Activity-Based Concept for Transport and Partitioning of Ionizing Organics
Bibliographic record
Abstract
Ionizing chemicals, including pesticides, pharmaceuticals, and personal care products, are care products, are widely used chemicals of commerce and have been detected in the environment in large numbers. These "ionics" are subject to a variety of processes, such as dissociation, ion trap, and electrical interactions with organic matter and biota. Conventional chemodynamic concepts and models designed to treat neutral compounds do not necessarily address these processes. A new system of equations, based on activity and analogous to the fugacity approach, is suggested to describe the fate of organic ionics. The total concentration of all molecule species in a bulk compartment is determined from the product of activity 'a' and a bulk activity capacity 'B'. The concentration ratio between compartments in equilibrium depends on the activity ratio and the capacity ratio. Changes in partitioning due to pH, ionic strength, and the ion trap effect are quantified. The calculation is illustrated for two pharmaceuticals, namely the monovalent acid ibuprofen and the monovalent base trimethoprim, in a multimedia lake system. Trimethoprim is neutral at high pH but ionized at low pH, while ibuprofen exhibits the opposite. The concentration ratios of air and biota to water are shown to depend on pH. The activity approach may be used to describe transport and partitioning of multivalent ionizable organic compounds and to build multimedia fate models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".