Strategies to Reduce Uncertainties from the Best Available Physicochemical Parameters Used for Modeling Novel Organophosphate Esters across Multimedia Environments
Bibliographic record
Abstract
Organophosphate esters (OPEs) raise growing environmental and human health concerns globally. However, numerous novel OPEs lack data on physicochemical properties, which are essential for assessing environmental fate, exposure, and risks. This study predicted water solubility ( S w ), vapor pressure ( V p ), octanol–water partition coefficient ( K ow ), and octanol–air partition coefficient ( K oa ) at 25 °C for 46 novel OPEs by identifying optimal in silico tools and establishing prediction strategies based on molecular weights (MWs). Prediction discrepancies between in silico tools increased with MWs and structural complexity. Method evaluations for compounds with MWs > 450 g/mol suggest that COSMOtherm is advantageous in predicting S w and V p for alkyl-OPEs, while SPARC is better for predicting V p for aryl- and halogenated-OPEs. For compounds with MWs > 500 g/mol, COSMOtherm and SPARC are recommended for K ow and K oa prediction, respectively. For smaller OPEs, average values from the top three of COSMOtherm, SPARC, EPI Suite, and OPERA, ranked by validation on traditional flame retardants, are recommended. Using improper software could cause deviations in multimedia distribution and overall persistence in the environment by up to 83 and 350%, respectively. The present data and prediction strategy are useful to enhance the reliability of environmental fate, exposure, and risk assessments of various OPEs and emerging contaminants.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".