Bibliographic record
Abstract
Abstract A new method for the treatment of wastewater phosphate is presented that relies on reductive iron dissolution (RID process). It is analogous to existing treatment methods that use iron salts (e.g., FeCl2) to precipitate P from wastewater, except that in this case ferrous iron is made available by the reductive dissolution of ferric iron solids that are contained in a reactive porous media. The ferric solids are minerals such as amorphous Fe(OH)3 that occur naturally in soils and sediments. A laboratory column study and a pilot scale field trial demonstrated that these solids are susceptible to reductive dissolution when mixed with sewage effluent, resulting in increased Fe(II) in solution. This promotes the precipitation of Fe(II)‐P solids and then Fe(III)‐P solids when the effluent is subsequently oxidized. Both experiments demonstrated the ability of the RID media to passively solubilize consistent, moderate concentrations of Fe (1–9 mg L−1) over extended periods (2–3 yr). In the column test, influent PO4 of 9.0 ± −3.7 mg P L−1 was lowered to 2.1 ± 1.1 mg P L−1. In the field trial, influent PO4 of 10.2 ± 6.0 mg P L−1 was lowered to <0.05 mg P L−1. This technique may be attractive for use with smaller wastewater treatment systems such as septic systems, because it can be maintenance‐free for long periods and it avoids excess sludge accumulation.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".