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Record W2052995674 · doi:10.1063/1.1428752

A preconditioned inexact spectral transform method for calculating resonance energies and widths, as applied to HCO

2002· article· en· W2052995674 on OpenAlexaff
Bill Poirier, Tucker Carrington

Bibliographic record

VenueThe Journal of Chemical Physics · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsLanczos resamplingPreconditionerSolverHamiltonian (control theory)Iterative methodApplied mathematicsMathematicsEigenvalues and eigenvectorsLanczos algorithmPhysicsAlgorithmMathematical optimizationQuantum mechanics

Abstract

fetched live from OpenAlex

We present a complex-symmetric version of the preconditioned inexact spectral transform (PIST) method, for calculating resonance energies and widths. The PIST method uses an iterative linear solver to compute inexact Lanczos vectors for (EI−H)−1, and then diagonalizes the Hamiltonian in the inexact Lanczos representation. Our new version requires complex-symmetric variants of: (1) the Lanczos algorithm, (2) the linear solver, (3) the preconditioner we introduced in a previous paper [J. Chem. Phys. 114, 9254 (2001)]. The new method works extremely well for HCO, enabling us to calculate the first 17 dissociative resonances in less then 90 second of CPU time.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.275
Teacher spread0.261 · 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
GenreMethods

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

Citations77
Published2002
Admission routes1
Has abstractyes

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