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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/3C63EHP
Repositorydpi.inpe.br/plutao/2012/06.21.17.23
Last Update2012:08.10.18.50.22 (UTC) administrator
Metadata Repositorydpi.inpe.br/plutao/2012/06.21.17.23.40
Metadata Last Update2018:06.05.00.01.43 (UTC) administrator
DOIS0102-311X2012000500017
ISSN0102-311X
Labellattes: 0271662670324136 1 CamargoShMo:2012:ExEU
Citation KeyCamargoKiPiShRiMo:2012:ExEUPr
TitleProposta sobre uso de dados de receitas de antimicrobianos retidas: a experiência EUREQA / A proposal for using data from antimicrobial prescriptions: the EUREQA experience
Year2012
MonthMay
Access Date2023, Jan. 31
Secondary TypePRE PN
Number of Files1
Size869 KiB
2. Context
Author1 Camargo, Eduardo Celso Gerbi
2 Kiffer, Caros Roberto Veiga
3 Pignatari, Antonio Carlos Campos
4 Shimakura, Silvia Emiko
5 Ribeiro Jr., Paulo Justiniano
6 Monteiro, Antonio Miguel Vieira
Resume Identifier1 8JMKD3MGP5W/3C9JGUK
2
3
4
5
6 8JMKD3MGP5W/3C9JGJN
Group1 DPI-OBT-INPE-MCTI-GOV-BR
2
3
4
5
6 DPI-OBT-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Escola Paulista de Medicina, Universidade Federal de São Paulo
3 Escola Paulista de Medicina, Universidade Federal de São Paulo
4 Laboratório de Estatística e Geoinformação, Universidade Federal do Paraná, Curitiba
5 Laboratório de Estatística e Geoinformação, Universidade Federal do Paraná, Curitiba
6 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 eduardo@dpi.inpe.br
e-Mail Addresseduardo@dpi.inpe.br
JournalCadernos de Saúde Pública
Volume28
Number5
Pages985-990
Secondary MarkA1_ADMINISTRAÇÃO,_CIÊNCIAS_CONTÁBEIS_E_TURISMO A1_ANTROPOLOGIA_/_ARQUEOLOGIA B1_ARQUITETURA_E_URBANISMO B4_BIOTECNOLOGIA B4_CIÊNCIA_DA_COMPUTAÇÃO B4_CIÊNCIA_DE_ALIMENTOS B1_CIÊNCIA_POLÍTICA_E_RELAÇÕES_INTERNACIONAIS B5_CIÊNCIAS_AGRÁRIAS_I B4_CIÊNCIAS_BIOLÓGICAS_I C_CIÊNCIAS_BIOLÓGICAS_II B5_CIÊNCIAS_BIOLÓGICAS_III B1_CIÊNCIAS_SOCIAIS_APLICADAS_I A2_DIREITO B3_ECOLOGIA_E_MEIO_AMBIENTE B2_ECONOMIA B2_EDUCAÇÃO B1_EDUCAÇÃO A2_ENFERMAGEM B4_ENGENHARIAS_I B2_ENGENHARIAS_II B2_ENGENHARIAS_III B3_ENGENHARIAS_IV B2_ENSINO_DE_CIÊNCIAS_E_MATEMATICA B3_FARMÁCIA B2_GEOCIÊNCIAS B1_GEOGRAFIA B1_HISTÓRIA B1_INTERDISCIPLINAR B3_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B3_MEDICINA_I B3_MEDICINA_II B3_MEDICINA_III B2_MEDICINA_VETERINÁRIA B1_ODONTOLOGIA A2_PLANEJAMENTO_URBANO_E_REGIONAL_/_DEMOGRAFIA A2_PSICOLOGIA C_QUÍMICA A2_SAÚDE_COLETIVA A2_SERVIÇO_SOCIAL B1_SOCIOLOGIA B3_ZOOTECNIA_/_RECURSOS_PESQUEIROS
History (UTC)2012-06-22 00:10:59 :: lattes -> administrator :: 2012
2012-07-17 17:49:17 :: administrator -> secretaria.cpa@dir.inpe.br :: 2012
2012-12-21 17:28:59 :: secretaria.cpa@dir.inpe.br -> administrator :: 2012
2018-06-05 00:01:43 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsPrescrições de Medicamentos
Antibacterianos
Resistência Microbiana
Drug Prescriptions
Anti-bacterial Agents
Microbial Drug Resistance resistance a Medicamentos
AbstractA presente nota pesquisa demonstra que o uso das informações de receituário ou prescrição médica tem fundamental valor para a compreensão das correlações da dinâmica da resistência bacteriana comunitária. Além disso, a análise dos dados gerada pode ajudar a estabelecer medidas e políticas de saúde pública mais adequadas para o controle e a otimização do consumo de antimicrobianos. Para isso, o artigo usa como base o modelo lógico desenvolvido pelo Projeto EUREQA voltado para aquisição, classificação, interpretação e análise das informações relacionadas à prescrição dos antimicrobianos de uso oral.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Proposta sobre uso...
