Glyphosate has a negligible impact on bacterial diversity and dynamics during composting
Bibliographic record
Abstract
The herbicide glyphosate has several potential entry points into composting sites and its impact on composting processes has not yet been evaluated. To assess its impact on bacterial diversity and abundance as well as on community composition and dynamics, we conducted a mesocosm experiment at the Montreal Botanical Garden. Glyphosate had no effect on physicochemical property evolution during composting, while it was completely dissipated by the end of the experiment. Sampling at Days 0, 2, 28 and 112 of the process followed by 16S rRNA amplicon sequencing also found no effect of glyphosate on species richness and community composition. Differential abundance analyses revealed an increase of a few taxa in the presence of glyphosate, namely TRA3-20 (order Polyangiales), Pedosphaeraceae and BIrii41 (order Burkholderiales) after 28 days. In addition, five amplicon sequence variants (ASVs) had lower relative abundance in the glyphosate treatment compared to the control on Day 2, namely Comamonadaceae, Pseudomonas sp., Streptomyces sp., Thermoclostridium sp. and Actinomadura keratinilytica, while two ASVs were less abundant on Day 112, namely Pedomicrobium sp. and Pseudorhodoplanes sp. Most differences in abundance were measured between the different sampling points within each treatment. These results present glyphosate as a poor determinant of species recruitment during composting.
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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) | 0.001 | 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".