Investigating the occurrence of antimicrobial resistance in the environment in Canada: a scoping review
Bibliographic record
Abstract
Antimicrobial resistance is an environmental, agricultural, and public health problem that is impacting the health of humans and animals. The role of the environment as a source of and transmission pathway for antibiotic resistant bacteria and antibiotic resistance genes is a topic of increasing interest that, to date, has received limited attention. This study aimed to describe the sources and possible pathways contributing to antimicrobial resistance dissemination through bioaerosols, water, and soil in Canada using a scoping review methodology and systems thinking approach. A systems map was created to describe the occurrence and relationships between sources and pathways for antimicrobial resistance dissemination through water, soil, and bioaerosols. The map guided the development of the scoping review protocol, specifically the keywords searched and what data were extracted from the included studies. In total, 103 studies of antimicrobial resistance in water, 67 in soil, and 12 in air were identified. Studies to detect the presence of antimicrobial resistance genes have mainly been conducted at wastewater treatment plants and commercial animal livestock facilities. We also identified elements in the systems map with little or no data available (e.g., retail) that need to be investigated further to have a better understanding of antimicrobial resistance dissemination through different Canadian environments.
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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.029 | 0.051 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".