COMPARING SORPTION BEHAVIOR OF PYRETHROIDS BETWEEN FORMULATED AND NATURAL SEDIMENTS
Bibliographic record
Abstract
Formulated sediments are recommended for use in toxicity tests to achieve standardized evaluations. However, the organic matter used in formulated sediments may differ qualitatively from that in natural sediments, which may lead to different chemical partition patterns and, hence, different toxicity effects. By deriving partition coefficients for organic carbon and dissolved organic carbon (K(OC) and K(DOC) , respectively) for eight pyrethroid insecticides from three formulated and five natural sediments, the authors characterized the differences between formulated and natural sediments in pesticide sorption. For all pyrethroids, the mean values of K(OC) and K(DOC) of formulated sediments were two to three, and three to 10 times smaller than those of natural sediments, respectively. The two formulated sediments containing α-cellulose or Manitoba peat showed significantly (p<0.0001) smaller K(OC) and K(DOC) values than natural sediments based on statistical analyses, while the difference was not significant for the formulated sediment containing New Brunswick peat. The K(OC) values were closely correlated (p<0.001) with soot carbon content, while the amount of carboxylic or phenolic functional groups may have affected K(DOC) . Therefore, the source and quality of organic matter are likely the most important factors in formulated sediments and must be standardized to provide consistency in sediment toxicity tests.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".