Bibliographic record
Abstract
Five wetland designs, based on conventional surface flow (SF) and subsurface flow (SSF) approaches, were assessed for nitrogen and phosphorus removal from greenhouse wastewater. Results indicated none of the individual designs assessed was capable of providing the highest treatment effect for all nutrients of concern; however, the SF wetland emerged as the most appropriate design for the treatment of greenhouse wastewater. The highest mean phosphorus reduction of 65% was observed in the unplanted SF wetlands. Peak nitrate reductions of 54% were observed in the 15-cm deep SF wetlands and ammonia removal of 74% was achieved in the unplanted SF wetlands. Nitrate concentration in the greenhouse effluent can be reduced to acceptable levels for the protection of freshwater aquatic life (i.e., less then 40 ppm) using a loading rate of 1.65 g NO3-N/m2/day and a design water depth of 30 cm or greater. Based on available literature and the results of this research project, a multistage design, consisting of an unplanted pre-treatment basin followed by a 25 to 35 cm deep surface flow marsh with open water components, is recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".