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agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/J8LNKAN8RW/3C63EHP
zipped data URLhttp://urlib.net/zip/J8LNKAN8RW/3C63EHP
Languagept
Target FileCamargo_ECG.pdf
User Groupadministrator
lattes
secretaria.cpa@dir.inpe.br
Visibilityshown
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Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
URL (untrusted data)http://www.scielo.br/scielo.php?script=sci_abstract&pid=S0102-311X2012000500017&lng=pt&nrm=iso&tlng=pt
DisseminationWEBSCI; PORTALCAPES; SCIELO.
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
NotesSetores de Atividade: Atividades de atenção à saúde humana, Pesquisa e desenvolvimento científico.
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel format isbn lineage mark mirrorrepository nextedition orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission rightsholder secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype typeofwork
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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
IdentifierJ8LNKAN8RW/3D545NE
Repositorydpi.inpe.br/plutao/2012/11.28.19.14.54
Last Update2013:01.17.11.09.56 (UTC) marciana
Metadata Repositorydpi.inpe.br/plutao/2012/11.28.19.14.55
Metadata Last Update2018:06.05.00.02.13 (UTC) administrator
Secondary KeyINPE--PRE/
DOI10.1590/S0100-204X2012000900004
ISSN0100-204X
Labellattes: 9840759640842299 7 LuBLMHFDS:2012:LaUsCl
Citation KeyLuBLMHFDS:2012:LaUsCl
TitleLand use/cover classification in the Brazilian Amazon using satellite images / Classificação de uso e cobertura da terra na Amazônia brasileira por meio de imagens de satélite
Year2012
MonthSet.
Access Date2023, Jan. 31
Type of Workjournal article
Secondary TypePRE PN
Number of Files1
Size7870 KiB
2. Context
Author1 Lu, Dengsheng
2 Batistela, Mateus
3 Li, Guiying
4 Moran, Emilio
5 Hetrick, Scott
6 Freitas, Corina da Costa
7 Dutra, Luciano Vieira
8 Sant'anna, Sidnei João Siqueira
Resume Identifier1
2
3
4
5
6
7 8JMKD3MGP5W/3C9JHMA
8 8JMKD3MGP5W/3C9JJ8N
Group1
2
3
4
5
6 DPI-OBT-INPE-MCTI-GOV-BR
7 DPI-OBT-INPE-MCTI-GOV-BR
8 DPI-OBT-INPE-MCTI-GOV-BR
Affiliation1 Indiana University, Anthropological Center for Training and Research on Global Environmental Change, Student Building 331, 701 East Kirkwood Avenue, Bloomington, Indiana, 47405, USA
2 Embrapa Monitoramento por Satélite, Avenida Soldado Passarinho, nº 303, CEP 13070‑115 Campinas, SP, Brazil. E‑mail: mb@cnpm.embrapa.br
3 Indiana University, Anthropological Center for Training and Research on Global Environmental Change, Student Building 331, 701 East Kirkwood Avenue, Bloomington, Indiana, 47405, USA
4 Indiana University, Anthropological Center for Training and Research on Global Environmental Change, Student Building 331, 701 East Kirkwood Avenue, Bloomington, Indiana, 47405, USA
5 Indiana University, Anthropological Center for Training and Research on Global Environmental Change, Student Building 331, 701 East Kirkwood Avenue, Bloomington, Indiana, 47405, USA
6 Instituto Nacional de Pesquisas Espaciais (INPE)
7 Instituto Nacional de Pesquisas Espaciais (INPE)
8 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1
2
3
4
5
6
7 dutra@dpi.inpe.br
e-Mail Addressdutra@dpi.inpe.br
JournalPesquisa Agropecuária Brasileira
Volume47
Number9
Pages1185-1208
