Treatment of Woodwaste Leachate in Surface Flow Mesocosm Wetlands
Bibliographic record
Abstract
Abstract Woodwaste leachate is usually acidic, of high oxygen demand, and toxic. To prevent potential adverse impacts of the raw leachate on heterotrophic bacteria and aquatic plants, woodwaste leachate was diluted before discharge to constructed wetlands. This study compared treatment performance among four vegetated surface flow mesocosm wetlands fed with different dilutions of woodwaste leachate over a period of 12 weeks. During another period of 13 weeks, the effluent of a vegetated wetland fed with the raw leachate was further treated in a vegetated wetland and an open wetland. The highest reduction rates for chemical oxygen demand as well as tannin and lignin were achieved in the wetland fed with the raw leachate. The most diluted (6x) woodwaste leachate yielded the lowest reduction rates and highest reduction efficiencies. Up to 47 mg L-1 volatile fatty acids in influent were depleted through wetlands with a hydraulic retention time of 13 d. Vegetation made insignificant performance differences for treatment of woodwaste leachate. Chemical oxygen demand as well as tannin and lignin were further removed through the wetlands in series, though at lower reduction rates. Wetland performance for treatment of woodwaste leachate was likely regulated by dissolved oxygen concentration and availability of bacterial substrates.
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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".