{"id":"W2774526168","doi":"10.15863/tas.2017.11.55.15","title":"TECHNOLOGICAL SUPPLY OF ADDITIVE TECHNOLOGIES FOR RECONSTRUCTION OF FACE SKELET","year":2017,"lang":"en","type":"article","venue":"Theoretical & Applied Science","topic":"E-commerce and Technology Innovations","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"SKiN Health","funders":"","keywords":"Face (sociological concept); Business; Computer science; Sociology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009896952,0.0004593388,0.0003123572,0.003034853,0.0005009491,0.001738992,0.0006740835,0.0006302873,0.01609699],"category_scores_gemma":[0.001165038,0.0003331368,0.0006159932,0.001682244,0.0004194003,0.0008297046,0.0009989176,0.0009165652,0.005203932],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005854774,"about_ca_system_score_gemma":0.0007221524,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003517368,"about_ca_topic_score_gemma":0.0007807877,"domain_scores_codex":[0.9990134,0.00008202515,0.00005915391,0.0001327834,0.0006664895,0.00004611359],"domain_scores_gemma":[0.9989519,0.0003183284,0.00009767812,0.0002441314,0.0003387522,0.00004918858],"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":[0.000189729,0.00009765851,0.003768543,0.001300053,0.00004408321,0.000773016,0.0003464653,0.001491443,0.2007428,0.0209268,0.00373761,0.7665818],"study_design_scores_gemma":[0.00003249734,0.0008587485,0.01739824,0.0005976803,0.0002299921,0.01090118,0.0005049728,0.006284821,0.316232,0.00926431,0.637617,0.00007846711],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2408105,0.1280826,0.3708561,0.003525522,0.002613458,0.0003544806,0.001587201,0.001537191,0.2506329],"genre_scores_gemma":[0.60133,0.04980569,0.2523797,0.001012135,0.001133524,0.0002158947,0.001032748,0.0004569677,0.09263341],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01609699,"threshold_uncertainty_score":0.05384976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01522629267325049,"score_gpt":0.2615645113048052,"score_spread":0.2463382186315547,"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."}}