Optical imaging through non-transparent small aquatic creatures with angular-domain imaging
Bibliographic record
Abstract
When imaging through small aquatic creatures, scattered photons produce problems in image quality and resolution. Angular Domain Imaging (ADI) reduces scattered photons and improves the image quality and resolution. ADI is an imaging technique which utilizes the angular spectrum of photons to filter multiple-scattered photons and accept only photons with small angular deviation from their original trajectory. Advantages of the ADI technique are that it is insensitive to wavelength and the sources are not required to be high optical quality, coherent, or pulsed, as with OCT or time domain. Our target is to image a small species called Branchiostoma lanceolatum, a lancet that is 5-8cm long and 5mm thick, by using ADI to remove the scattering in order to image internal structures. A laser illuminates the lancelet in a water-filled container and a spatiofrequency filter removes the scattered photons before the imager. Experimentally, a coherent Nd:Yag second harmonic (533nm) laser creates images but also optical interference occuring within the internal structures of the lancelet. Conversely, an incoherent broad-band white light source eliminates the structural interference effect; however, the wavelength variation of the scattering coefficient combined with the limitation of the image sensor's dynamic range limit the ability to distinguish the internal structures in many areas. Thus, an IR diode laser (780nm) is used to lower the scattering coefficient as compared to conventional visible light source and to diminish the interference effects due to its shorter coherence length.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".