Autotrophic nitrogen‐removing biofilms on porous and non‐porous membranes
Bibliographic record
Abstract
The effective removal of nitrogen compounds from wastewater has become a critical issue for treatment plants as the awareness of their negative impact on the environment increased. Autotrophic nitrogen removal has become an interesting alternative to the more conventional heterotrophic processes, as it eliminates the need for an organic carbon addition to the source water and reduces biomass yields. Gas transfer membrane biofilm reactors (MBfR) for nitrification and hydrogen driven denitrification are of special interest as they combine membrane diffusers and biofilms, provide an efficient supply of necessary electron donor for autotrophic removal of ammonia and nitrate, extend solids retention times and retain biomass within the reactor. Subsequently, a wide range of MBfR, which vary based on the type of membrane material and membrane module configuration, are being tested for this purpose. This paper reviews the research to date and also discusses the challenges that still lay ahead before MBfR can be used at treatment plants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".