Computational laser intensity stabilisation for organic molecule concentration estimation in low-resource settings
Bibliographic record
Abstract
An ideal laser is a useful tool for the analysis of biological systems. In particular, the polarization property of lasers can allow for the concentration of important organic molecules in the human body, such as proteins, amino acids, lipids, and carbohydrates, to be estimated. However, lasers do not always work as intended and there can be effects such as mode hopping and thermal drift that can cause time-varying intensity fluctuations. The causes of these effects can be from the surrounding environment, where either an unstable current source is used or the temperature of the surrounding environment is not temporally stable. This intensity fluctuation can cause bias and error in typical organic molecule concentration estimation techniques. In a low-resource setting where cost must be limited and where environmental factors, like unregulated power supplies and temperature, cannot be controlled, the hardware required to correct for these intensity fluctuations can be prohibitive. We propose a method for computational laser intensity stabilisation that uses Bayesian state estimation to correct for the time-varying intensity fluctuations from electrical and thermal instabilities without the use of additional hardware. This method will allow for consistent intensities across all polarization measurements for accurate estimates of organic molecule concentrations.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".