Detection and degradation of organic contaminants in an agricultural soil amended with alkaline-treated biosolids
Bibliographic record
Abstract
Gillis, J. D., Price, G. W. and Stratton, G. W. 2014. Detection and degradation of organic contaminants in an agricultural soil amended with alkaline-treated biosolids. Can. J. Soil Sci. 94: 595–604. The agricultural use of wastewater biosolids is a common practice in many countries, but concerns exist regarding the presence of organic wastewater contaminants that remain in the land-applied biosolids. The objective of this study was to determine if contaminants present in biosolids are detectable in soil following land application. A suite of organic contaminants were monitored by gas chromatograph with mass spectrometer in agricultural soil samples from a site amended with increasing rates of alkaline-treated biosolids. Triclosan, a common antimicrobial agent, was detected at levels greater than the reporting limit in an environment-controlled incubation study and validated through in situ field samples from soils receiving the same alkaline-treated biosolid. A rapid decrease in triclosan concentration was observed during the first few weeks of the incubation study, with concentrations decreasing from 92±26 to 20±2 ng g−1 (average 78% decrease) after 4 mo. The field results indicate that triclosan in fall-applied may persist overwinter. However, a rapid decrease in triclosan concentration during the spring and summer months led to levels lower than predicted following the spring application, and levels below our reporting limit (up to 85% decrease) by the end of the study. Removal is posited to be through aerobic microbial degradation.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".