Groundwater antibiotic pollution and its relationship with dissolved organic matter: Identification and environmental implications
Bibliographic record
Abstract
The occurrence of veterinary antibiotics and hydro-chemical parameters in eleven natural springs in a livestock production area is evaluated, jointly with the characterization of their DOM fingerprint by Orbitrap HRMS. Tetracycline and sulfonamide antibiotics were ubiquitous in all sites, and they were detected at low ng L−1 concentrations, except for doxycycline, that was present at μg L−1 in one location. DOM analysis revealed that most molecular formulas were CHO compounds (49 %–68 %), with a remarkable percentage containing nitrogen and sulphur (16 %–23 % and 11 %–24 %, respectively). Major DOM components were phenolic and highly unsaturated compounds (~90 %), typical for soil-derived organic matter, while approximately 11 % were unsaturated aliphatic, suggesting that springs may be susceptible to anthropogenic contamination sources. Comparing the DOM fingerprint among sites, the spring showing the most different profile was the one with surface water interaction and characterized by having lower CHO and higher CHOS formulas and aliphatic compounds. Correlations between antibiotics and DOM showed that tetracyclines positively correlate with unsaturated oxygen-rich substances, while sulfonamides relate with aliphatic and unsaturated oxygen-poor compounds. This indicates that the fate of different antibiotics will be controlled by the type of DOM present in groundwater.
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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 | 0.000 | 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".