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
Abstract This article presents an examination of non‐targeted analysis and suspect‐screening approaches in the identification and measurement of plastic additives and related contaminants. Four distinct case studies are highlighted, each demonstrating the utility of these techniques in various environmental contexts. The first study utilizes non‐targeted analysis for rapid fingerprinting of microplastics, illuminating sources of plastic pollution. The second investigation emphasizes the role of particle size in the leaching of plastic additives used in tires, with non‐targeted analysis revealing differences in chemical profiles based on particle size. The third case study introduces the novel technique of ‘smart suspect screening’, combining non‐targeted analysis and suspect screening to identify environmentally relevant compounds and their transformation products in the environment. The final study demonstrates the power of suspect screening in characterizing persistent, mobile, and toxic (PMT) plastic additives. Together, these case studies underscore the value of non‐targeted analysis and suspect screening in addressing plastic pollution as a complex chemical mixture problem, advancing our understanding of emerging contaminant threats from plastics. Rigorous data filtering, quality assurance, and reporting standards are emphasized to ensure the credibility and utility of the obtained results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.095 | 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 it