Table_1_Human Activity Determines the Presence of Integron-Associated and Antibiotic Resistance Genes in Southwestern British Columbia.XLSX
Bibliographic record
Abstract
<p>The dissemination of antibiotic resistant bacteria from anthropogenic sources into the environment poses an emerging public health threat. Antibiotic resistance genes (ARGs) and gene-capturing systems such as integron-associated integrase genes (intI) play a key role in alterations of microbial communities and the spread of antibiotic resistant bacteria into the environment. In order to assess the effect of anthropogenic activities on watersheds in southwestern British Columbia, the presence of putative antibiotic resistance and integrase genes was analyzed in the microbiome of agricultural, urban influenced, and protected watersheds. A metagenomics approach and high-throughput quantitative PCR (HT qPCR) were used to screen for elements of resistance including ARGs and intI. Metagenomic sequencing of bacterial genomic DNA was used to characterize the resistome of microbial communities present in watersheds over a 1-year period. There was a low prevalence of ARGs relative to the microbial population (<1%). Analysis of the metagenomic sequences detected a total of 60 elements of resistance including 46 ARGs, intI1, and groEL/intI1 genes and 12 quaternary ammonium compounds (qac) resistance genes across all watershed locations. The relative abundance and richness of ARGs was found to be highest in agriculture impacted watersheds compared to urban and protected watersheds. A downstream transport pattern was observed in the impacted watersheds (urban and agricultural) during dry months. Similar to other reports, this study found a strong association between intI1 and ARGs (e.g., sul1), an association which may be used as a proxy for anthropogenic activities. Chemical analysis of water samples for three major groups of antibiotics was below the detection limit. However, the high richness and gene copy numbers (GCNs) of ARGs in impacted sites suggest that the effects of effluents on microbial communities are occurring even at low concentrations of antimicrobials in the water column. Antibiotic resistance and integrase genes in a year-long metagenomic study showed that ARGs were driven mainly by environmental factors from anthropogenized sites in agriculture and urban watersheds. Environmental factors such as land-use and water quality parameters accounted for 45% of the variability observed in watershed locations.</p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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 teacher head, 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".