Bibliographic record
Abstract
This study introduces a Kernel Ridge Regression (KRR)-based microplastic quantification method, enhanced by active learning strategies, to predict the number of microplastic particles from aggregate weight measurements. Our approach utilizes a subset of experimentally quantified samples to train the KRR model, which then predicts microplastic counts in remaining samples, thus reducing labor and resource use. The active learning strategies help in determining the most informative samples, thereby minimizing the number of necessary training samples without compromising prediction accuracy.Multiple experimental datasets are used to evaluate the performance of three active learning querying strategies: passive sampling, maximum variance reduction, and residual regression. Numerical results show that passive sampling significantly outperforms traditional leave-one-out cross-validation method by selecting highly representative samples for training. Moreover, we explore the influence of incorporating additional features on the prediction accuracy, finding that the effectiveness of these features varies depending on their correlation with microplastic counts across different datasets. The study demonstrates the potential of combining KRR with active learning to efficiently and effectively quantify microplastics, which is crucial for microplastic pollution monitoring and management.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".