Compact, Field-Portable Smartphone Chiral Molecule Concentration Estimation System via Multi-sensor Computational Polarimetry
Bibliographic record
Abstract
In this paper, we present a compact, field-portable smartphone chiralmolecule concentration estimation system based on the principleof multi-sensor computational polarimetry. The presented systemwas designed as an attachment for a smartphone, thus leveragingthe computational power to achieve full autonomy and smallform factor, while greatly reducing the cost of the system. In addition,by leveraging Maul’s Law, the size and complexity of thepresented system can be greatly reduced, consisting of just fourstatic components: i) a diode laser source, ii) a linear polariser, iii)a cell for chiral solution, and iv) a linear analyser. Finally, the highmegapixel count of the smartphone camera is leveraged via multisensorcomputational polarimetry, where a multitude of measurementsby different sensors are made in a single acquisition to enhancethe estimation of the angle of linear polarisation, and therebyenhance the estimation of the concentration of chiral molecules insolution. Such a system can have potential for enabling low-cost,mobile chiral molecule concentration analysis, which would be wellsuitedfor a wide range of industrial and clinical applications wherefield testing or on-site testing is required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".