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Record W3104380968 · doi:10.3808/jeil.202000036

Biological Treatment of Dairy Wastewater: A Mini Review

2020· review· en· W3104380968 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 · 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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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