Slag columns for upgrading phosphorus removal from constructed wetland effluents
Bibliographic record
Abstract
The current best option to upgrade constructed wetlands (CWs) for phosphorus (P) retention, in terms of efficiency, cost and simplicity, consists in using media having a strong P affinity. The media can be used either in the planted beds or in a filtration system downstream of the beds. The use of slag filters was shown to be efficient for removing P from wastewater as it represented a slow release source of calcium and hydroxide, favouring the formation of hydroxyapatite. Our study aimed at maximising the P retention capacity of slag filters located at the outlet of CWs since electric arc furnace slag has been shown to inhibit the growth of macrophytes when used in the filtration matrix. Bench-scale columns (Vtot = 6.2 L) filled with various combinations of filter media (slag, granite, limestone) of different sizes (2-5, 5-10, 10-20 mm) were fed on-site during four months with a CW effluent (in mg/L: 30 COD, 30 TSS, 10 Pt). Results showed that the best media combination enabling the maximum o-PO4 retention (more than 80% removal without clogging) consisted in a series of a ternary mix column (slag 5-10 mm, granite 2-5 mm, limestone 5-10 mm) followed by a slag column (slag 5-10 mm). Pilot scale columns (Vtot = 300 L), filled with the best media combination, were installed at the outlet of a 28 m2 CW. These columns showed more than 75% removal efficiency during one year and were designed to be easily replaced each year.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".