Influence of free-floating plant species richness and composition on water quality improvement
Bibliographic record
Abstract
Background The link between species richness and ecosystem services remains a central question in ecology.Aims To evaluate the effect of composition and plant richness on water quality improvement.Methods Thirty-nine mesocosms (65 L) were divided into four quadrants and were either planted in monocultures, or in 2- or 4-species combinations. Mesocosms were fed with synthetic wastewater during one growing season and outflow samples were collected weekly for physico-chemical analyses.Results Pollutant removal efficiency varied among plant species and species combinations. Eichhornia crassipes outperformed the other plant species and was the only one whose presence in a plant combination had a positive effect on pollutant removal. Species richness had a small but highly significant effect on nitrogen removal, with 2-species and 4-species systems outperforming by 4% and 5%, respectively, the average removal of the monocultures. The removal efficiency of a combination of two species was occasionally better than the average of these species in monocultures. However, higher plant species richness never showed greater treatment performance over the most efficient monoculture of its constituent species.Conclusions Our study showed some weak but significant biodiversity effects of free-floating plant species on water quality improvement. Nevertheless, total plant biomass remained a better predictor of water purification capacity than species richness.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".