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Record W2082778652 · doi:10.1080/09593330.2014.963695

Pretreatment of poultry manure anaerobic-digested effluents by electrolysis, centrifugation and autoclaving process for<i>Chlorella vulgaris</i>growth and pollutants removal

2014· article· en· W2082778652 on OpenAlex
Mengzi Wang, Yu Wu, Baoming Li, Renjie Dong‬, Haifeng Lu, Hongde Zhou, Wei Cao

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

VenueEnvironmental Technology · 2014
Typearticle
Languageen
FieldEnergy
TopicAlgal biology and biofuel production
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEffluentChlorella vulgarisChemistryTurbidityPulp and paper industryEnvironmental chemistryElectrolysisMicroorganismBacteriaEnvironmental engineeringAlgaeBotanyBiologyEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

Different pretreatments (electrolysis, centrifugation and autoclaving) coupled with Chlorella vulgaris biological system was used for the treatment of poultry manure anaerobic-digested effluents. The pretreated effluents were used as the growth medium for algal cultivation. The pollutant removal efficiencies of the combined treatments were determined. Electrochemical pretreatment can efficiently remove the ammonia (NH4+), total phosphorus (TP), total organic carbon (TOC), total carbon (TC), turbidity and bacteria in the digested effluents. About 100.0% NH4+, turbidity and bacteria, 97.6% TP, 81.5% TOC and 96.6% inorganic carbon were removed by 5-h electrochemical treatment. The maximal algal biomass accumulation (0.53 g L(-1)) was obtained from culture in the effluents pretreated with 2-h electrolysis. The pollutants removal amounts by the combination of electrolysis and biological treatment were much higher than the other combinations.

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 score0.076
Threshold uncertainty score0.720

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 head0.003
GPT teacher head0.200
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