Unpacking Phthalates from Obscurity in the Environment
Bibliographic record
Abstract
Phthalates (PAEs) are a group of synthetic esters of phthalic acid compounds mostly used as plasticizers in plastic materials but are widely applied in most industries and products. As plasticizers in plastic materials, they are not chemically bound to the polymeric matrix and easily leach out. Logically, PAEs should be prevalent in the environment, but their prevalence, transport, fate, and effects have been largely unknown until recently. This has been attributed, inter alia, to a lack of standardized analytical procedures for identifying them in complex matrices. Nevertheless, current advancements in analytical techniques facilitate the understanding of PAEs in the environment. It is now known that they can potentially impact ecological and human health adversely, leading to their categorization as endocrine-disrupting chemicals, carcinogenic, and liver- and kidney-failure-causing agents, which has landed them among contaminants of emerging concern (CECs). Thus, this review article reports and discusses the developments and advancements in PAEs' standard analytical methods, facilitating their emergence from obscurity. It further explores the opportunities, challenges, and limits of their advancements.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".