Increasing the Accessibility for Characterizing Microplastics: Introducing New Application-Based and Spectral Libraries of Plastic Particles (SLoPP and SLoPP-E)
Why this work is in the frame
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Bibliographic record
Abstract
As smaller particle sizes are increasingly included in microplastic research, it is critical to chemically characterize microparticles to identify whether particles are indeed microplastics. To increase the accessibility of methods for characterizing microparticles via Raman spectroscopy, we created an application-based library of Raman spectroscopy parameters specific to microplastics based on color, morphology, and size. We also created two spectral libraries that are representative of microplastics found in environmental samples. Here, we present SLoPP, a spectral library of plastic particles, consisting of 148 reference spectra, including a diversity of polymer types, colors, and morphologies. To account for the effects of aging on microplastics and associated changes to Raman spectra, we present a spectral library of plastic particles aged in the environment (SLoPP-E). SLoPP-E includes 113 spectra, including a diversity of types, colors, and morphologies. The microplastics used to make SLoPP-E include environmental samples obtained across a range of matrices, geographies, and time. Our libraries increase the likelihood of spectral matching for a broad range of microplastics because our libraries include plastics containing a range of additives and pigments that are not generally included in commercial libraries. When used in combination with commercial libraries of over 24 000 spectra, 63% of the top 5 matches across all particles tested (product and environmental) are from SLoPP and SLoPP-E. These tools were developed to improve the accessibility of microplastics research in response to a growing and multidisciplinary field, as well as to enhance data quality and consistency.
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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 | 0.001 |
| 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