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Modeling and analysis of cyclic inhomogeneous Markov processes: a wind turbine case study

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Teresa Scholz, Vitor V. Lopes, Pedro Lind, Frank Raischel

Data Analysis, Statistics and Probability · Submitted 1 Apr 2014

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

The authors' main claim A method is proposed to reconstruct a cyclic time-inhomogeneous Markov pro- cess from measured data.

how it is stated · asserted 0.99 read the record →

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arXiv ID1404.0203 (version 2)
Pith IDP7XVOKSQ, Pith's permanent record of this paper and its review
CitationFrank Raischel, Pedro Lind, Teresa Scholz, Vitor V. Lopes. "Modeling and analysis of cyclic inhomogeneous Markov processes: a wind turbine case study." arXiv:1404.0203 (2014). https://pith.science/paper/P7XVOKSQ
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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, 3,475 input tokens, $0.0001 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.05 · human participants 0.03 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.05 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

A method is proposed to reconstruct a cyclic time-inhomogeneous Markov pro- cess from measured data. First, a time-inhomogeneous Markov model is fit to the data, taken here from measurements on a wind turbine. From the time-dependent transition matrices, the time-dependent Kramers-Moyal coefficients of the corresponding stochastic process are computed. Further applications of this method are discussed.

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 A method is proposed to reconstruct a cyclic time-inhomogeneous Markov pro- cess from measured data. contribution 0.99 0.83 0.33 asserted 0.99
s1 First, a time-inhomogeneous Markov model is fit to the data, taken here from measurements on a wind turbine. method 0.99 0.90 0.06 asserted 1.00
s2 From the time-dependent transition matrices, the time-dependent Kramers-Moyal coefficients of the corresponding stochastic process are computed. method 0.95 0.87 0.12 asserted 1.00
s3 Further applications of this method are discussed. future work 0.97 0.27 0.05 unclear 0.64

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from analysis of existing data 0.86.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
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

A method is proposed to reconstruct a cyclic time-inhomogeneous Markov pro- cess from measured data. First, a time-inhomogeneous Markov model is fit to the data, taken here from measurements on a wind turbine. From the time-dependent transition matrices, the time-dependent Kramers-Moyal coefficients of the corresponding stochastic process are computed. Further applications of this method are discussed.

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