{"id":"W2744153085","doi":"10.1111/idj.12326","title":"Support vector machine-based differentiation between aggressive and chronic periodontitis using microbial profiles","year":2017,"lang":"en","type":"article","venue":"International Dental Journal","topic":"Oral microbiology and periodontitis research","field":"Dentistry","cited_by":79,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Chronic periodontitis; Aggressive periodontitis; Support vector machine; Periodontitis; Vector (molecular biology); Medicine; Microbiology; Computer science; Artificial intelligence; Biology; Dentistry; Genetics","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":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000182261,0.0001665518,0.0002081275,0.0001581122,0.0009416196,0.001133705,0.0005949311,0.0001393749,0.003060125],"category_scores_gemma":[0.0001232502,0.0001550898,0.000137532,0.00002050681,0.0002760236,0.0005310466,0.0002384087,0.0004667208,0.0001295625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002906838,"about_ca_system_score_gemma":0.0001562581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001971842,"about_ca_topic_score_gemma":0.0004921809,"domain_scores_codex":[0.9987909,0.00009446299,0.0003152089,0.0002418551,0.0002856874,0.0002718878],"domain_scores_gemma":[0.9989851,0.00003448804,0.000525,0.0001668364,0.0001638361,0.0001247891],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008818502,0.00003995042,0.8674588,0.00001674294,0.0001620763,0.0005858613,0.00003176329,0.000005699026,0.127601,0.00003356264,0.0006309895,0.003345332],"study_design_scores_gemma":[0.001835197,0.0001088974,0.9665781,0.0001659785,0.00003747973,0.003206404,0.00001573843,0.0006453984,0.02562964,0.00004964396,0.001531465,0.0001960195],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948934,0.0002768178,0.0007349706,0.0003194349,0.002725566,0.0001240969,0.0006652211,0.00001441962,0.0002461062],"genre_scores_gemma":[0.9969937,0.00005643263,0.0003017149,0.00003492419,0.001500168,0.000003491348,0.0002316891,0.00002118332,0.0008566736],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1019714,"threshold_uncertainty_score":0.9999032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0283191877448398,"score_gpt":0.3375747043803918,"score_spread":0.309255516635552,"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."}}