{"id":"W2732295666","doi":"","title":"SEGMENTASI CITRA MAGNETIC RESONANCE IMAGING (MRI) MENGGUNAKAN FUZZY CMEANS(FCM)","year":2017,"lang":"id","type":"article","venue":"","topic":"Computer Science and Engineering","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Magnetic resonance imaging; Cluster analysis; Pattern recognition (psychology); Humanities; Computer science; Medicine; Radiology; Philosophy","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.000794809,0.000585168,0.0004633841,0.0002292351,0.001601204,0.004580411,0.0061531,0.0001002756,0.0001672905],"category_scores_gemma":[0.00004917688,0.0005902931,0.0002286079,0.0003882214,0.0003368767,0.003077963,0.002479609,0.0004571725,0.0007354791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001427758,"about_ca_system_score_gemma":0.0001901817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003398662,"about_ca_topic_score_gemma":0.000087441,"domain_scores_codex":[0.995524,0.00006387287,0.0005904838,0.001484291,0.0008893191,0.001448044],"domain_scores_gemma":[0.9955174,0.0000783882,0.0002265427,0.003540139,0.0001378582,0.0004997273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008707364,0.0001433477,0.007023307,0.00007873496,0.00001674371,0.0004555796,0.002566333,0.0003731046,0.001927637,0.01649067,0.01467482,0.956241],"study_design_scores_gemma":[0.001337551,0.000257611,0.1052313,0.0004620042,0.00003569097,0.0002106825,0.0001259018,0.7508582,0.003427885,0.00171552,0.1346999,0.001637751],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01600131,0.01876605,0.7818193,0.01444794,0.01281098,0.0008734594,0.00001483312,0.001043563,0.1542226],"genre_scores_gemma":[0.8640655,0.001033345,0.1076729,0.001838063,0.0011172,0.00003601594,0.000003076597,0.00007743484,0.02415643],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9546033,"threshold_uncertainty_score":0.9996986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01339950243240381,"score_gpt":0.2308601045906639,"score_spread":0.21746060215826,"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."}}