MétaCan
Menu
Back to cohort
Record W3004484569 · doi:10.3390/toxins11120723

Metabolome Variation between Strains of Microcystis aeruginosa by Untargeted Mass Spectrometry

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score1.000

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)0.0050.001

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 head0.005
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