Text Mining and Machine Learning-Based Data Extraction for Environmental Pollutants (PPDs and PPDs-Q): Machine Learning-Enabled Global Aqueous Concentration Compilation and Potential Biotic Risk Implications
Bibliographic record
Abstract
p-Phenylenediamine antioxidants (ppDs) and their quinone derivatives (ppDs-Q) are key additives in rubber products with strong toxicity, persistence, and increasing aquatic concentrations, though global data scarcity hinders risk assessment. Thisstudy addressed limitations of manual data extraction by using a python-based toolkit integrating ocR and Spacy neural networks to eficiently extract key information (concentrations,locations, media) from unstructured literature, compiling globaldata via Web of science, scopus, and pubMed. ppDs/ppDs-Q show significant concentration differences across global aquaticmedia: in artificial water, 6ppD in road runoff reaches 80019,000 ng/L (Seattle, UsA) and 907 ng/L (china's Greater Bay Area)with lower levels in wastewater effluents; in natural water, 6ppD-Q varies regionally (2003,500 ng/L in Us rivers, 290890 ng/lin Canadian rivers, 0.2611.3 ng/L in chinese rivers); snowmelt water shows high 6ppD.Q (19.0 g/L in seattle, 367 ng/L aver.age in Canadian cold cities,. species sensitivity follows patterns: salmoniformes are most sensitive to 6PPD/6PPD-Q (LC/EC5O <0.001 mg/L), followed by echinoderms/mollusks, with lower sensitivity in lower trophic organisms. Coupled analysis showsUS river 6PpD-Q exceeds ultra-sensitive organisms' thresholds, chinese rivers pose subchronic risks to benthic organisms,and widespread detection in human fluids indicates continuous exposure. This "ocR-neural network" framework resolvesmanual extraction bottienecks, provlding an extensible paradigm for other bolutants, wnle conclusons support regulationsecological protection,and risk management for PPDs.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".