysis and Likelihood Ratios seems to be a viable solution for this problem. The proposed method draws upon this work and attempts to simplify its application. Variation between pairs of digitized sinus tracings was quantified by summing the difference between corresponding measurements taken from a fixed origin to the outer edge of the sinus outlines using Adobe Photoshop CS2. Same-skull and different-skull pairs were used to develop reference distributions from which the probability of unknown pairs coming from the same or a different individual was estimated. Error rates of 0% were achieved. Resulting correlation coefficients demonstrated inter-rater and test-retest reliability. Further refinement of the reference distributions and more rigorous testing of error rates should make this technique applicable to casework.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it