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Record W3104380968 · doi:10.3808/jeil.202000036

Biological Treatment of Dairy Wastewater: A Mini Review

2020· review· en· W3104380968 on OpenAlexafffund
Kai Zhao, Yonghua Wu, Shelley Young, Xiujuan Chen

Bibliographic record

VenueJournal of Environmental Informatics Letters · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsUniversity of Regina
FundersPetroleum Technology Research CentreNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsBiochemical oxygen demandWastewaterChemical oxygen demandEffluentEnvironmental scienceAnaerobic exerciseSewage treatmentDairy industryWaste managementBiochemical engineeringEnvironmental engineeringChemistryEngineeringBiologyFood science

Abstract

fetched live from OpenAlex

The dairy industry is one of the primary water consumers. It produces a large quantity of wastewater with a high concentration of solids, nutrients, fat, and organic compounds characterized by biochemical oxygen demand (BOD) and chemical oxygen demand (COD). Therefore, the treatment of dairy wastewater attracts increasingly more attention. The purpose of the paper is to provide an overview of biological treatment processes for dairy wastewater treatment, including one-stage and two-stage biological processes. The advantages, disadvantages, and limitations of aerobic and anaerobic technologies have been summarized and discussed in detail. Two-stage biological systems are also analyzed. In conclusion, the combined anaerobic and aerobic systems are determined as the most promising technologies for dairy effluent treatment in terms of the quality of the treated water.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.037
GPT teacher head0.261
Teacher spread0.224 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations17
Published2020
Admission routes2
Has abstractyes

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