Effects of artificial aeration, macrophyte species, and loading rate on removal efficiency in constructed wetland mesocosms treating fish farm wastewater
Bibliographic record
Abstract
We studied the contribution of artificial aeration, loading rate, and macrophyte species on pollutant removal in horizontal subsurface flow constructed wetlands (HSSFCWs) treating reconstituted trout farm wastewater. Twelve 1 m2 mesocosms located in a controlled greenhouse environment were used to test two species of macrophytes (Phragmites australis, Typha angustifolia), three loading rates (30, 60, and 90 L·m–2·d–1), and presence or absence of artificial aeration at the intermediate loading rate. There was no effect of any variable (macrophytes, loading, aeration) on total suspended solids (TSS) or chemical oxygen demand (COD) removal. Artificial aeration improved nitrogen removal while higher loading rates diminished removal of nitrogen and phosphorus. Macrophytes improved nitrogen and phosphorus removal, but this effect varied depending on loading rates and presence or absence of artificial aeration. We found no differences between Phragmites and Typha for treatment of trout fish farm wastewater. Under summer conditions, our results suggest that artificial aeration could be used to improve nitrogen removal by HSSFCWs. Key words: horizontal subsurface flow constructed wetlands, artificial aeration, loading rates, Phragmites australis, Typha angustifolia, fish farm wastewater.
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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".