Fate of tetracycline antibiotics in dairy manure-amended soils
Bibliographic record
Abstract
The US dairy industry has changed significantly during the past 20 years. The number of dairies declined 63% from 1997 to 2012 owing to the rise in concentrated animal feeding operations and the concomitant decline of small dairy farms. Efficient and cost-effective dairies adhering to the concentrated animal feeding operation business design are praised for their high milk production. However, with a per capita daily manure production of 55 kg, storage and disposal of manure at these large operations pose significant management challenges and environmental risks. Application to surrounding agricultural fields is a common practice for disposing of manure, but the fate and consequences of antibiotics present in dairy waste are issues of great concern. Although antibiotics in the environment promote microbial resistance, their risks to humans and the environment are not completely known. Understanding and predicting the fate of antibiotics from dairy manure in soils is complicated by the variability and complex interactions of soil factors in addition to the diversity of chemicals of emerging concern, their amphoteric structures, and potential antagonistic and synergistic interactions among chemicals of emerging concern. This review explores behavior of the tetracycline (TC) class of antibiotics from dairy manure in the soil environment. TC fate in soils depends significantly on soil pH, ionic strength, and soil organic matter (SOM). Molecular charge and physicochemical properties of TCs at typical soil pHs encourage strong sorption to soils; however, this interaction is complicated by organic matter and metals, and may also encourage development of antibiotic resistance. Furthermore, TC degradation products exhibit distinct properties from their parent compounds that also must be considered. Increased knowledge of the behavior of TC antibiotics in soil is needed to enable mitigation of their potential risks.
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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 | 0.000 | 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".