{"id":"W7160093786","doi":"10.5281/zenodo.15534372","title":"Використання штучного інтелекту в ортодонтії","year":2023,"lang":"","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Medical and Biological Sciences","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Everyday life; Dentition; Pathological anatomy; Scientific literature","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":[0.005627054,0.000904171,0.0006343909,0.002871929,0.005651156,0.01624836,0.001379346,0.003159735,0.02778083],"category_scores_gemma":[0.01212733,0.0008329037,0.0009640937,0.003119018,0.01191958,0.009813551,0.005213338,0.005224592,0.01298796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006125042,"about_ca_system_score_gemma":0.01247421,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01004079,"about_ca_topic_score_gemma":0.0121802,"domain_scores_codex":[0.9924907,0.002322858,0.00047951,0.001017513,0.002901563,0.000787865],"domain_scores_gemma":[0.9935088,0.001906833,0.0006498037,0.0007754987,0.002257747,0.0009012705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001052257,0.00007874805,0.003900036,0.0006548212,0.00004194212,0.000578701,0.01431644,0.0006086103,0.001322646,0.8390509,0.03784951,0.1014924],"study_design_scores_gemma":[0.00002611701,0.00005163215,0.003621637,0.0008704201,0.00004902898,0.0006112317,0.01062535,0.0004614541,0.001391724,0.2131622,0.769047,0.00008227568],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"review","genre_scores_codex":[0.02715951,0.02821354,0.04352707,0.04639201,0.003541955,0.0002446983,0.0009449948,0.0004178555,0.8495584],"genre_scores_gemma":[0.7097363,0.03109474,0.04658118,0.006782138,0.001847311,0.0006778673,0.001064312,0.0008315857,0.2013846],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02778083,"threshold_uncertainty_score":0.09293616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08938157973880083,"score_gpt":0.2924571818909587,"score_spread":0.2030756021521578,"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."}}