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Record W2951715160 · doi:10.1063/1.5064456

Potential envelope theory and the local energy theorem

2019· article· en· W2951715160 on OpenAlexafffund

Bibliographic record

VenueJournal of Mathematical Physics · 2019
Typearticle
Languageen
FieldMathematics
TopicSpectral Theory in Mathematical Physics
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEigenfunctionConvexityEigenvalues and eigenvectorsBounded functionOperator (biology)Upper and lower boundsEnergy operatorEnvelope (radar)

Abstract

fetched live from OpenAlex

We consider a one-particle bound quantum mechanical system governed by a Schrödinger operator H=−Δ+v f(r), where f(r) is an attractive central potential and v > 0 is a coupling parameter. If ϕ∈D(H) is a “trial function,” the local energy theorem tells us that the discrete energies of H are bounded by the extreme values of (H ϕ)/ϕ, as a function of r. We suppose that f(r) is a smooth transformation of the form f = g(h), where g is monotone increasing with definite convexity and h(r) is a potential for which the eigenvalues Hn(u) of the operator H=−Δ+u h(r), for appropriate u > 0, are known. It is shown that the eigenfunctions of H provide local-energy trial functions ϕ which necessarily lead to finite eigenvalue approximations that are either lower or upper bounds. This is used to extend the local energy theorem to the case of upper bounds for the excited-state energies when the trial function is chosen to be an eigenfunction of such an operator H. Moreover, we prove that the local-energy approximations obtained are identical to “envelope bounds,” which can be obtained directly from the spectral data Hn(u) without explicit reference to the trial wave functions.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.012
GPT teacher head0.262
Teacher spread0.251 · 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 designTheoretical or conceptual
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
Published2019
Admission routes2
Has abstractyes

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