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Uncertainties and Ambiguities in Percentiles and how to Avoid Them

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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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Michael Schreiber

Other Statistics · Submitted 16 May 2012

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

The authors' main claim It is shown that in this way uncertainties and ambiguities in the evaluation of percentile ranks do not occur.

how it is stated · asserted 1.00 read the record →

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arXiv ID1205.3588 (version 1)
Pith IDLKK4Z2U5, Pith's permanent record of this paper and its review
CitationMichael Schreiber. "Uncertainties and Ambiguities in Percentiles and how to Avoid Them." arXiv:1205.3588 (2012). https://pith.science/paper/LKK4Z2U5
Claim paper

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.

Open the compiled record

Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,828 input tokens, $0.0001 reported.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.06 · human participants 0.03 · controlled comparison 0.04 · randomized assignment 0.02 · states quantitative result 0.23 · compares with prior results 0.29 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

The recently proposed fractional scoring scheme is used to attribute publications to percentile rank classes. It is shown that in this way uncertainties and ambiguities in the evaluation of percentile ranks do not occur. Using the fractional scoring the total score of all papers exactly reproduces the theoretical value.

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 The recently proposed fractional scoring scheme is used to attribute publications to percentile rank classes. method 0.58 0.15 0.06 asserted 1.00
s1 It is shown that in this way uncertainties and ambiguities in the evaluation of percentile ranks do not occur. result 0.94 0.84 0.78 asserted 1.00
s2 Using the fractional scoring the total score of all papers exactly reproduces the theoretical value. result 1.00 0.80 0.26 asserted 1.00

What kind of paper the classifier reports this is: theoretical result 0.53 · evidence from proof or derivation 0.84.

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

The recently proposed fractional scoring scheme is used to attribute publications to percentile rank classes. It is shown that in this way uncertainties and ambiguities in the evaluation of percentile ranks do not occur. Using the fractional scoring the total score of all papers exactly reproduces the theoretical value.

Human review

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