Evaluating the effects of triclosan on 3 field crops grown in 4 formulations of biosolids
Bibliographic record
Abstract
A growing body of evidence suggests that amending soil with biosolids can be an integral component of sustainable agriculture. Despite strong evidence supporting its beneficial use in agriculture, there are concerns that chemicals, such as pharmaceuticals and personal care products, could present a risk to terrestrial ecosystems and human health. Triclosan is one of the most commonly detected compounds in biosolids. To date, laboratory studies indicate that triclosan likely poses a de minimis risk to field crops; however, these studies were either conducted under unrealistic exposure conditions or only assessed 1 or 2 formulations of biosolids. The purpose of the present study was to characterize the effects of triclosan on field crops in soils amended with 4 different formulations of biosolids (liquid, dewatered, compost, and alkaline-hydrolyzed), containing both background and spiked triclosan concentrations, following best management practices used in the province of Ontario. Three crop species (corn, soybean, and spring wheat) were evaluated using several plant growth endpoints (e.g., root wet mass, shoot length, shoot wet/dry mass) in 70-d to 90-d potted soil tests. The results indicated no adverse impact of triclosan on any crop-biosolids combination. Conversely, amending soil with biosolids either enhanced or had no negative effect, on the growth of plants. Results of the present study suggest little risk of triclosan to crops in agricultural fields amended with biosolids. Environ Toxicol Chem 2017;36:1896-1908. © 2016 SETAC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".