Solvent Substitution: An Analysis of Comprehensive Hazard Screening Indices
Bibliographic record
Abstract
The air index (ψ(i)(air)) of the PARIS II software (Environmental Protection Agency), the Indiana Relative Chemical Hazard Score (IRCHS), and the Final Hazard Score (FHS) used in the P2OASys system (Toxics Use Reduction Institute) are comprehensive hazard screening indices that can be used in solvent substitution. The objective of this study was to evaluate these indices using a list of 67 commonly used or recommended solvents. The indices ψ(i)(air), IRCHS and FHS were calculated considering 9, 13, and 33 parameters, respectively, that summarized health and safety hazards, and environmental impacts. Correlation and sensitivity analyses were performed. The vapor hazard ratio (VHR) was used as a reference point. Two good correlations were found: (1) between VHR and ψ(i)(air) (ρ = 0.84), (2) and between IRCHS and FHS (ρ = 0.81). Values of sensitivity ratios above 0.2 were found with ψ(i)(air) (4 of 9 parameters) and IRCHS (3 of 13 parameters), but not with FHS. Overall, the three indices exhibited important differences in the way they integrate key substitution factors, such as volatility, occupational exposure limit, skin exposure, flammability, carcinogenicity, photochemical oxidation potential, atmospheric global effects, and environmental terrestrial and aquatic effects. These differences can result in different choices of alternatives between indices, including the VHR. IRCHS and FHS are the most comprehensive indices but are very tedious and complex to use and lack sensitivity to several solvent-specific parameters. The index ψ(i)(air) is simpler to calculate but does not cover some parameters important to solvents. There is presently no suitably comprehensive tool available for the substitution of solvents. A two-tier approach for the selection of solvents is recommended to avoid errors that could be made using only a global index or the consideration of the simple VHR. As a first tier, one would eliminate solvent candidates having crucial impacts. As a second tier, other parameters would be considered, with emphasis on the VHR.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 0.000 |
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 teacher head, 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".