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Record W3000375748 · doi:10.4491/eer.2019.425

Insight on the microbial activity and microbiome in partial nitrification systems: CuO nanoparticles impact under different pH levels

2020· article· en· W3000375748 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironmental Engineering Research · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Ottawa
FundersNational Natural Science Foundation of China
KeywordsNitrificationOxidizing agentAmmoniaChemistryNitrosomonasEffluentBacteriaInorganic chemistryNitrosomonas europaeaEnvironmental chemistryNuclear chemistryBiochemistryNitrogenOrganic chemistryBiologyEnvironmental engineering

Abstract

fetched live from OpenAlex

In this study, the effect of CuO nanoparticles (NPs) on partial nitrification (PN) process was investigated at various pH values. The shortand long-term experiments were carried out in six identical reactors, with or without CuO NPs, at pH values of 6.5, 8.0 and 10.0. The ammonia oxidation, reaction rates, copper content, and the microbial community were investigated. The results of this work suggested that CuO NPs inhibited the ammonia oxidation by aerobic ammonia-oxidizing bacteria (AOB), under both the acid and alkali conditions. AOB could resist the acid condition but was significantly suppressed when CuO NPs was fed, and the low pH did not aggravate the inhibition level. Almost all the bacteria lost bioactivity under the pH as 10.0, while anaerobic ammonia-oxidizing bacteria survived. The acid condition increased the Nitrosomonas relative abundance while the alkali condition lowered it. More than 60% of the supplied CuO NPs was discharged via effluent.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.298
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.048
GPT teacher head0.260
Teacher spread0.212 · 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