Phytoplankton abundance and methane emissions are minimally impacted by environmentally-relevant glyphosate concentrations in small-scale outdoor mesocosms
Bibliographic record
Abstract
Glyphosate is one of the most widely applied agrochemicals in North America and can be directly transported via runoff into non-target aquatic habitats. Yet, our understanding of how this herbicide affects aquatic ecosystems is limited; past studies have often focused on single species effects and/or used herbicide concentrations several orders of magnitude higher than what has been reported in contaminated aquatic systems. Further, glyphosate in aquatic systems has the potential to alter greenhouse gas emissions (methane) if it is broken down for phosphate utilization by bacteria under specific environmental conditions (i.e., oxygen, nutrient concentrations). In this study, we assessed the temporal changes in nutrients, phytoplankton, copy number of genes associated with breakdown of glyphosate or production of methane (phnJ, mcrA), and methane concentrations in 12-day mesocosms with amendments of glyphosate, nitrogen, and/or phosphorus. We found glyphosate at environmentally-relevant concentrations (∼4 ug/L) did not confer changes in overall ratios and total concentrations of nutrients, or abundance of any major phytoplankton group (cyanobacteria, diatoms, green algae), methane concentration or flux, or gene copy numbers of phnJ and mcrA. Our results suggest that the relatively low concentrations of glyphosate we used (relative to levels used in toxicological studies) did not cause major changes over short time periods in mesocosms, and that the potential for glyphosate to increase greenhouse gas emissions in aquatic systems requires specific conditions to occur and may not be universal in contaminated systems.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".