Large area full-field optical coherence tomography using white light source
Bibliographic record
Abstract
Optical coherence tomography (OCT) is an emerging technology for high-resolution cross-sectional imaging of 3D structures. Not only could OCT extract the internal features of an object, but it could acquire the 3D profile of an object as well. Hence it has huge potentials for industrial applications. Owing to non-scanning along the X-Y axis, full-field OCT could be the simplest and most economic imaging system, especially for applications where the speed is critical. For an OCT system, the performance and cost basically depends on the light source being used. The broader the source bandwidth, the finer of the depth resolution that could be reached; the more power of the source, the better signal-to-noise ratio and the deeper of penetration the system achieves. A typical SLD (Superluminescent Diode) light source has a bandwidth of 15 nm and 10 mW optical power at a price around $6,000. However, a Halogen bulb having 50W power and 200nm bandwidth only costs less than $10. The design and implementation of a large-area, full-field OCT system using Halogen white-light source is described in the paper. The experimental results obtained from 3D shaping and multiple-layer tomographies are also presented.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".