Evolution and Emergence of Antibiotic Resistance in Given Ecosystems: Possible Strategies for Addressing the Challenge of Antibiotic Resistance
Bibliographic record
Abstract
Antibiotics were once considered the magic bullet for all human infections. However, their success was short-lived, and today, microorganisms have become resistant to almost all known antimicrobials. The most recent decade of the 20th and the beginning of the 21st century have witnessed the emergence and spread of antibiotic resistance (ABR) in different pathogenic microorganisms worldwide. Therefore, this narrative review examined the history of antibiotics and the ecological roles of antibiotics, and their resistance. The evolution of bacterial antibiotic resistance in different environments, including aquatic and terrestrial ecosystems, and modern tools used for the identification were addressed. Finally, the review addressed the ecotoxicological impact of antibiotic-resistant bacteria and public health concerns and concluded with possible strategies for addressing the ABR challenge. The information provided in this review will enhance our understanding of ABR and its implications for human, animal, and environmental health. Understanding the environmental dimension will also strengthen the need to prevent pollution as the factors influencing ABR in this setting are more than just antibiotics but involve others like heavy metals and biocides, usually not considered when studying ABR.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".