Secondary MarkB1_ARQUITETURA_E_URBANISMO B5_ASTRONOMIA_/_FÍSICA B4_BIOTECNOLOGIA B2_CIÊNCIA_DE_ALIMENTOS B1_CIÊNCIAS_AGRÁRIAS_I B1_CIÊNCIAS_BIOLÓGICAS_I B5_CIÊNCIAS_BIOLÓGICAS_II B2_ECOLOGIA_E_MEIO_AMBIENTE B1_ENGENHARIAS_I B2_ENGENHARIAS_II B1_ENGENHARIAS_III B1_ENGENHARIAS_IV B2_GEOCIÊNCIAS B1_GEOGRAFIA A2_INTERDISCIPLINAR B2_MEDICINA_II B1_MEDICINA_VETERINÁRIA B4_QUÍMICA B2_SAÚDE_COLETIVA B1_ZOOTECNIA_/_RECURSOS_PESQUEIROS
History (UTC)2012-11-28 23:06:35 :: lattes -> marciana :: 2012
2013-01-17 11:11:07 :: marciana -> administrator :: 2012
2016-06-04 01:08:19 :: administrator -> marciana :: 2012
2016-08-29 18:51:54 :: marciana -> administrator :: 2012
2018-06-05 00:02:13 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsdata fusion
multiple sensor data
nonparametric classifiers
texture
fusão de dados
dados de sensor múltiplo
classificadores não paramétricos
textura
AbstractLand use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation‑based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi‑resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Among the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, have the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical‑based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. RESUMO A classificação de uso e cobertura da terra é uma das principais aplicações do sensoriamento remoto. Contudo, a precisão no mapeamento da distribuição espacial do uso/cobertura da terra é um desafio, principalmente em regiões tropicais úmidas, em razão do complexo ambiente biofísico e das limitações dos dados de sensoriamento remoto per se. Este trabalho revisa experimentos relacionados à classificação do uso/cobertura da terra na Amazônia brasileira, durante uma década. A partir de análise compreensiva dos resultados de classificação, conclui-se que a informação espacial, em dados de sensoriamento remoto, tem papel fundamental na melhoria da classificação de uso/cobertura da terra. A incorporação de imagens de textura, em bandas multiespectrais, e o uso de método baseado em segmentação são formas importantes de melhorar a classificação, especialmente para imagens de alta resolução espacial. A fusão de dados de imagens de resolução múltipla dentro de dados do sensor ótico é vital para a interpretação visual, mas pode não melhorar o desempenho da classificação. Em contraste, a integração de dados ópticos e de radar melhorou o desempenho da classificação, quando o método adequado de fusão de dados foi utilizado. Entre os algoritmos de classificação disponíveis, o classificador de máxima verossimilhança ainda é importante para se obter precisão razoável, mas algoritmos não paramétricos, como a análise por árvore de decisão, podem promover melhores resultados. Porém, algoritmos não paramétricos geralmente demandam mais tempo para obtenção da parametrização otimizada. O uso adequado de métodos baseados em hierarquia é fundamental para a precisão na classificação de uso/cobertura da terra, sobretudo em dados de sensoriamento remoto antigos.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Land use/cover classification...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
data URLhttp://urlib.net/ibi/J8LNKAN8RW/3D545NE
zipped data URLhttp://urlib.net/zip/J8LNKAN8RW/3D545NE
Languagept
User Groupadministrator
lattes
marciana
Reader Groupadministrator
marciana
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Read Permissionallow from all
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3EQCCU5
DisseminationWEBSCI; PORTALCAPES; SCIELO.
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
NotesSetores de Atividade: Administração pública, defesa e seguridade social, Outras atividades profissionais, científicas e técnicas.
