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Record W1972441310 · doi:10.1139/s03-046

Aerobic treatment of maize-processing wastewater (<i>nejayote</i>) in a single-stream multi-stage bioreactor

2003· article· en· W1972441310 on OpenAlexvenueno aff
Angélica María Salmerón-Alcocer, Norma Rodríguez-Mendoza, Virgen Pineda-Santiago, Eliseo Cristiani‐Urbina, Cleotilde Juárez‐Ramírez, Nora Ruiz‐Ordaz, Juvencio Galíndez‐Mayer

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsnot available
Fundersnot available
KeywordsEffluentBioreactorChemical oxygen demandWastewaterPulp and paper industryBiodegradationEnvironmental scienceSewage treatmentPollutionChemistryWaste managementEnvironmental engineeringBiologyOrganic chemistryEngineeringEcology

Abstract

fetched live from OpenAlex

The manufacturing processes of tortillas, corn chips, tortilla chips, and related products yield a liquid waste called nejayote. This waste causes serious pollution problems since its chemical oxygen demand is very high, from 25000 to 30000 ppm. To reduce the pollution potential of this effluent, the use of a cascade of three continuous flow bioreactors-in-series is proposed in this work. When nejayote was supplemented with KH2PO4 and (NH4)2SO4, at COD:KH2PO4 and COD:(NH4)2SO4 ratios of 87 and 26, respectively, and the pH in the first stage was adjusted at 7.0 every 24 h, high removal efficiencies of COD (86.4%) and nitrogen (80.9%) were obtained. Although the treated water still requires a further treatment prior to its disposal, the proposed system is a potential alternative for nejayote treatment, since it allows a greater COD removal efficiency than those reported in the literature for equivalent organic loads. Key words: nejayote, biodegradation, multi-stage bioreactor, cascade.

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)0.000
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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 head0.017
GPT teacher head0.210
Teacher spread0.193 · 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

Citations43
Published2003
Admission routes1
Has abstractyes

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