Influence of polyphenols on low-loaded synthetic winery wastewater constructed wetland treatment with different plant speciesA paper submitted to the Journal of Environmental Engineering and Science.
Bibliographic record
Abstract
Synthetic wastewaters simulating physically pre-treated low-loaded winery effluents were treated for four months with five pilot-scale horizontal subsurface flow constructed wetlands (HSSF-CWs) using different plants. Species under study were Phragmites australis (HSSF-CW2), Lythrum salicaria (HSSF-CW3), Cladium mariscus (HSSF-CW4), and Iris pseudacorus (HSSF-CW5). The designation HSSF-CW1 was not planted, and was used as a control. The mean dissolved oxygen and oxidation–reduction potential values in all HSSF-CWs indicated anaerobic conditions in the bulk liquid. High pollutant-removal efficiencies were obtained. Apparently, the species with higher growth (Phragmites, Lythrum, and particularly Iris) improved total nitrogen (TN) and nitrogen as ammonium (N-NH 4 + ) removals, but adversely affected sulphate (SO 4 2– ) anaerobic reduction. Chemical oxygen demand (COD) removal efficiencies were high, although there were no clear indications how the kinds of plants might have influenced this parameter. A statistical analysis of variance indicated that only N-NH 4 + removal efficiencies were statistically different owing to the influence of the different plants. In a second 6 month experimental period, polyphenols (13 mg L –1 ) were added to the synthetic wastewater to study possible inhibition effects. The addition of polyphenols did not seem to cause inhibition effects on COD, TN, and N-NH 4 + removals, but clearly negatively affected SO 4 2– removal. A new two-way analysis of variance confirmed that only SO 4 2– removal was negatively affected by polyphenols, while the effects of the different plants were only significant for N-NH 4 + removal. Polyphenols were nearly completely removed. First order rate constants obtained for COD, TN, SO 4 2– , and polyphenol removals were similar to those reported by other authors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".