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Record W3085917107 · doi:10.1039/d0ra04604c

Supermolecule-assisted imaging of low-molecular-weight quaternary-ammonium compounds by MALDI-MS of their non-covalent complexes with cucurbit[7]uril

2020· article· en· W3085917107 on OpenAlexafffund
Di Chen, Jun Han, Juncong Yang, David Schibli, Zhenzhong Zhang, Christoph H. Borchers

Bibliographic record

VenueRSC Advances · 2020
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsMcGill UniversityJewish General HospitalGenome British ColumbiaUniversity of Victoria
FundersWarren Y. Soper Charitable TrustFondation De Famille Alvin SegalMinistry of Science and Higher Education of the Russian FederationGenome AlbertaJewish General HospitalLeading Edge Endowment FundTerry Fox Research InstituteGenome British ColumbiaSkolkovo Institute of Science and TechnologyGenome CanadaMcGill University
KeywordsSupermoleculeChemistryPhosphocholineAmmoniumCovalent bondMALDI imagingCholineSelectivityMass spectrometryMatrix-assisted laser desorption/ionizationOrganic chemistryMoleculeChromatographyBiochemistryPhospholipidPhosphatidylcholine

Abstract

fetched live from OpenAlex

Cucurbit[7]uril was used to form non-covalent complexes with low-molecular-weight quaternary-ammonium compounds for their indirect analysis by MALDI-MS. By shifting the ion signals to a higher and interference-free mass region, the distributions of neurine, choline, and phosphocholine in rat brain were visualized by MALDI imaging with high selectivity and good sensitivity.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.239
Teacher spread0.231 · 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 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

Citations3
Published2020
Admission routes2
Has abstractyes

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