{"id":"W4386356208","doi":"10.11606/t.55.2023.tde-01092023-164636","title":"Deep learning and data warehousing techniques applied to real data in the medical domain","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo; Agence Nationale de la Recherche; Nvidia","keywords":"Data warehouse; Domain (mathematical analysis); Big data; Computer science; Data science; Data mining; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.002435527,0.0002897261,0.0003142496,0.000411502,0.0002338044,0.000723095,0.003428069,0.0002990155,0.0002745405],"category_scores_gemma":[0.0007043529,0.0002060532,0.00001450296,0.001016289,0.00004583057,0.0008997461,0.002448095,0.0006133158,0.0001933896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001306213,"about_ca_system_score_gemma":0.00004969477,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005446698,"about_ca_topic_score_gemma":0.03827715,"domain_scores_codex":[0.9974677,0.00002057844,0.0004306299,0.0009521187,0.0007947519,0.0003342158],"domain_scores_gemma":[0.998118,0.0001665639,0.0001765436,0.001459153,0.00006228382,0.00001747397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001681508,0.0001075548,0.002764438,0.001480566,0.00004912812,0.0001756302,0.000645871,0.000003939892,0.0001540232,0.01310875,0.0492993,0.9320427],"study_design_scores_gemma":[0.0003165937,0.00001243719,0.01500108,0.001519704,0.0001993208,0.00001372369,0.0259715,0.01150829,0.00003239587,0.005168175,0.9388015,0.001455281],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1416716,0.001063372,0.03596016,0.01794369,0.004553993,0.007000789,0.0002344376,0.004616186,0.7869557],"genre_scores_gemma":[0.5226836,0.006978691,0.01508197,0.01219946,0.01516443,0.0005370571,0.4165273,0.0008235847,0.01000393],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9305874,"threshold_uncertainty_score":0.9792718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09115934031651689,"score_gpt":0.3706633151245383,"score_spread":0.2795039748080214,"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."}}