Screening of polymer types and chemical weathering in macro- and meso-plastics found on lake and river beaches using a combined chemometric approach
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
In the environment, synthetic polymers, commonly known as "plastics", are well-known to undergo various chemical weathering processes, which modify their surface chemistry by introducing new functional groups. Such changes are important to monitor, as they can severely influence the toxicity caused by plastic debris. Therefore, in this study, two chemometric models are proposed to accelerate the chemical classification of macro- and meso-plastics found in the environment. For this purpose, principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied on preprocessed infrared spectra of 83 plastic fragments found on public lake and river beaches. HCA associated all beach samples with a known plastic, whereas PCA enabled the association of only 39.8% (33 out of 83) of the beach samples with a known plastic. However, both techniques agreed on 93.9% of the samples identified. According to PCA and HCA results, polypropylene and polyethylene were the most frequently identified polymers in the samples. PCA turned out to be a very promising tool for fast screening of weathered plastics, since the distance of samples from the polypropylene cluster in the PCA plot was correlated with weathering. This was later confirmed by employing other characterization techniques such as micro-Raman, X-ray photoelectron spectroscopy and scanning electron microscopy. Finally, future experiments should focus on the applicability of the proposed combined chemometric approach for very small microplastics (<100 μm), as they have more important effects than larger plastics on aquatic ecosystems.
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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 | 0.001 | 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