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Record W3004484569 · doi:10.3390/toxins11120723

Metabolome Variation between Strains of Microcystis aeruginosa by Untargeted Mass Spectrometry

2019· article· en· W3004484569 on OpenAlexafffund
Marianne Racine, Ammar Saleem, Frances R. Pick

Bibliographic record

VenueToxins · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of OttawaEnvironment and Climate Change Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMetabolomeCyanobacteriaMicrocystis aeruginosaMetabolomicsMicrocystinMass spectrometryMicrocystisChemistryBiologyChromatographyBacteriaGenetics

Abstract

fetched live from OpenAlex

Cyanobacteria are notorious for their potential to produce hepatotoxic microcystins (MCs), but other bioactive compounds synthesized in the cells could be as toxic, and thus present interest for characterization. Ultra performance liquid chromatography and high-resolution accurate mass spectrometry (UPLC-QTOF-MS/MS) combined with untargeted analysis was used to compare the metabolomes of five different strains of the common bloom-forming cyanobacterium, Microcystis aeruginosa. Even in microcystin-producing strains, other classes of oligopeptides including cyanopeptolins, aeruginosins, and aerucyclamides, were often the more dominant compounds. The distinct and large variation between strains of the same widespread species highlights the need to characterize the metabolome of a larger number of cyanobacteria, especially as several metabolites other than microcystins can affect ecological and human health.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.196
Teacher spread0.192 · 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 designObservational
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

Citations14
Published2019
Admission routes2
Has abstractyes

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