Using quantitative structural property relationships, chemical fate models, and the chemical partitioning space to investigate the potential for long range transport and bioaccumulation of complex halogenated chemical mixtures
Bibliographic record
Abstract
Some substances are mixtures of very large number of constituents which vary widely in their properties, and thus also in terms of their environmental fate and the hazard that they may pose to humans and the environment. Examples of such substances include industrial chemicals such as the chlorinated paraffins, technical pesticides such as toxaphene, and unintended combustion side products, such as mixed halogenated dibenzo-p-dioxins and dibenzofurans. Here we describe a simple graphical superposition method that could precede a more detailed hazard assessment for such substances. First, partitioning and degradation properties for each individual constituent of a mixture are estimated with high-throughput quantitative structure-property relationships. Placed in a chemical partitioning space, i.e. a coordinate system defined by two partitioning coefficients, the mixtures appear as 'clouds'. When model-derived hazard assessment metrics, such as the potential for bioaccumulation and long range transport, are superimposed on these clouds, the resulting maps identify the constituents with the highest value for a particular parameter and thus potentially the greatest hazard. The maps also indicate transparently how the potential for long range transport and bioaccumulation is dependent on structural attributes, such as chain length, and the degree and type of halogenation. In contrast to previous approaches, in which the mixture is represented by a single set of properties or those of a few selected constituents, the whole range of environmental fate behaviors displayed by the constituents of a mixture are being considered. The approach is illustrated with three sets of chemical substances.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".