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Record W3003331566 · doi:10.3808/jeil.201900020

Perspective for Emission and Control of Nitrous Gas in Biological Wastewater Treatment

2019· article· en· W3003331566 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Environmental Informatics Letters · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsBank of CanadaUniversity of Regina
FundersUniversity of Regina
KeywordsNitrous oxideDenitrifying bacteriaDenitrificationNitrificationWastewaterGreenhouse gasSewage treatmentMethaneCarbon dioxideEnvironmental scienceNitriteEnvironmental chemistryChemistryEnvironmental engineeringNitrogenNitrateEcology

Abstract

fetched live from OpenAlex

Being the third most significant anthropogenic greenhouse gas, nitrous gas (N2O) has 300-fold stronger effect than carbon dioxide (CO2) and 4 ~ 30-fold stronger effect than methane (CH4). In this study, the main sources and mechanisms of N2O emission from biological wastewater treatment were reviewed, and the possible mitigation strategies were discussed. Parameters including dissolved oxygen, temperature, pH, organic carbon, and nitrite concentration have influences on the emission of N2O. The possible mitigation strategies were put forward by controlling these parameters in biological wastewater treatment and inducing modified technologies such as simultaneous nitrification-denitrification, denitrifying phosphorus removal, and aerobic granular sludge. In order to obtain a near-zero N2O emission, applying typical catalysts in the nitrification or denitrification tank to decompose N2O into harmless gas is recommended. Moreover, a method of developing photochemical processes to transform N2O could also be suggested.

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 score0.051
Threshold uncertainty score0.390

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 head0.008
GPT teacher head0.205
Teacher spread0.197 · 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