Upgrading municipal lagoons in temperate and cold climates: Total nitrogen removal and phosphorus assimilation at ultra‐low temperatures
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this study, a municipal lagoon with high wintertime effluent total ammonia nitrogen (TAN) concentrations was upgraded with a pilot‐scale nitrifying‐nitrifying‐denitrifying (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) and to 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 69.8 ± 24.5% and 74.7 ± 20.1%, respectively. Furthermore, it was observed that sulfide toxicity may play an important role in the inhibition of nitrifying organisms in lagoons. Finally, the MBBR treatment technology has emerged as a suitable and sustainable upgrade technology for existing lagoon and waste stabilization pond facilities operating in temperate, northern and cold climate countries.
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.
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 | 0.000 |
| 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 it