Towards Raman Automation for Microplastics: Developing Strategies for Particle Adhesion and Filter Subsampling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Automation and subsampling have been proposed as solutions to reduce the time required to quantify and characterize microplastics in samples using spectroscopy. However, there are methodological dilemmas associated with automation that are preventing its widespread implementation including ensuring particles stay adhered to the filter during filter mapping and developing an appropriate subsampling strategy to reduce the time needed for analysis. We provide a solution to the particle adherence issue by applying Skin Tac, a non-polymeric permeable adhesive that allows microplastic particles to adhere to the filter without having their Raman signal masked by the adhesive. We also explore different subsampling strategies to help inform how to take a representative subsample. Based on the particle distributions observed on filters, we determined that assuming a homogenous particle distribution is inappropriate and can lead to over- and under-estimations of extrapolated particle counts. Instead, we provide recommendations for future studies that wish to subsample to increase the throughput of samples for spectroscopic analysis.
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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 it