{"id":"W4381950811","doi":"10.29303/ipr.v6i1.196","title":"APPLICATION OF SUPPORT VECTOR MACHINE ON DROUGHT CODE CLASSIFICATION IN NORTH SUMATRA INDONESIA","year":2022,"lang":"en","type":"article","venue":"Indonesian Physical Review","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Support vector machine; Code (set theory); Computer science; Meteorology; Data mining; Geography; Climatology; Artificial intelligence; Geology","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":[],"consensus_categories":[],"category_scores_codex":[0.0004320928,0.0001763344,0.0004082611,0.0001328598,0.0001358099,0.0000216562,0.001153523,0.00001994808,0.00001156922],"category_scores_gemma":[0.00004067605,0.0001717392,0.0001072645,0.001589234,0.00004165103,0.0001737092,0.0002350875,0.0004329534,0.00008162984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009064997,"about_ca_system_score_gemma":0.00009716189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005980894,"about_ca_topic_score_gemma":0.00002678717,"domain_scores_codex":[0.9980451,0.0002599705,0.000465587,0.0005373796,0.0004685466,0.0002233861],"domain_scores_gemma":[0.9982719,0.0001583873,0.0003603292,0.001086335,0.00004314564,0.00007987127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00003133318,0.002470474,0.04426973,0.001115281,0.0000315561,0.00001166872,0.000663695,0.0006267568,0.001494214,0.398389,0.001967972,0.5489283],"study_design_scores_gemma":[0.001118966,0.0007086499,0.7832562,0.0003772147,0.00007753006,0.00004333496,0.00002807721,0.1196262,0.00026689,0.001989365,0.09169949,0.0008080406],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6357881,0.006608187,0.3067305,0.0352431,0.0004397563,0.006737574,0.0008039161,0.001125881,0.006522871],"genre_scores_gemma":[0.9970405,0.0003189831,0.0007356112,0.0006881574,0.00003650629,0.0008187969,0.0003261876,0.00001819237,0.00001713497],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7389865,"threshold_uncertainty_score":0.7003319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01909559190960787,"score_gpt":0.3020399888117014,"score_spread":0.2829443969020936,"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."}}