The evolution and future of environmental partition coefficients
Bibliographic record
Abstract
Partition or distribution coefficients (and increasingly referred to as partition ratios) are widely used in environmental science to relate the concentration of a chemical solute in one phase to that in a second phase between which equilibrium applies or is approached. The solutes include organic and inorganic substances; the focus of this paper being on the former. The phases of interest include air, water, soils, sediments, aerosols, and biotic phases, such as lipids, blood, and various tissues. Availability of reliable partition coefficients for contaminants is essential for regulatory and scientific purposes, the general aim being to understand and predict the distribution of the substances in multimedia environmental and biological systems. The history of partition coefficients is reviewed, followed by a brief outline of their theoretical basis and a discussion of methods for determining partition coefficients both empirically and using a variety of predictive methods. It is suggested that ultimately a combination of empirical measurements, quantitative structure–property relationships, and computationally intensive quantum chemical molecular modeling techniques is required to provide accurate data for the large and increasing number of chemicals of commerce that may enter the environment.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".