Degradation of antimicrobials in soils and sediments
Bibliographic record
Abstract
The potential for developing and spreading antimicrobial resistance in pathogens is the most important health risk associated with the widespread use of antimicrobials in human medication and in the livestock industry. Residual antimicrobials enter the environment through the discharge of contaminated effluent or through land application of contaminated livestock manure. Many studies have investigated the degradation of antimicrobials in water; however, soils and sediments are also an important environmental matrix as they can act as reservoirs for recalcitrant antimicrobials. This research examines the degradation kinetics of antimicrobials in soils and sediments for two processes, biodegradation and sorption. This paper makes three main research contributions. Firstly, we reviewed the literature to discuss the fate and potential adverse impacts of residual antimicrobials in the environment. Secondly, we examine the important processes governing the environmental fate and transport of residual antimicrobials to highlight trends and contributing factors. Lastly, we developed a multi-level experimental design to study the sorption and biodegradation of five priority antimicrobials (lincomycin, monensin, sulfamethazine, tetracycline, and triclosan) in soils and sediments. The results of this experimental study will be used to model the adsorption and biodegradation kinetics of the target antimicrobials. Soil and sediment samples have been collected from three pristine sites in a Southern Ontario watershed.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".