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Large Disorder Renormalization Group Study of the Anderson Model of Localization

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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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Sonika Johri, R. N. Bhatt

Disordered Systems and Neural Networks · Submitted 21 May 2014

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

The authors' main claim We show that our LDRG scheme flows to infinite disorder, and thus becomes asymptotically exact.

how it is stated · asserted 1.00 read the record →

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arXiv ID1405.5468 (version 1)
Pith ID2XHDCLC3, Pith's permanent record of this paper and its review
CitationR. N. Bhatt, Sonika Johri. "Large Disorder Renormalization Group Study of the Anderson Model of Localization." arXiv:1405.5468 (2014). https://pith.science/paper/2XHDCLC3
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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-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,579 input tokens, $0.0001 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.92 · human participants 0.01 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.15 · compares with prior results 0.14 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

We describe a large disorder renormalization group (LDRG) method for the Anderson model of localization in one dimension which decimates eigenstates based on the size of their wavefunctions rather than their energy. We show that our LDRG scheme flows to infinite disorder, and thus becomes asymptotically exact. We use it to obtain the disorder-averaged inverse participation ratio and density of states for the entire spectrum. A modified scheme is formulated for higher dimensions, which is found to be less efficient, but capable of improvement.

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 describe a large disorder renormalization group (LDRG) method for the Anderson model of localization in one dimension which decimates eigenstates based on the size of their wavefunctions rather than their energy. method 0.84 0.92 0.09 asserted 1.00
s1 We show that our LDRG scheme flows to infinite disorder, and thus becomes asymptotically exact. result 0.91 0.95 0.51 asserted 1.00
s2 We use it to obtain the disorder-averaged inverse participation ratio and density of states for the entire spectrum. result 0.65 0.96 0.09 asserted 1.00
s3 A modified scheme is formulated for higher dimensions, which is found to be less efficient, but capable of improvement. limitation 0.74 0.94 0.05 asserted 0.97

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.86.

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
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  • 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 describe a large disorder renormalization group (LDRG) method for the Anderson model of localization in one dimension which decimates eigenstates based on the size of their wavefunctions rather than their energy. We show that our LDRG scheme flows to infinite disorder, and thus becomes asymptotically exact. We use it to obtain the disorder-averaged inverse participation ratio and density of states for the entire spectrum. A modified scheme is formulated for higher dimensions, which is found to be less efficient, but capable of improvement.

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