{"id":"W2904295877","doi":"10.5102/pic.n3.2017.5774","title":"LEVANTAMENTO E CADASTRAMENTO DE PATOLOGIAS ESTRUTURAIS UTILIZANDO FOTOGRAFIAS TERMOGRÁFICAS E DRONES NA COMPOSIÇÃO DE BANCO GEOGRÁFICOS","year":2018,"lang":"pt","type":"article","venue":"Programa de Iniciação Científica - PIC/UniCEUB - Relatórios de Pesquisa","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Adidas (Canada)","funders":"","keywords":"Humanities; Physics; Art","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.002587017,0.002111855,0.001865629,0.0009046022,0.001365513,0.001988707,0.002608937,0.002175177,0.0005410368],"category_scores_gemma":[0.0008426026,0.002264877,0.001147976,0.001817303,0.001119339,0.0006948196,0.000969659,0.003437256,0.0003393123],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003235744,"about_ca_system_score_gemma":0.0009355787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006168105,"about_ca_topic_score_gemma":0.0003270317,"domain_scores_codex":[0.9869959,0.0007855794,0.002394683,0.002119747,0.001344693,0.006359446],"domain_scores_gemma":[0.9937022,0.0006731693,0.0009184858,0.002241108,0.0005747689,0.001890314],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002225856,0.003321236,0.5646505,0.00260967,0.004049543,0.002302663,0.07496594,0.004244191,0.03843449,0.01166581,0.009793182,0.2817369],"study_design_scores_gemma":[0.01789886,0.008360366,0.1579256,0.007366166,0.005214832,0.006812669,0.03213865,0.2543403,0.04351369,0.004300152,0.4504708,0.01165795],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9650033,0.003645101,0.01822362,0.0006429645,0.00420852,0.0027705,0.0002263214,0.001792272,0.003487368],"genre_scores_gemma":[0.9797254,0.0006375412,0.01504083,0.0008540306,0.00202118,0.000613019,0.0001313159,0.000482228,0.0004944716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4406776,"threshold_uncertainty_score":0.9999346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01813189825488365,"score_gpt":0.2852183874230534,"score_spread":0.2670864891681698,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}