Characterization of tire and road wear particles in experimental biota samples
Bibliographic record
Abstract
There is a growing interest in the development of reliable analytical methods for characterizing tire and road wear particles (TRWP). The current research extends the use of single particle analysis techniques to various experimental biota samples. TRWP and cryogenically milled tire tread (CMTT) were identified using a weight of evidence framework including density separation, optical microscopy, and chemical mapping (scanning electron microscopy coupled with energy dispersive X-ray spectroscopy). Our techniques successfully identified CMTT particles in laboratory earthworms exposed to soil spiked with CMTT. A river biota sample (bivalves) collected from the Seine with no detectable TRWP was spiked with road dust containing TRWP. Particle identification was performed after a biota digestion protocol and density separation of particles > 1.5 g/cm 3 and < 2.2 g/cm 3 which resulted in sufficient TRWP for identification and characterization. The average TRWP particle size from the road dust spiked biota sample was 126 μm by number and 220 μm by volume (range: 9 –572 μm). The size distribution overlay of TRWP identified from spiked biota were consistent with TRWP identified from the original road dust sample suggesting that the current method for biota digestion, dual density separation, and TRWP characterization is feasible for similar 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".