Comparison of quasi‐spherical surfaces – application to corneal biometry
Bibliographic record
Abstract
In this study, the authors present two new techniques with their own particular advantages dedicated to the authentication of a person based on the three‐dimensional geometry of the cornea. A device known as corneal topographer is used for capturing the shape of each cornea. Until now only a few studies on corneal biometry have been conducted and they were limited only to the anterior surface. In this study, since the whole cornea is a tissue layered by two (anterior and posterior) surfaces, the authors propose to use both surfaces to characterise the corneal shape. The first proposed method consists of comparing coefficients from a spherical harmonics decomposition, and this allows to do a fast comparison that can be used to perform many‐to‐one comparisons. The second approach is based on the minimal residual volume between two corneas after a registration step, this geometry‐based method is more accurate but slower, and is thus used to perform one‐to‐one comparisons. A cascade fusion scheme is also proposed to benefit from the advantages of both methods. The authors’ study demonstrates that corneal shape could be used for biometry. The two proposed methods have been tested and validated on a dataset of 257 corneas.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".