MétaCan
Menu
Back to cohort
Record W2042120014 · doi:10.2166/wst.2006.417

Combined partial nitritation/Anammox system for treatment of digester supernatant

2006· article· en· W2042120014 on OpenAlexfundno aff
Larry J. Gut, E. Płaza, Józef Trela, Bengt Hultman, Jan Bosander

Bibliographic record

VenueWater Science & Technology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsnot available
FundersEgg Farmers of Canada
KeywordsAnammoxNitriteNitrogenPulp and paper industryChemistryEnvironmental scienceEnvironmental engineeringAnaerobic exerciseAmmoniumWaste managementNitrateDenitrificationEngineeringBiologyDenitrifying bacteria

Abstract

fetched live from OpenAlex

One-year (2004) comprehensive investigations in a semi-industrial pilot plant (5 m3) were carried out with the aim of assessing the influence of operational parameters on the partial nitritation/Anammox system performance. In the system designed as a moving-bed biofilm reactor, the influent nitrogen load to the Anammox reactor was progressively increased and a stable Anammox bacterial culture was obtained. Interaction between subsequent aerobic and anaerobic conditions in the partial nitritation and Anammox reactors, respectively, granted conditions to remove nitrogen through the nitrite route. It implies that the oxygen supply can be limited to a high extent. A control strategy for the partial nitritation step relied on concomitant adjustment of the air supply with a variable influent nitrogen load, which can be monitored by both pH and conductivity measurements. In the Anammox reactor, an influent nitrite-to-ammonium ratio plays a vital role in obtaining efficient nitrogen removal. During the 1-year experimental period, the Anammox reactor was operated steadily and average nitrogen removal efficiency was 84% with 97% as the maximum value.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.009
GPT teacher head0.213
Teacher spread0.204 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations59
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueWater Science & TechnologySame topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207