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Record W2492265198 · doi:10.1139/cjc-2016-0282

Structural features of the carbon–sulfur chemical bond: a semi-experimental perspective

2016· article· en· W2492265198 on OpenAlexvenueno aff
Emanuele Penocchio, Marco Mendolicchio, Nicola Tasinato, Vincenzo Barone

Bibliographic record

VenueCanadian Journal of Chemistry · 2016
Typearticle
Languageen
FieldChemistry
TopicFree Radicals and Antioxidants
Canadian institutionsnot available
FundersEuropean Commission
KeywordsChemistrySulfurMoleculeDensity functional theoryCarbon fibersComputational chemistryWork (physics)Field (mathematics)Biochemical engineeringChemical physicsThermodynamicsAlgorithmOrganic chemistryComputer science

Abstract

fetched live from OpenAlex

In this work semi-experimental and theoretical equilibrium geometries of 10 sulfur-containing organic molecules, as well as 4 oxygenated ones, are determined by means of a computational protocol based on density functional theory. The results collected in the present paper further enhance our online database of accurate semi-experimental equilibrium molecular geometries, adding 13 new molecules containing up to 8 atoms, for 12 of which the first semi-experimental equilibrium structure is reported, to the best of our knowledge. We focus in particular on sulfur-containing compounds, aiming both to provide new accurate data on some rather important chemical moieties, only marginally represented in the literature of the field, and to examine the structural features of carbon-sulfur bonds in the light of the previously presented linear regression approach. The structural changes issuing from substitution of oxygen by sulfur are discussed to get deeper insights on how modifications in electronic structure and nuclear potential can affect equilibrium geometries. With respect to our previous works, we perform non-linear constrained optimizations of equilibrium SE structures with a new general and user-friendly software under development in our group with updated definition of useful statistical indicators.

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 score0.022
Threshold uncertainty score0.986

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 science0.0010.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.210
Teacher spread0.205 · 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

Citations58
Published2016
Admission routes1
Has abstractyes

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