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel format isbn lineage mark mirrorrepository nextedition orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder secondarydate session shorttitle sponsor subject targetfile tertiarymark tertiarytype url
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1. Identity statement
Reference TypeJournal Article
Siteplutao.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W/3FCLN2N
Repositorysid.inpe.br/plutao/2013/12.12.18.59.44   (restricted access)
Last Update2014:01.17.13.02.21 (UTC) administrator
Metadata Repositorysid.inpe.br/plutao/2013/12.12.18.59.45
Metadata Last Update2021:03.05.23.11.17 (UTC) administrator
ISSN1807-4545
Labellattes: 8594179234801599 3 PantaleãoDutrSand:2013:ScAnIm
Citation KeyPantaleãoDutrSand:2012:ScAnIm
TitleScenario analysis for image classification using multi-objective optimization
Year2012
Monthset.-dez.
Access Date2023, Jan. 31
Secondary TypePRE PN
Number of Files1
Size1607 KiB
2. Context
Author1 Pantaleão, Eliana
2 Dutra, Luciano Vieira
3 Sandri, Sandra Aparecida
Resume Identifier1
2 8JMKD3MGP5W/3C9JHMA
Group1
2 DPI-OBT-INPE-MCTI-GOV-BR
3 LAC-CTE-INPE-MCTI-GOV-BR
Affiliation1
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1
2
3 sandri.at.lac.inpe.br@gmail.com
e-Mail Addresssandri.at.lac.inpe.br@gmail.com
JournalInfoComp
Volume11
Number3
Pages15-22
Secondary MarkC_CIÊNCIA_DA_COMPUTAÇÃO C_CIÊNCIAS_AGRÁRIAS_I B5_CIÊNCIAS_BIOLÓGICAS_I B5_ENGENHARIAS_III B5_ENGENHARIAS_IV B3_INTERDISCIPLINAR B4_MATERIAIS
History (UTC)2013-12-12 18:59:45 :: lattes -> administrator ::
2014-01-17 13:02:22 :: administrator :: 2013 -> 2012
2021-03-05 23:11:17 :: administrator -> marcelo.pazos@inpe.br :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsIn a typical image classification task
the analyst decides beforehand the number of classes and which image channels to use. If there is a need to modify the classes or data channels
it is necessary to start over. This paper proposes a scenario analysis tool for the task of image classification as a way of automating this process. Each scenario represents the parameters that will be used in a complete supervised classification task
including training and classification. The proposed method uses multi-objective optimization to evaluate different sets of attributes and classes
and presents the compromising solutions
regarding the user objectives. A class hierarchy structure is used to generate different class sets
and the system attempts to find the most appropriate combinations of class and attribute sets. In this work
the system is applied to remote sensing problems and we consider three objectives: the best classification accuracy
the smallest attribute set and the biggest class set. The system shows the compromising combinations of class and attribute sets
along with the accuracy on a testing sample. The user can then choose which combination to use for the image classification
AbstractIn a typical image classification task, the analyst decides beforehand the number of classes and which image channels to use. If there is a need to modify the classes or data channels, it is necessary to start over. This paper proposes a scenario analysis tool for the task of image classification as a way of automating this process. Each scenario represents the parameters that will be used in a complete supervised classification task, including training and classification. The proposed method uses multi-objective optimization to evaluate different sets of attributes and classes, and presents the compromising solutions, regarding the user objectives. A class hierarchy structure is used to generate different class sets, and the system attempts to find the most appropriate combinations of class and attribute sets. In this work, the system is applied to remote sensing problems and we consider three objectives: the best classification accuracy, the smallest attribute set and the biggest class set. The system shows the compromising combinations of class and attribute sets, along with the accuracy on a testing sample. The user can then choose which combination to use for the image classification.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Scenario analysis for...
Arrangement 2urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Scenario analysis for...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Contentthere are no files
4. Conditions of access and use
Languagept
User Grouplattes
marcelo.pazos@inpe.br
Reader Groupadministrator
marcelo.pazos@inpe.br
Visibilityshown
Archiving Policydenypublisher denyfinaldraft
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Mirror Repositoryiconet.com.br/banon/2006/11.26.21.31
Next Higher Units8JMKD3MGPCW/3EQCCU5
8JMKD3MGPCW/3ESGTTP
URL (untrusted data)http://www.dcc.ufla.br/infocomp/index.php?option=com_content&view=article&id=530&Itemid=216
DisseminationWEBSCI; PORTALCAPES.
