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Record W2970330227 · doi:10.1186/s13104-019-4580-x

Development and validation of a reliable LC–MS/MS method for quantitative analysis of usnic acid in Cladonia uncialis

2019· article· en· W2970330227 on OpenAlexfundno aff
Natalia Sveshnikova, Tao Yuan, Jamie Warren, Michele D. Piercey‐Normore

Bibliographic record

VenueBMC Research Notes · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsUsnic acidChromatographyMass spectrometryLiquid chromatography–mass spectrometryChemistryLichenBiologyBotany

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to develop and validate a specific and sensitive liquid chromatography tandem mass-spectrometry method for quantification of usnic acid concentration in the lichen, Cladonia uncialis, suitable for detection of relatively small fluctuations of usnic acid concentration in response to environmental changes. RESULTS: The resulting method was fully validated according to international guidelines and demonstrated good selectivity and sensitivity with minor levels of a matrix effect and high accuracy.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.372
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.217
GPT teacher head0.424
Teacher spread0.206 · 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 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

Citations16
Published2019
Admission routes1
Has abstractyes

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