Angular domain image detectability with changing turbid medium scattering coefficients
Bibliographic record
Abstract
Optical tomography within highly scattering media has usually employed coherence domain and time domain imaging, which observe the shortest path photons over the dominant randomly scattered background light. An alternative, Angular Domain Imaging, employs micromachined collimators which detect photons within a small angle of the aligned laser light source. These angular filters consist of micromachined silicon collimator channels 51 micron wide by 10 mm long on 102 micron spacing giving an acceptance angle of 0.29 degrees at a CCD detector. Phantom test objects were observed in turbid mediums ranging from 1 to 5 cm thick at effective scattered to ballistic ratios from 1:1 to greater than 100,000,000:1. Simple line and space test objects detection limits are set by detector pixel size not collimator hole spacing. Restricting the light emission to only the collimating array hole area provides increased detectability by reducing the amount of scattered light background. This is best done using cylindrical spherical cylindrical lens beam expanders/shrinkers to create a wide line of light of small thickness aligned to the collimator array. As object locations within the medium are moved closer to the detector/collimator, image detectability appears to depend on the scattering ratio after the test object rather than the total medium scattering. Hence, objects located closer to the detector than the middle of the medium are observed at a much higher scattering levels than those nearer the light source.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".