PlasticNet: Deep Learning for Automatic Microplastic Recognition via FT-IR Spectroscopy
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
The recognition of microplastics (MPs) in environmental samples via FT-IR is challenging due to a plethora of factors can lead to significant variances in measured spectra. Conventional library search approaches compare the observed spectrum with spectra in reference libraries, which will lead to errors due the variance in spectra. Motivated to tackle this challenge, this study explores the feasibility of leveraging deep learning for automatic MP recognition via FT-IR spectroscopy. More specifically, a deep convolution neural network (CNN) architecture, referred to here as PlasticNet, is introduced for the purpose of automatic MP recognition. PlasticNet was trained on a large corpus of FT-IR spectra of different plastic types in order to learn discriminative spectral features characterizing each plastic type. Experimental results showed that PlasticNet was capable of recognizing between MPs in an effective way and at a faster speed compared with libary search.
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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