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Record W4403082266 · doi:10.1038/s41598-024-72490-0

C/N ratio effect on oily wastewater treatment using column type SBR: machine learning prediction and metagenomics study

2024· article· en· W4403082266 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

VenueScientific Reports · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring Technologies
Canadian institutionsUniversité Laval
FundersKing Fahd University of Petroleum and Minerals
KeywordsMetagenomicsColumn (typography)WastewaterComputer scienceBioinformaticsComputational biologyChromatographyEnvironmental scienceBiologyChemistryGeneticsEnvironmental engineeringGeneComputer network

Abstract

fetched live from OpenAlex

The sequencing batch reactor has emerged as a promising technology in treating wastewater; however, its application in the treatment of generated water still needs to be explored. This research gap led to the investigation of various carbon-to-nitrogen (C/N) ratios in a column-type sequencing batch reactor (cSBR). The resulting data and model demonstrated that augmenting the SND process with an external carbon source is effective until the C/N ratio reaches 15, ultimately eliminating nitrogen in the produced water. Conversely, a reduced C/N ratio can limit the ability of polyphosphate-accumulating organisms to incorporate carbon into polyphosphate synthesis, thereby decreasing phosphorus removal efficiency within the cSBR. When the C/N ratio ranged from 6 to 8, and the mixed liquor suspended solids concentration was high, the average phosphate removal was approximately 55%, compared to only around 25% when the C/N ratio was less than 6.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.476
Threshold uncertainty score0.508

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.033
GPT teacher head0.275
Teacher spread0.242 · 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