MétaCan
Menu
Back to cohort
Record W2374505944

Relations Between COD Removal and Biofilm Thickness and Density of Downflow Anaerobic Turbulent Bed

2001· article· en· W2374505944 on OpenAlex
Cheng He

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

VenueJournal of Hohai University · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsTurbulenceFluidizationBiogasAnaerobic exerciseChemistryRange (aeronautics)Fluidized bedHydraulic retention timeDraft tubeChemical engineeringMaterials sciencePulp and paper industryEnvironmental engineeringEnvironmental scienceWastewaterWaste managementMechanicsComposite material
DOInot available

Abstract

fetched live from OpenAlex

A reactor,called a downflow anaerobic turbulent bed,is introduced,in which the circulation is forced by biogas to ensure fluidization of floating carrier particles.When the operational range of the reactor is 6 22?kg/(m 3·d) and the hydraulic retention time 0.25~0.94?d,the COD Cr removal efficiency remains 65%~85%,and the bio film thickness is rather small(5~30?μm),while the bio film density is high (70?g/L).Experimental results show that the biofilm thickness and density increase with the decrease of COD removal.Bioactivity kept at a high level in the reactor indicates that violent turbulence and shear are beneficial for the growth of microbe and the formation of a thin,dense and active film.Meanwhile,the volatile solid detachment at a high organic loading rate (OLR) increases quasi linearly with COD removal and with the decrease of the amount of solid in the reactor.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.226

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 head0.014
GPT teacher head0.211
Teacher spread0.196 · 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