Double‐Angling‐Subspace Enabled Laser‐Induced Fluorescence Method for Determining the Types and Mass Ratio of Marine Microplastics
Bibliographic record
Abstract
Currently, the laser-induced fluorescence method faces challenges in reliably determining the types and mass ratios of marine microplastics due to overlapped fluorescence spectra of different microplastics. To address this issue, this paper proposes a double-angling-subspace (DAS) method to differentiate the overlapped fluorescence spectra. The key idea is to span subspaces with vectors converted by known fluorescence spectra, followed by calculating the angle between vectors and subspaces. Specifically, it is found that the angle between the vectors converted from fluorescence spectra of unknown microplastics and their projections on the subspaces, as well as the angle between these vectors and the vectors spanning the subspaces, is indicative of microplastic types. The vector of an unknown microplastic belongs to the subspace spanned by the vectors converted by the known microplastics, and the mass ratios of unknown samples can be determined by analyzing the linear correlation between the vectors of both unknown and known microplastics. The reliability of the proposed DAS method is validated with real marine microplastic samples.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".