Bibliographic record
Abstract
Antibiotics, widely used to combat infections, have an unintended impact on the environment, contributing to climate change through their production, application, and disposal. The pharmaceutical sector is a significant source of greenhouse gas (GHG) emissions and other pollutants throughout the antibiotic manufacturing process, exacerbating global warming. Additionally, improper antibiotic disposal leads to environmental contamination, promoting antimicrobial resistance (AMR) and disrupting microbial ecosystems essential for the carbon and nitrogen cycles. Moreover, their use in livestock increases methane emissions and soil degradation. Addressing this dual challenge—AMR and climate change—requires sustainable practices in antibiotic production, distribution, and waste management. Strategies such as implementing green chemistry in drug manufacturing, exploring alternative therapies, and enhancing wastewater treatment processes can significantly reduce pharmaceutical pollution. Policies must focus on minimizing antibiotic misuse while mitigating the environmental consequences of pharmaceutical waste. Cross-disciplinary collaboration is essential to tackle the interconnected challenges posed by antibiotics and climate change. Developing sustainable solutions will help maintain both public health and ecological balance while reducing the long-term environmental footprint of antibiotic usage.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".