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Record W2936767888 · doi:10.1002/jms.4359

Assigning the ESI mass spectra of organometallic and coordination compounds

2019· article· en· W2936767888 on OpenAlex

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

VenueJournal of Mass Spectrometry · 2019
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaSan Francisco State University
KeywordsChemistryMass spectrumGroup 2 organometallic chemistryCoordination complexOrganometallic chemistryMass spectrometryCombinatorial chemistryComputational chemistryStereochemistryOrganic chemistryChromatographyMoleculeCatalysisMetal

Abstract

fetched live from OpenAlex

Electrospray ionization mass spectrometry (ESI-MS) is a useful technique for solving organometallic and coordination chemistry characterization problems that are difficult to address using traditional methods. However, assigning the ESI mass spectra of such compounds can be challenging, and the considerations involved in doing so are quite different from assigning the mass spectra of purely organic samples. This is a tutorial article for organometallic/coordination chemists using ESI-MS to analyze pure compounds or reaction mixtures. The fundamentals of assigning ESI mass spectra are discussed within the context of organometallic and coordination systems. The types of ions commonly observed by ESI-MS are categorized and described. Finally, a step-by-step guide for the assignment of organometallic and coordination chemistry ESI mass spectra is provided along with two case studies.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.101
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.235
Teacher spread0.226 · 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