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Record W2051091417 · doi:10.1002/bit.20603

Detachment of multi species biofilm in circulating fluidized bed bioreactor

2005· article· en· W2051091417 on OpenAlexaff
Ajay Patel, George Nakhla, Jesse Zhu

Bibliographic record

VenueBiotechnology and Bioengineering · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsWestern University
Fundersnot available
KeywordsAnoxic watersBiofilmBioreactorChemistryBiomass (ecology)Volatile suspended solidsFluidized bedEnvironmental chemistryMicrobiologyBiologyBacteriaEnvironmental engineeringSuspended solidsEcologyEnvironmental scienceWastewaterOrganic chemistry

Abstract

fetched live from OpenAlex

In this study, the detachment rates of various microbial species from the aerobic and anoxic biofilms in a circulating fluidized bed bioreactor (CFBB) with two entirely separate aerobic and anoxic beds were investigated. Overall detachment rate coefficients for biomass, determined on the basis of volatile suspended solids (VSS), glucose and protein as well as for specific microbial groups, i.e., for nitrifiers, denitrifiers, and phosphorous accumulating organisms (PAOs), were established. Biomass detachment rates were found to increase with biomass attachment on carrier media in both beds. The detachment rate coefficients based on VSS were significantly affected by shear stress, whereas for protein, glucose and specific microbial groups, no significant effect of shear stress was observed. High detachment rates were observed for the more porous biofilm structure. The presence of nitrifiers in the anoxic biofilm and denitrifiers in the aerobic biofilm was established by the specific activity measurements. Detachment rates of PAOs in aerobic and anoxic biofilms were evaluated.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.207
Teacher spread0.194 · 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

Citations36
Published2005
Admission routes1
Has abstractyes

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