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Record W1898190355 · doi:10.1002/mrc.4230

Comprehensive multiphase NMR: a promising technology to study plants in their native state

2015· article· en· W1898190355 on OpenAlexafffund
Heather L. Wheeler, Ronald Soong, Denis Courtier‐Murias, Adolfo Botana, Blythe Fortier‐McGill, Werner Maas, Michael Fey, Howard Hutchins, Sridevi Krishnamurthy, Rajeev Kumar, Martine Monette, Henry J. Stronks, Malcolm M. Campbell, André J. Simpson

Bibliographic record

VenueMagnetic Resonance in Chemistry · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Gene Expression Analysis
Canadian institutionsThe Scarborough HospitalBruker (Canada)University of Toronto
FundersKrembil FoundationCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaGovernment of Ontario
KeywordsChemistryState (computer science)Algorithm

Abstract

fetched live from OpenAlex

Nuclear magnetic resonance (NMR) spectroscopy is arguably one the most powerful tools to study the interactions and molecular structure within plants. Traditionally, however, NMR has developed as two separate fields, one dealing with liquids and the other dealing with solids. Plants in their native state contain components that are soluble, swollen, and true solids. Here, a new form of NMR spectroscopy, developed in 2012, termed comprehensive multiphase (CMP)-NMR is applied for plant analysis. The technology composes all aspects of solution, gel, and solid-state NMR into a single NMR probe such that all components in all phases in native unaltered samples can be studied and differentiated in situ. The technology is evaluated using wild-type Arabidopsis thaliana and the cellulose-deficient mutant ectopic lignification1 (eli1) as examples. Using CMP-NMR to study intact samples eliminated the bias introduced by extraction methods and enabled the acquisition of a more complete structural and metabolic profile; thus, CMP-NMR revealed molecular differences between wild type (WT) and eli1 that could be overlooked by conventional methods. Methanol, fatty acids and/or lipids, glutamine, phenylalanine, starch, and nucleic acids were more abundant in eli1 than in WT. Pentaglycine was present in A. thaliana seedlings and more abundant in eli1 than in WT.

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 score0.030
Threshold uncertainty score0.983

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 head0.020
GPT teacher head0.294
Teacher spread0.275 · 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

Citations35
Published2015
Admission routes2
Has abstractyes

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