PhotoFate: A New Approach in Accounting for the Contribution of Indirect Photolysis of Pesticides and Pharmaceuticals in Surface Waters
Bibliographic record
Abstract
A new approach was developed to account for the contribution of indirect photolysis of pesticides and pharmaceuticals in which laboratory test conditions are similar to those prevalent in the aqueous environment. Rates of photolysis as a function of water composition were investigated for several aquatic contaminants. Using the laboratory-based test system, PhotoFate, the dependence of phototransformation rates on concentrations of natural water constituents that are radical producers and scavengers (nitrate, colored dissolved organic matter, bicarbonate) was studied. Mean half-lives of the model compounds in the presence of water constituents were compared to their direct photolysis half-lives to assess the contribution of photosensitized reactions to their fate in surface waters. Reactions mediated by .OH were predominant in waters with high nitrate concentrations. Colored dissolved organic matter (cDOM) acted mainly as a radiation filter and had a more important role in scavenging radicals than in their production. However, in low nitrate waters, the contribution of cDOM-derived reactive intermediates to the degradation of parent compounds became more apparent
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 | 0.001 | 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 | 0.000 | 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".