Spectro-angular mapping of localized gold inclusions in Intralipid phantoms
Bibliographic record
Abstract
We have applied an interstitial radiance-based technique based on a spectro-angular mapping approach to the identification and angular localization of 250-nm and 5-nm Au nanoparticle-based inclusions and non-scattering (water only) inclusions in the Intralipid-1% liquid phantom. A combination of the point radiance spectroscopy and white light spectroscopy was used to measure angular resolved light distribution in 450-900 nm spectral range in Intralipid-1% with and without localized inclusions. Characteristic spectro-angular snapshots of the liquid phantom alone and with the localized inclusions were obtained. For liquid phantoms without inclusions, the snapshots demonstrate wavelength dependent light distribution inside the turbid medium. For liquid phantoms with gold inclusions, the approach allows to isolate the spectroscopic signatures of the inclusions from the background, identify locations of the inclusions in the angular domain and show how a presence of water in the inclusion affects spectral identification and angular localization of the target. For liquid phantoms with water-based inclusions, an ability of the inclusion to enhance photon density above reference values and angular dependent signatures were demonstrated. The technique is seen as a potential tool in prostate treatment and diagnostics with gold nanoparticles.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".