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Record W2523124733 · doi:10.1039/c6sc03422e

Molecular titanium nitrides: nucleophiles unleashed

2016· article· en· W2523124733 on OpenAlexaff
Lauren N. Grant, Balázs Pintér, Takashi Kurogi, Maria E. Carroll, Gang Wu, Brian C. Manor, Patrick J. Carroll, Daniel J. Mindiola

Bibliographic record

VenueChemical Science · 2016
Typearticle
Languageen
FieldChemistry
TopicInorganic Chemistry and Materials
Canadian institutionsQueen's University
FundersChemical Sciences, Geosciences, and Biosciences DivisionJapan Society for the Promotion of ScienceOffice of ScienceBasic Energy SciencesFonds Wetenschappelijk OnderzoekUniversity of PennsylvaniaVlaamse regeringU.S. Department of Energy
KeywordsNucleophileTitaniumNitrideChemistryMaterials scienceNanotechnologyOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Reactivity studies of a rare example of a molecular titanium nitride are presented. A combination of theory and NMR spectroscopy provide a description of the bonding in the these nitrides, the role of the counter cation, K + , as well as the origin of their highly downfield 15 N NMR spectroscopic shifts.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.218
Teacher spread0.212 · 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

Citations49
Published2016
Admission routes1
Has abstractyes

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