Development and Exploration of an Organic Contaminant Fate Model Using Poly-Parameter Linear Free Energy Relationships
Bibliographic record
Abstract
Octanol-based partitioning relationships, referred to as single-parameter linear free energy relationships (SP-LFERS), are often criticized for their limited applicability to polar organic substances. Therefore, SP-LFERS describing environmental phase partitioning in CoZMo-POP2, a dynamic multimedia chemical fate model, are replaced with poly-parameter linear free energy relationships (PP-LFERS) which describe temperature-dependent partitioning as the linear sum of various specific and nonspecific molecular interactions. A data set of chemicals with available solute descriptors, which quantify these molecular interactions, is compiled from the literature and, together with a data set of hypothetical chemicals, used to investigate the differences in the predictions of SP-LFER- and PP-LFER-based model in relative and absolute terms for three different emission scenarios. Model outputs are manipulated to allow the results to be displayed as a function of log K(AW) and log K(OA). Whereas the primary environmental fate is similar in both models, differences arise mostly in the environmental phases which contain only a small fraction of chemical. Larger differences in model results occur either because a difference in the predicted partitioning between water and organic matter affects the extent of soil-water runoff, or because differences in gas-particle partitioning affectthe relative deposition to aqueous and forested surfaces. The two models showed smaller differences for degradable chemicals than for chemicals assumed to be perfectly persistent Overall, however, the absolute differences between the model results are relatively small in comparison to the precision generally associated with model parametrization. Accordingly, we suggest that the quality of the available chemical input parameters should decide whether a PP-LFER model is preferable over a SP-LFER model. The PP-LFER model is further used to evaluate the effects of various molecular interactions on chemical fate, and the solute descriptor associated with van der Waals dispersive interactions is found to have the most pronounced effect on the environmental distribution of chemicals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".