How Many Chemicals in Commerce Have Been Analyzed in Environmental Media? A 50 Year Bibliometric Analysis
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Over the past 50 years, there has been a tremendous expansion in the measurement of chemical contaminants in environmental media. But how many chemicals have actually been determined, and do they represent a significant fraction of substances in commerce or of chemicals of concern? To address these questions, we conducted a bibliometric survey to identify what individual chemicals have been determined in environmental media and their trends over the past 50 years. The CAplus database of CAS, a Division of the American Chemical Society, was searched for indexing roles “analytical study” and “pollutant” yielding a final list of 19,776 CAS Registry Numbers (CASRNs). That list was then used to link the CASRNs to biological studies, yielding a data set of 9.251 × 10 6 total counts of the CASRNs over a 55 year period. About 14,150 CASRNs were substances on various priority lists or their close analogs and transformation products. The top 100 most reported CASRNs accounted for 34% of the data set, confirming previous studies showing a significant bias toward repeated measurements of the same substances due to regulatory needs and the challenges of determining new, previously unmeasured, compounds. Substances listed in the industrial chemical inventories of Europe, China, and the United States accounted for only about 5% of measured substances. However, pharmaceuticals and current use pesticides were widely measured accounting for 50–60% of total CASRN counts for the period 2000–2015.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.097 | 0.175 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".