{"id":"W2186431393","doi":"","title":"Segmentação de imagens de alta resolução utilizando o programa SMAGIC","year":2009,"lang":"pt","type":"article","venue":"Biblioteca Digital da Memória Científica do INPE (National Institute for Space Research)","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Markov random field; Segmentation; Pattern recognition (psychology); Set (abstract data type); Benchmark (surveying); Contextual image classification; Noise (video); Process (computing); Image segmentation; Class (philosophy); Data set; Image (mathematics); Computer vision; Geography; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008253122,0.0006249936,0.0005784646,0.002443088,0.0004084601,0.0008801441,0.000593584,0.0004773305,0.003621413],"category_scores_gemma":[0.001732391,0.0003035221,0.000519812,0.00163961,0.0003552366,0.000700449,0.000383895,0.0004088462,0.001272222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005463777,"about_ca_system_score_gemma":0.0005030764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003721401,"about_ca_topic_score_gemma":0.0082597,"domain_scores_codex":[0.9997297,0.00003244916,0.00001598379,0.0000862564,0.0001018246,0.00003383727],"domain_scores_gemma":[0.9994469,0.0001908554,0.00007337493,0.0001211939,0.0001392174,0.00002844221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005975346,0.00009865665,0.01292964,0.000348613,0.0001041379,0.0001568009,0.000487467,0.02433328,0.1207776,0.001870499,0.003960765,0.834335],"study_design_scores_gemma":[0.00008176077,0.0006903959,0.1329963,0.0001727294,0.0002219521,0.0009442638,0.001123619,0.5782181,0.233453,0.005551894,0.04642063,0.0001254746],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2689899,0.0006016603,0.6917636,0.0002910823,0.00006569472,0.0003199918,0.002385151,0.0264155,0.009167472],"genre_scores_gemma":[0.4010231,0.0003117118,0.5915187,0.00008162279,0.00002628951,0.0001456486,0.002946188,0.0009869466,0.002959809],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003721401,"threshold_uncertainty_score":0.01211476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1151680368566174,"score_gpt":0.3780020313260005,"score_spread":0.2628339944693832,"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."}}