Biodegradation of caffeine in agricultural soil
Bibliographic record
Abstract
Caffeine (1,3,7-trimethylxanthine) could represent a useful marker of contamination of effluent from agricultural land receiving biosolids. The persistence characteristics of caffeine in three agricultural soils was investigated. In laboratory microcosms of moist soil incubated at 30°C, [8-ring-14C]-caffeine was rapidly and thoroughly mineralized to 14CO2 in a sandy loam and a loam soil, and less rapidly in a silt loam soil. Caffeine mineralization was very responsive to soil temperature and moisture. Mineralization of caffeine was hastened by the addition of liquid municipal biosolids (LMB) from three municipal sewage-treatment plants (MSTPs) that aerate this material. In contrast, LMB from three MSTPs that did not aerobically digest their LMB did not accelerate caffeine mineralization. Autoclaved LMB had no effect on caffeine dissipation. Abacterium, designated Pseudomonas sp. Strain TH1, was isolated from aerated LMB. The bacterium first demethylated caffeine to 3,7-dimethylxanthine, and then mineralized the molecule. Inoculation of Pseudomonas sp. Strain TH1 into soil hastened mineralization of [8-ring-14C]-caffeine. In summary, caffeine was more stable in a silt loam soil than a sandy loam or loam soil, but biodegradation in all three soils was quite uniform upon the addition of caffeine-degrading bacteria or aerated biosolids. We suggest that caffeine would likely not be a suitably conservative chemical marker for detecting chronic contamination of agricultural drainage water following fertilization wit hLMB. Aerobic digestion of LMB promotes the enrichment of caffeine-degrading microorganisms. Key words: Caffeine biodegradation, biosolids chemical marker, biosolids organic contaminant, water quality
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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".