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Record W2481616475 · doi:10.1139/cjc-2016-0291

Using the centre-of-mass of localized electron pairs to quantify electronegativity

2016· article· en· W2481616475 on OpenAlexafffundvenue
Adam J. Proud, Jason K. Pearson

Bibliographic record

VenueCanadian Journal of Chemistry · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsUniversity of Prince Edward Island
FundersNatural Sciences and Engineering Research Council of CanadaAtlantic Canada Opportunities Agency
KeywordsElectronegativityChemistryElectronValence electronAtom (system on chip)Formal chargeValence (chemistry)Atomic physicsElectron pairMolecular physicsPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

Localized electron pair densities are explored with the purpose of developing a relationship between properties of the electron pair centre-of-mass and atomic electronegativities. These electron pair centre-of-mass densities are determined for the localized molecular orbital representing the A–H bond in a compound AHn (where n = 1–4) for each of the first and second row atoms with available valence sites. By analyzing the topography of these densities, we observe the migration of the electron pair as the electronegativity of atom A changes. We demonstrate strong linear correlations between features of these centre-of-mass densities and various empirically based electronegativity scales, all of which are defined in significantly different ways. Based on strong agreement with past electronegativity scales, we propose the use of localized electron pair centre-of-mass densities as a simple and intuitive theoretical model for electronegativity. We have presented our own electronegativity scale, employing Pauling units as is common amongst many existing scales.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.014
GPT teacher head0.253
Teacher spread0.239 · 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
Published2016
Admission routes3
Has abstractyes

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