Correlating environmental partitioning properties of organic compounds: The three solubility approach
Bibliographic record
Abstract
Abstract It is suggested that in addition to correlating the environmental partitioning characteristics of chemicals as partition coefficients, it is also valuable to correlate them as solubilities or pseudo-solubilities. These solubilities are essentially convenient, readily understood, and in many cases, measurable expressions of single-phase activity coefficients. To illustrate this approach, a novel, three solubility, quantitative structure-property relationships (or QSAR) approach is described for correlating the physico-chemical parameters in which the solubilities or pseudo-solubilities of individual chemicals in the liquid or super-cooled liquid state, both individually and as homologous series, are compiled and correlated as a function of temperature in the three primary media of air, water, and octanol and possibly in other relevant media. These quantities, which are deduced from measured partition coefficients, solubilities, and vapor pressures, comprise a consistent data set that can be used to estimate a variety of environmentally relevant partition coefficients. The approach is demonstrated in detail for the chlorobenzenes and in a preliminary fashion for a variety of persistent and hydrophobic substances. The merits of this approach as a supplement to the conventional use of partition coefficients are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".