The response of Scenedesmus quadricauda and Selenastrum capricornutum to glyphosate toxicity (Roundup® formulation) with cellular growth and chlorophyll-a synthesis as endpoints
Bibliographic record
Abstract
Glyphosate is a commonly-used agricultural herbicide which enters freshwater sources and risks affecting non-target aquatic organisms, including algae. In this study, lab cultures of Scenedesmus quadricauda and Selenastrum capricornutum were inoculated with glyphosate (Roundup® formulation) to determine its impact on cellular growth and chlorophyll-a (Ch-a) synthesis. A concentration of 10 mg/L of glyphosate significantly inhibited growth and Ch-a synthesis in S. quadricauda and S. capricornutum. Concentrations ranging from 0 to 3 mg/L of glyphosate did not affect cellular growth or Ch-a synthesis in either species. A concentration of 6 mg/L of glyphosate did initially reduce the growth of S. quadricauda, but growth recovered and Ch-a concentrations were high. For S. capricornutum, growth and Ch-a synthesis were low, and pheophytin concentrations were significantly elevated relative to the control at 6 mg/L of glyphosate. Based these results, S. capricornutum was more sensitive to glyphosate than S. quadricauda, which was likely due to differences in the surface area to volume ratios between the species. In the future, algal toxicity should be studied in greater detail by conducting mesocosm studies within the natural aquatic environment.
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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) | 0.001 | 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".