Influence of polyphenols on low-loaded synthetic winery wastewater constructed wetland treatment with different plant species
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-NH4+) removals, but adversely affected sulphate (SO42−) 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-NH4+ 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-NH4+ removals, but clearly negatively affected SO42− removal. A new two-way analysis of variance confirmed that only SO42− removal was negatively affected by polyphenols, while the effects of the different plants were only significant for N-NH4+ removal. Polyphenols were nearly completely removed. First order rate constants obtained for COD, TN, SO42−, and polyphenol removals were similar to those reported by other authors.
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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) | 0.000 | 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 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".