The effect of glyphosate on bacteria and archaea community composition in freshwater biofilms
Bibliographic record
Abstract
Glyphosate-based herbicides are some of the most widely used herbicides in the world today, however, there is still much to learn about how glyphosate affects non-target ecosystems. Specifically, freshwater aquatic biofilms are often exposed to glyphosate-based herbicides through anthropogenic activities. This study aims to understand the effects of glyphosate on bacteria and archaea components of freshwater biofilms through a simulated agricultural pulse-dose exposure of 0.5 mg glyphosate a.e./L biweekly over 21 days. Biofilms were cultured in situ from a variety of wetlands in Rondeau Bay, Ontario and were transported to lab microcosms for the exposure experiment. We found that glyphosate exposure did not have a significant effect on the richness or Shannon-Weiner diversity of bacteria or archaea within biofilm communities. These communities did significantly change over time due to glyphosate exposure, but the exposure did not drive the microbial communities toward greater homogeneity or heterogeneity in composition. We also found evidence that amplicon sequence variants that were indicative of glyphosate-exposed communities may be resistant to glyphosate through class II EPSPS enzymes and some may be able to use glyphosate as a phosphorus source through C-P lyase. This suggests that biofilm communities are structurally resilient to pulsed exposures of glyphosate over chronic exposure durations at realistic environmental exposure levels. Additionally, some bacteria or archaea may be useful indicators of episodic glyphosate contamination in wetland ecosystems. Given their complexity, ubiquity, and functional importance in shallow waters, biofilm ecology is a growing field of study.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".