Upgrading municipal lagoons in temperate and cold climates: Total nitrogen removal and phosphorus assimilation at ultra-low temperatures
Bibliographic record
Abstract
In this study, a municipal lagoon with high wintertime effluent total ammonia nitrogen (TAN) concentrations was upgraded with a pilot-scale NIT-NIT-DENIT moving bed biofilm reactor (MBBR) treatment train to characterize its effluent over wintertime operation, investigate the feasibility of upgrading lagoons to achieve substantial biological total nitrogen removal across ultra-low temperatures (0.6 – 3.0°C) and investigate nitrification inhibition pathways in facultative lagoon systems at ultra-low temperatures. Throughout the study, it was observed that the system substantially reduced total nitrogen (TN) and total phosphorus (TP) effluent concentrations by an average of 69.0 ± 24.5% and 74.7 ± 20.1%, respectively, with average TN and TP concentrations exiting the treatment train of 7.60 ± 5.60 mg-N/L and 0.05 ± 0.02 mg-P/L, respectively, indicating the feasibility of upgrading municipal lagoons to meet increasing stringent effluent standards to ensure the perenniality of water resources. Furthermore, it was observed that sulfide toxicity may play an important role in the inhibition of nitrifying organisms in lagoons.
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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) | 0.000 | 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".