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Record W2557742061

Characterization of iron Bacterium Gallionella ferruginea isolated from the drinking water of the collector wells in Northern Sri Lanka

2016· article· en· W2557742061 on OpenAlexaff
Aberamy Sayanthan, Ponipus T. J. Jashothan, Suntharalingam Saravanan, Ranganathan Kapilan

Bibliographic record

VenueTropical Plant Research · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBacteriaChemistryFood scienceMicrobiologyUreaseBiologyUreaBiochemistry
DOInot available

Abstract

fetched live from OpenAlex

The objective of the study was to isolate, identify and characterize the organism/s responsible for the brownish slime colour and bad odour of the water from the collector wells at Vallipuram area of the Northern Sri Lanka. The iron concentrations in the collector well samples that are free of fecal coliform bacteria, varied from 0.093 to 0.307 mg.l -1 during the day time. Based on the microscopic biochemical and molecular characterization, the bacterial strain isolated from the collector wells was identified as Gallionella ferruginea. They are gram negative kidney- shaped mycoplasmodial bodies found in clusters. The colonial growth was powdery, opaque and flat in elevation. The biochemical characterization showed the positive interpretation for indole and catalase tests while methyl red, citrate, Voges-Proskaeur, urease production, nitrate reduction, tyrosine utilization, acetoin production and oxidase tests showed negative. The bacteria were capable of fermenting glucose with the production of acid in anaerobic condition, but not in aerobic condition thus confirmed as Gallionella ferruginea. This bacterial strain grew well in iron added liquid media at temperatures between 25-40 o C and the optimum growth was observed at 35 o C. Though Gallionella grew well at a broad range pH values between 6.0-10.0, the optimum

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.121

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 head0.023
GPT teacher head0.251
Teacher spread0.228 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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