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Record W2027266970 · doi:10.1002/qua.560280824

Configuration selection in the MCSCF method. I. Application to the B1∑+ state of HF

2009· article· en· W2027266970 on OpenAlexafffund
Katherine Valenta Darvesh, Friedrich Grein

Bibliographic record

VenueInternational Journal of Quantum Chemistry · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsUniversity of New BrunswickDalhousie University
FundersMedical Research CouncilNatural Sciences and Engineering Research Council of Canada
KeywordsWave functionSelection (genetic algorithm)Excited stateState (computer science)ChemistryAtomic physicsComputational chemistryConfiguration interactionStatistical physicsPhysicsComputer scienceAlgorithmArtificial intelligence

Abstract

fetched live from OpenAlex

Due to the relatively small number of configurations used in MC wavefunctions, proper selection of configurations is essential. Systematic selection methods, allowing for the anticipated changes in orbital characteristics, are developed for an excited state system, the B1∑+ state of HF. Configurations qualifying for inclusion into the MC wavefunction must have either a large coefficient in CI trial functions, or a large energy lowering relative to a few reference configurations. The MCSCF results for the B1∑+ state of HF compare well with CI results obtained here and obtained previously by other authors.

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: none
Teacher disagreement score0.549
Threshold uncertainty score0.200

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 science0.0000.000
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 head0.009
GPT teacher head0.319
Teacher spread0.310 · 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

Citations1
Published2009
Admission routes2
Has abstractyes

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