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Record W1999527292 · doi:10.1063/1.1385364

<i>Ab initio</i> multireference configuration interaction study of the electronic spectra of carbon chain anions C2n+1− (n=2–5)

2001· article· en· W1999527292 on OpenAlexaff
Zexing Cao, Sigrid D. Peyerimhoff, Fritz Grein, Qianer Zhang

Bibliographic record

VenueThe Journal of Chemical Physics · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMultireference configuration interactionExcited stateAb initioChemistryCarbon chainAtomic physicsConfiguration interactionExcitationAb initio quantum chemistry methodsMatrix isolationElectronic structureSpectral lineDensity functional theoryChain (unit)Molecular physicsComputational chemistryPhysicsMoleculeQuantum mechanics

Abstract

fetched live from OpenAlex

Multireference configuration interaction calculations on the vertical transition energies of the low-lying excited states in carbon chain anions C2n+1− (n=2–5) are carried out. Calculated vertical term energies confirm the previously suggested assignments to the first and second Π2←X2Π band systems in matrix isolation spectroscopy. The lowest 1 2Π←X 2Π electronic transitions of C5−, C7−, C9−, and C11−, are calculated at 2.66, 2.27, 1.90, and 1.54 eV, respectively, with large oscillator strengths. Relevant theoretical evidences for a more detailed assignment of the observed transitions are presented. Generally, corresponding excitation energies in the C2n+1− carbon chain anions are found descending with an increase of the chain. The equilibrium geometries and harmonic vibrational frequencies of these linear carbon chains determined by the density functional theory approach also show reasonable agreement with available experimental data.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.017
GPT teacher head0.269
Teacher spread0.252 · 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 designSimulation or modeling
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

Citations15
Published2001
Admission routes1
Has abstractyes

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