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Record W2088063275 · doi:10.1002/mrc.1453

A relativistic DFT study of one-bond fluorine-X indirect spin–spin coupling tensors

2004· article· en· W2088063275 on OpenAlexaff
Kirk W. Feindel, Roderick E. Wasylishen

Bibliographic record

VenueMagnetic Resonance in Chemistry · 2004
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicElectron Spin Resonance Studies
Canadian institutionsUniversity of Alberta
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsChemistrySpin (aerodynamics)AnisotropyTensor (intrinsic definition)DipoleCoupling constantQuantum chemicalCoupling (piping)FluorineQuantum chemistryMagnetic dipole–dipole interactionComputational chemistryRelativistic quantum chemistryQuantum mechanicsMolecular physicsMoleculeThermodynamicsPhysicsOrganic chemistryGeometry

Abstract

fetched live from OpenAlex

The relativistic zeroth-order regular approximation (ZORA) DFT method was employed to investigate indirect spin-spin coupling tensors involving fluorine, (1)J(X, F). The relative contributions of the mechanisms contributing to (1)J(X, F) are discussed, with special attention paid to the magnitude and origin of the anisotropy in this tensor, DeltaJ. This quantum chemical study demonstrates that, for the systems investigated, the ZORA-DFT method reproduces the magnitude of (1)J(X, F)(iso) and indicates that DeltaJ(X, F) is of the same order of magnitude as (1)J(X, F)(iso). Several examples are provided that demonstrate the importance of considering contributions of DeltaJ to the experimental measurement of effective dipolar coupling constants, R(eff). Given the difficulties with determining DeltaJ experimentally and the promising computational results, we suggest that the quantum chemical calculation of (1)J(X, F) be used as a complementary tool to aid in the analysis of data from NMR experiments designed to measure dipolar coupling constants.

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 categoriesMeta-epidemiology (narrow)
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 score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.013
GPT teacher head0.269
Teacher spread0.256 · 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.

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

Citations14
Published2004
Admission routes1
Has abstractyes

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