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Record W1993504223 · doi:10.1080/10934520802293602

Isolation and molecular identification of extracellular polymeric substances (EPS) producing bacterial strains for sludge settling and dewatering

2008· article· en· W1993504223 on OpenAlexaff
S. Bala Subramanian, S. Yan, R. D. Tyagi, R. Y. Surampalli, B. N. Lohani

Bibliographic record

VenueJournal of Environmental Science and Health Part A · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Nitrogen Removal
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSettlingDewateringFlocculationExtracellular polymeric substanceActivated sludgePulp and paper industryMicroorganismSewage treatmentWastewaterSerratiaChemistryWaste managementBacteriaEnvironmental scienceMicrobiologyBiologyEnvironmental engineeringGeotechnical engineeringGeologyEngineeringBiofilm

Abstract

fetched live from OpenAlex

One of the major problems in overall wastewater treatment process is sludge settling and dewatering. In general, sludge settling and dewatering is carried out using conventional physico-chemical methods that are known to be expensive, and these processes further increase the sludge volume and ultimate disposal costs. To overcome this problem, a suitable alternative could be the use of bioflocculants for sludge settling and dewatering. To achieve bioflocculation, extracellular polymeric substances (EPS) producing bacterial strains were isolated from the complex microbial community of wastewater sludge. Crude EPS produced in the form of bacterial broth was used to test kaolin flocculation activity. Three out of 10 bacterial strains (B2, B8 and B9) were pre-selected for sludge settling. Based on sludge settling and dewatering results, B8 possessed better flocculating property than other bacterial strains. These sludge microorganisms were identified based on their 16S rDNA sequences and bacterial strain B8 was identified as Serratia sps.

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 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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.307

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.257
Teacher spread0.232 · 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 teacher head, 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

Citations19
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Environmental Science and Health Part ASame topicWastewater Treatment and Nitrogen RemovalFrench-language works237,207