Integrated computational imaging system for enhanced polarimetric measurements
Bibliographic record
Abstract
Polarimetry is a common technique used in chemistry for solution characterization and analysis, giving insight into the molecular structure of a solution measured through the rotation of linearly polarized light. This rotation is characterized by the Boits law. Without large optical path lengths, or high concentrations of solution, these optical rotations are typically very small, requiring elaborate and costly apparatuses. To ensure that the rotation measurements are accurate, these devices usually perform complex optical procedures or time-averaged point measurements to ensure that any intensity variation seen is a product of optical rotation and not from inherent noise sources in the system, such as sensor or shot noise. Time averaging is a lengthy process and rarely utilizes all of the information available on the sensor. To this end, we have developed a novel integrated, miniature, computational imaging system that enhances polarimetric measurements by taking advantage of the full spot size observed on an array detector. This computational imaging system is capable of using a single acquisition at unity gain to enhance the polarimetric measurements using a probabilistic framework, which accounts for inherent noise and optical characteristics in the acquisition process, to take advantage of spatial intensity relations. This approach is faster than time-averaging methods and can better account for any measurement uncertainties. In preliminary experiments, this system has produced comparably consistent measurements across multiple trials with the same chemical solution than time averaging techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".