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Record W1999611092 · doi:10.1007/s13213-010-0181-6

Screening for polyhydroxyalkanoate (PHA)-producing bacterial strains and comparison of PHA production from various inexpensive carbon sources

2010· article· en· W1999611092 on OpenAlexfundno aff
Waqas Chaudhry, Nazia Jamil, Iftikhar Ali, Mian Hashim Ayaz, Shahida Hasnain

Bibliographic record

VenueAnnals of Microbiology · 2010
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsnot available
FundersPublic Health Agency of Canada
KeywordsPolyhydroxyalkanoatesFood sciencePseudomonas16S ribosomal RNACorn steep liquorFermentationStrain (injury)Carbon sourceBiologyMetabolic flux analysisBacteriaSugarMicrobiologyGramStainingIndustrial microbiologyChemistryGeneBiochemistry

Abstract

fetched live from OpenAlex

A total of 20 different strains were isolated, purified and screened for polyhydroxyalkanoate (PHA) production. PHA-producing strains were screened by Nile blue staining and confirmed by Sudan Black B staining. Strain 1.1 was selected for further analysis due to its high PHA production ability. PHA production was optimized and time profiling was calculated. PHA production on various different cheap carbon sources, i.e., sugar industry waste (fermented mash, molasses, spent wash) and corn oil, was compared. Cell dry weight and PHA content (%) were calculated and compared. The 12.53 g/L is the CDW of bacterial strain when grown in medium containing corn oil. It was found that corn oil at 12.53 g/L medium can serve as a carbon source for bacterial growth, allowing cells to accumulate PHA up to 35.63 %. The Pha C gene was amplified to confirm the genetic basis for the production of PHAs. Moreover, 16S rRNA gene sequence analysis showed that strain 1.1 belongs to Pseudomonas species.

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)0.000
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.061
GPT teacher head0.290
Teacher spread0.229 · 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

Citations128
Published2010
Admission routes1
Has abstractyes

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