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  <titleInfo>
    <title>Partial Volume Prediction Through Nonlinear Mixed Modeling</title>
  </titleInfo>
  <name type="personal">
    <namePart>Nicoletti, Marcos Felipe</namePart>
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      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Carvalho, Samuel de Pádua Chaves e</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Machado, Sebastião do Amaral</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Figueiredo Filho, Afonso</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Oliveira, Gustavo Silva</namePart>
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    </role>
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  <abstract>

ABSTRACT

The objective of this study was to assess the prediction of partial volumes with nonlinear mixed
modeling for Pinus taeda. The volume of 558 trees was measured. The four-parameter logistic
model was used in its modified form for the nonlinear mixed approach and, for comparison, the
5th degree polynomial was used. In the mixed modeling, the random effects diameter, age and
place were inserted. The statistical criteria used to assess the quality of the adjustment were the
Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), standard error
of the estimate (Syx) and residual graphical analysis. Among the random effects analyzed, age
obtained the best adjustment. However, to predict partial volumes, it was noticed that, regardless
of the analyzed portion of the trunk, the 5th degree polynomial had the best estimates, with a
mean standard error of 20.1% of the estimate compared to 51.8% of the logistic.

Keywords: forest biometrics, logistic model, taper.</abstract>
  <note>Publicação on-line; 26 ref.; 7 tables; 2 illus.; Summary (En)</note>
  <subject>
    <topic>PINUS TAEDA</topic>
  </subject>
  <subject>
    <topic>BIOMETRIA</topic>
  </subject>
  <subject>
    <topic>FLORESTA</topic>
  </subject>
  <subject>
    <topic>ANÁLISE ESTATÍSTICA</topic>
  </subject>
  <relatedItem type="host">
    <titleInfo>
      <title>Floresta e Ambiente (Brazil)</title>
    </titleInfo>
    <originInfo>
      <publisher>Rio de Janeiro-RJ Instituto de Florestas - UFRRJ 1994</publisher>
    </originInfo>
    <identifier>2025-0452</identifier>
    <identifier type="issn">1415-0980  /  ISSN 2179-8087 0nline</identifier>
    <identifier type="local">BR2026001193</identifier>
    <part>
      <text>v. 26(4) p. 1-10; (2019)</text>
    </part>
  </relatedItem>
  <identifier type="uri">https://www.scielo.br/j/floram/a/CkHrFYG4RgK5kJygRkGDttF/?format=pdf&amp;lang=en</identifier>
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    <url>https://www.scielo.br/j/floram/a/CkHrFYG4RgK5kJygRkGDttF/?format=pdf&amp;lang=en</url>
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