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Similarity Techniques in the Spectral Analysis of Perturbed Operator Matrices

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Anatoly G. Baskakov, Ilya A. Krishtal, Natalia B. Uskova

Functional Analysis · Submitted 26 Dec 2018

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We develop the method of similar operators to study the spectral properties of unbounded perturbed linear operators that can be represented by matrices of various kinds.

how it is stated · asserted 1.00 read the record →

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arXiv ID1812.10331 (version 1)
Pith IDANWWDMK3, Pith's permanent record of this paper and its review
CitationAnatoly G. Baskakov, Ilya A. Krishtal, Natalia B. Uskova. "Similarity Techniques in the Spectral Analysis of Perturbed Operator Matrices." arXiv:1812.10331 (2018). https://pith.science/paper/ANWWDMK3
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,266 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.07 · human participants 0.01 · controlled comparison 0.01 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.05 · mentions code or data availability 0.01.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

We develop the method of similar operators to study the spectral properties of unbounded perturbed linear operators that can be represented by matrices of various kinds. The class of operators under consideration includes various differential operators with an involution, such as one-dimensional Dirac operators of a certain type.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We develop the method of similar operators to study the spectral properties of unbounded perturbed linear operators that can be represented by matrices of various kinds. contribution 0.83 0.95 0.26 asserted 1.00
s1 The class of operators under consideration includes various differential operators with an involution, such as one-dimensional Dirac operators of a certain type. contribution 0.72 0.24 0.08 asserted 0.99

What kind of paper the classifier reports this is: method or tool 0.99 · evidence from proof or derivation 1.00.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We develop the method of similar operators to study the spectral properties of unbounded perturbed linear operators that can be represented by matrices of various kinds. The class of operators under consideration includes various differential operators with an involution, such as one-dimensional Dirac operators of a certain type.

Human review

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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 12 sentences without a confident label

Cites as background 1 paper

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