Contrasting Toxicity of a Fomesafen-Based Herbicide on Three Freshwater Phytoplanktonic Species
Bibliographic record
Abstract
Pesticides leaching and run-off to nearby freshwater sources are a major ecological concern. The emergence of herbicide-resistant weeds led to the increased usage of fomesafen, a diphenyl ether herbicide inhibiting protoporphyrinogen oxidase (PPO). This recent rise in demand and use for this molecule invariably increases the chance of this herbicide entering freshwater environments and affecting non-target organisms. However, there is still a lack of information in the literature regarding the impact of this herbicide on the physiology of freshwater phytoplankton. This study aimed to determine the impact of five concentrations (0, 5, 10, 40, 320 µg · L−1) of a fomesafen-based herbicide (Reflex®) on the physiology of two species of green microalgae (Raphidocelis subcapitata FACHB-271; Chlamydomonas snowii) and one cyanobacterial species (Microcystis aeruginosa CPCC 632). While physiological biomarkers (growth, photosynthesis, pigment content, oxidative stress and morphology) of R. subcapitata were significantly affected by the fomesafen treatments, no significant effects were observed in the physiology of C. snowii and M. aeruginosa. We hypothesize that this difference in fomesafen resistance is most likely due to intracellular morphological and genetic differences between species. Modeling of fomesafen uptake in R. subcapitata showed that alteration of cell biovolume is unlikely to be an efficient mechanism modulating fomesafen toxicity and that potential fomesafen efflux or breakdown would need to be very fast (and operate at a high energy cost) in order to protect against uptake and toxicity. This study provides new insights into the sensitivity of different algae species toward fomesafen as well as the associated cellular toxicity mechanisms.
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) | 0.000 | 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".