Multi-year and short-term responses of soil ammonia-oxidizing prokaryotes to zinc bacitracin, monensin, and ivermectin, singly or in combination
Bibliographic record
Abstract
A field experiment was initiated whereby a series of replicated plots received annual applications of ivermectin, monensin, and zinc bacitracin, either singly or in a mixture. Pharmaceuticals were added at concentrations of 0.1 mg/kg soil or 10 mg/kg soil. The authors collected soil samples in 2013, before and after the fourth annual application of pharmaceuticals. In addition, a 30-d laboratory experiment was undertaken with the same soil and same pharmaceuticals, but at concentrations of 100 mg/kg soil. The impact of the pharmaceuticals on nitrification rates, on the abundance of ammonia-oxidizing bacteria (AOB), and on the abundance of ammonia-oxidizing archaea (AOA) was assessed. None of the pharmaceuticals at 0.1 mg/kg had any effect on nitrification. Referenced to control soil, nitrification was accelerated in soil exposed to 100 mg/kg zinc bacitracin or 10 mg/kg of the pharmaceutical mixture, but none of the treatments inhibited nitrification. Neither AOB abundance nor AOA abundance was affected by the pharmaceuticals at 0.1 mg/kg. At 10 mg/kg, monensin, zinc bacitracin, and a mixture of all 3 pharmaceuticals suppressed the abundance of AOB, and zinc bacitracin and the mixture increased AOA abundance. The decrease in AOB abundance and increase in AOA abundance when exposed to 10 mg/kg soil suggests that AOB are more sensitive to these chemicals and that AOA populations can expand to occupy the partially vacated niche.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".