Beta Testing an AI-Based Physical Analysis Technology for Microplastic Quantification and Characterization
Bibliographic record
Abstract
Microplastic pollution is accumulating at alarming rates in the natural environment. New and innovative technologies are needed to help understand the gravity of the global microplastic pollution. In this study, a portable artificial intelligence system using image capture and analysis technology was beta tested to determine its suitability for microplastic quantification and characterization. Many factors were examined, including quantity, colour, shape and appearance (i.e., fragment, pellet, and film), and environmentally simulated (i.e., weathered and humic acid soaked). These were all factors considered. The beta prototype showed a pronounced aptitude for microplastic detection with a clean microplastic detection accuracy of 89% and an environmentally simulated microplastic detection accuracy of 77%. The beta prototype was compact, easy to use, and provided extensive information about the samples through its machine learning algorithm. The beta prototype would be well-suited for both scientific research and citizen science and is ideal for larger (≥0.5 mm) and lighter-coloured microplastic characterization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".