{"id":201,"date":"2023-08-24T13:46:10","date_gmt":"2023-08-24T16:46:10","guid":{"rendered":"https:\/\/pdbguilherme.com\/?page_id=201"},"modified":"2023-08-24T15:30:57","modified_gmt":"2023-08-24T18:30:57","slug":"portfolio","status":"publish","type":"page","link":"https:\/\/pdbguilherme.com\/?page_id=201","title":{"rendered":"Portf\u00f3lio"},"content":{"rendered":"\n

Gr\u00e1ficos, Mapas e Diagramas<\/h2>\n\n\n\n
\n
\n
\"\"
Fig 1. Schematic diagram of integrative taxonomy applied to Ophiothrix<\/em> spp. (A) A priori<\/em> classification inferred from morphological characters. (B) Morphological characters (B1) external morphology and (B2) arm microstructures morphology. (C) Morphometry: measurements and linear discriminant analysis (LDA). (D) Molecular characters: (D1) phylogenetic analyses (Maximum Parsimony, Bayesian Inference, and Maximum Likelihood) and (D2) genetic diversity and species delimitation test (genetic distances, AMOVA, haplotype network, bPTP). (E) Congruence framework of integrative taxonomy.
https:\/\/doi.org\/10.1371\/journal.pone.0210331.g001<\/a><\/figcaption><\/figure>\n<\/div>\n\n\n\n
\n
\"\"
Fig 3. Axes 1 and 2 from linear discriminant analysis (LDA) based on 17 brittle stars\u2019 characters. https:\/\/doi.org\/10.1371\/journal.pone.0210331.g003<\/a><\/figcaption><\/figure>\n<\/div>\n<\/div>\n\n\n\n
\n
\n
\"\"
Figure 5. Geographical distribution of Plagiobrissus grandis <\/em>in America. The dots represent the previous and new records of the species. Map background generated with package \u201cggmap\u201d using Google Maps API. http:\/\/dx.doi.org\/10.11606\/1807-0205\/2023.63.026<\/a><\/figcaption><\/figure>\n<\/div>\n\n\n\n
\n
\"\"
Fig. 3. Schematic diagram of the statistical analysis using the machine learning approach. Step A: A priori classification of quadrat images into one of six sub\u0002environments: mesolittoral (ML), drift zone (DZ), supralittoral (SL), incipient foredunes (IF), established foredunes (EF), and restinga (RE). Step B\u2013D: Pre-processing of numeric predictor variables to select the final model: sub-environments \u223c sector (SEC) + local slope (SLO) + segmented cover vegetation (SVC) + drift cover rate (DCR) + entropy of texture (ENT) + average vegetation height (AVH) + number of sites with sand or other marine litter (NSC) + number of points with Ipomoea pes-caprae (ipe) + number of points with Blutaparon portulacoides (blpo) + number of points with Panicum racemosum (pan) + number of points with herbaceous plants (herb) + number of points with grass plants (grass) + species richness (S). Step E: Implementation of the candidate machine learning algorithms: classifi\u0002cation and regression trees (rpart), random forests (RF), stochastic gradient boosting (GBM), support vector machines with radial basis function kernel (SVM), C5.0 decision trees and rule-based models (C50), penalized multinomial regression (MREG), variational Bayesian multinomial probit regression (VBMP). Step F\u2013H: Computation of statistics to aid selection of the best algorithm. Step I: Assessment of the results from the best-performing machine learning algorithm. http:\/\/dx.doi.org\/10.1016\/j.ecss.2018.04.030<\/a><\/figcaption><\/figure>\n<\/div>\n<\/div>\n\n\n\n

Fotos<\/h2>\n\n\n\n
\n
\n
\"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n
\n
\n
\"\"<\/figure>\n<\/div>\n\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n\n
\n
\"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n
\n
\n
\"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n
\n
\n
\"\"<\/figure>\n<\/div>\n\n\n\n
\n
\"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n
\n
\n
\"\"<\/figure>\n<\/div>\n\n\n\n
\n
\"\"<\/figure>\n<\/div>\n\n\n\n
\n
\"\"<\/figure>\n<\/div>\n<\/div>\n\n\n\n
\n
\n
\"\"<\/figure>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

Gr\u00e1ficos, Mapas e Diagramas Fotos<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_uag_custom_page_level_css":"","footnotes":""},"class_list":["post-201","page","type-page","status-publish","hentry"],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"pdbguilherme","author_link":"https:\/\/pdbguilherme.com\/?author=1"},"uagb_comment_info":0,"uagb_excerpt":"Gr\u00e1ficos, Mapas e Diagramas Fotos","_links":{"self":[{"href":"https:\/\/pdbguilherme.com\/index.php?rest_route=\/wp\/v2\/pages\/201","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pdbguilherme.com\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/pdbguilherme.com\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/pdbguilherme.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/pdbguilherme.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=201"}],"version-history":[{"count":13,"href":"https:\/\/pdbguilherme.com\/index.php?rest_route=\/wp\/v2\/pages\/201\/revisions"}],"predecessor-version":[{"id":240,"href":"https:\/\/pdbguilherme.com\/index.php?rest_route=\/wp\/v2\/pages\/201\/revisions\/240"}],"wp:attachment":[{"href":"https:\/\/pdbguilherme.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=201"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}