Antibiotics in the global river system arising from human consumption
Bibliographic record
Abstract
The presence of antibiotics in surface waters poses risks to aquatic ecosystems and human health due to their toxicity and influence on antimicrobial resistance. After human consumption and partial metabolism, antibiotic residues are excreted and undergo complex accumulation and decay processes along their pathway from wastewater to natural river systems. Here, we use a global contaminant fate model to estimate that of the annual human consumption of the 40 most used antibiotics (29,200 tonnes), 8,500 tonnes (29%) are released into the river system and 3,300 tonnes (11%) reach the world's oceans or inland sinks. Even when only domestic sources are considered (i.e. not including veterinary or industrial sources), we estimate that 6 million km of rivers worldwide are subject to total antibiotic concentrations in excess of thresholds that are protective of ecosystems and resistance promotion during low streamflow conditions, with the dominant contributors being amoxicillin, ceftriaxone, and cefixime. Therefore, it is of concern that human consumption alone represents a significant risk for rivers across all continents, with the largest extents found in Southeast Asia. Global antibiotic consumption has grown rapidly over the last 15 years and continues to increase, particularly in low- and middle-income countries, requiring new strategies to safeguard water quality and protect human and ecosystem health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".