Dissipation of triclosan, triclocarban, carbamazepine and naproxen in agricultural soil following surface or sub-surface application of dewatered municipal biosolids
Bibliographic record
Abstract
In many jurisdictions land application of municipal biosolids is a valued source of nutrients for crop production. The practice must be managed to ensure that crops and adjacent water are not subject to contamination by pharmaceuticals or other organic contaminants. The broad spectrum antimicrobial agents triclosan (TCS) and triclocarban (TCC), the anti-epileptic drug carbamazepine (CBZ), and the nonsteroidal anti-inflammatory drug naproxen (NAP) are widely used and are carried in biosolids. In the present study, the effect of biosolids and depth of placement in the soil profile on the rates of TCS, TCC, CBZ, and NAP dissipation were evaluated under semi-field conditions. Aggregates of dewatered municipal biosolids (DMBs) supplemented with (14)C-labeled residues were applied either on the soil surface or in the subsurface of the soil profile, and incubated over several months under ambient outdoor conditions. The dissipation of TCS, TCC and NAP was significantly faster in sub-surface than surface applied biosolid aggregates. In contrast the dissipation rate for CBZ was the same in surface applied and incorporated aggregates. Overall, the present study has determined a significant effect of depth of placement on the dissipation rate of biodegradable molecules.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".