Occurrence of Triclocarban and Triclosan in an Agro-ecosystem Following Application of Biosolids
Bibliographic record
Abstract
Triclocarban (TCC) and triclosan (TCS), two of the most commonly used antimicrobial compounds, can be introduced into ecosystems by applying wastewater treatment plant biosolids to agricultural fields. Concentrations of TCC and TCS were measured in different trophic levels within a terrestrial food web encompassing land-applied biosolids, soil, earthworms (Lumbricus), deer mice (Peromyscus maniculatus), and eggs of European starlings (Sturnus vulgaris) and American kestrels (Falco sparverius) at an experimental site amended with biosolids for the previous 7 years. The samples from this site were compared to the same types of samples from a reference (biosolids-free) agricultural site. Inter-site comparisons showed that concentrations of both antimicrobials were higher on the experimental site in the soil, earthworms, mice (livers), and European starling eggs, but not American kestrel eggs, compared to the control site. Inter-species comparisons on the experimental site indicated significantly higher TCC concentrations in mice (TCC: 12.6-33.3 ng/g) and in starling eggs (TCC: 15.4-31.4 ng/g) than in kestrel eggs (TCC: 3.6 ng/g). Nesting success of kestrels only was significantly lower on the experimental site compared to the reference site due to nest abandonment. This study demonstrates that biosolids-derived TCC and TCS are present throughout the terrestrial food web, including secondary (e.g., starlings) and tertiary (i.e., kestrels) consumers, after repeated, long-term biosolids application.
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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 | 0.001 | 0.001 |
| 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".