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Micrococcus yunnanensis and Psychrobacter sp. as Potential Producers of Polymers from Hot Spring

2022· article· en· W4283729073 on OpenAlexfundno aff
Asma Jabeen, Rida Batool, Nazia Jamil

Bibliographic record

VenueIndustria Jurnal Teknologi dan Manajemen Agroindustri · 2022
Typearticle
Languageen
FieldMaterials Science
Topicbiodegradable polymer synthesis and properties
Canadian institutionsnot available
FundersPublic Health Agency of Canada
KeywordsPolyhydroxyalkanoatesStrain (injury)BacteriaGlycerolFood scienceHot springMicrococcusChemistryPolysaccharideBiologyBiochemistry

Abstract

fetched live from OpenAlex

Polyhydroxyalkanoates (PHAs) and exopolysaccharides (EPSs) are biopolymers bacteria under nutrient-limiting conditions. In this study, bacterial strains were isolated from hot springs. Soil samples were collected from Tatta Pani, Azad Kashmir, Pakistan. Bacterial strains AJ2 and AJ3 were selected due to their ability to produce PHAs and EPSs. Phylogenetic analysis showed that strain AJ2 was Micrococcus yunnanensis and AJ3 was Psychrobacter sp. Three carbon sources (glucose, glycerol, and molasses) were used for polymer production. The effect of high pH (8) and high temperature (55 °C) was checked on PHAs and EPSs production. The highest yield of PHAs was given by strain AJ3 (89.43%) with molasses. When grown at 55 °C for 24 hours, strain AJ3 showed the highest PHAs accumulation, 79% with glucose. At alkaline pH 8, strain AJ3 gave 34% PHAs with molasses. The highest EPSs production was observed for strain AJ3. AJ3 gave 70g/L of EPSs with both glucose and glycerol. The amplification of the phaC gene was done to confirm the genetic basis of PHAs production. FTIR analysis showed clear bands at 1722 cm-1 and 2925 cm-1 representing the carbonyl and alkyl groups of PHAs, respectively.

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)0.000
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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.028
GPT teacher head0.227
Teacher spread0.199 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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