Host Collectiondpi.inpe.br/plutao@80/2008/08.19.15.01
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel doi format isbn lineage mark nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder secondarydate secondarykey session shorttitle sponsor subject targetfile tertiarymark tertiarytype typeofwork
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1. Identity statement
Reference TypeJournal Article
Sitemtc-m16d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP7W/3CDQ3LB
Repositorysid.inpe.br/mtc-m19/2012/08.08.11.21
Last Update2012:08.31.18.57.22 (UTC) administrator
Metadata Repositorysid.inpe.br/mtc-m19/2012/08.08.11.21.20
Metadata Last Update2020:10.01.15.58.02 (UTC) administrator
Secondary KeyINPE--PRE/
ISSN1413-4853
Citation KeySartoriImaiMuraTach:2012:RaImPo
TitleRadar image polarimetric attributes for the inference of macrophyte morphologic parameters
ProjectFAPESP 2008/07537-1, PROCAD/CAPES 0258059
Year2012
MonthJan.-Mar.
Access Date2023, Jan. 31
Secondary TypePRE PN
Number of Files1
Size834 KiB
2. Context
Author1 Sartori, LR
2 Imai, Nilton Nobuhiro
3 Mura, Jose Claudio
4 Tachibana, Vilma Mayumi
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JHGR
Group1
2
3 DPI-OBT-INPE-MCTI-GOV-BR
Affiliation1 Univ Estadual Paulista, Programa Posgrad Ciencias Cartog
2 Univ Estadual Paulista, Programa Posgrad Ciencias Cartog
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Univ Estadual Paulista, Programa Posgrad Ciencias Cartog
JournalBoletim de Ciências Geodésicas
Volume18
Number1
Pages138 -153
History (UTC)2012-08-31 18:57:30 :: marciana -> banon :: 2012
2012-09-24 19:29:25 :: banon -> administrator :: 2012
2020-10-01 15:58:02 :: administrator -> marciana :: 2012
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsPolarimetric Radar
PALSAR data
Multiple Linear Regression
Macrophyte Monitoring
Amazon Floodplain eastern amazon floodplain
vegetation
decomposition
productivity
emissions
AbstractThis work aims at modelling the morphological variable steam-volume of the macrophyte species as a function of the attributes derived from the ALOS/PALSAR polarimetric data using multiple regression technique. The study was carried out at Monte Alegre Lake, in the Amazon floodplain area, Para State, Brazil. The modeled variable steam-volume was evaluated and the contribution of the phase information from the radar data was highlighted. The fieldwork was performed almost simultaneously to the radar acquisition. Macrophyte morphological variables were measured in the field and used to derive the stem-volume model regarding the attributes generated from the radar data. Although the adjusted coefficient of determination was not high (R-aj(2) = 44%), the presented predictive ability and all validation elements were expected with 95% of confidence. Among the five independent variables of the model, four were generated from the phase information, which brings about this reliable information.
AreaCEA
Arrangementurlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDPI > Radar image polarimetric...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
agreement.html 08/08/2012 08:21 1.0 KiB 
4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGP7W/3CDQ3LB
zipped data URLhttp://urlib.net/zip/8JMKD3MGP7W/3CDQ3LB
Languageen
User Groupadministrator
banon
marciana
Reader Groupadministrator
marciana
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Read Permissionallow from all
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/mtc-m19@80/2009/08.21.17.02.53
Next Higher Units8JMKD3MGPCW/3EQCCU5
DisseminationWEBSCI; PORTALCAPES; SCIELO.
Host Collectionsid.inpe.br/mtc-m19@80/2009/08.21.17.02
6. Notes
Empty Fieldsalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel doi e-mailaddress electronicmailaddress format isbn label lineage mark nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress rightsholder secondarydate secondarymark session shorttitle sponsor subject targetfile tertiarymark tertiarytype typeofwork url
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e-Mail (login)marciana
update