{"id":"W2989613929","doi":"10.14203/oseana.2018.vol.43no.2.16","title":"KARAKTERISTIK DAN DAMPAK SIKLON TROPIS YANG TUMBUH DI SEKITAR WILAYAH INDONESIA","year":2018,"lang":"en","type":"article","venue":"OSEANA","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Tropical cyclone; Cyclone (programming language); Extratropical cyclone; Climatology; Environmental science; Equator; Tropical cyclone scales; Latitude; Geography; Geology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002443614,0.000164009,0.0001621155,0.0001056805,0.000358106,0.0002208386,0.001219869,0.00005753558,0.00001426833],"category_scores_gemma":[0.00006713745,0.0001563509,0.00005370174,0.000454698,0.0001314259,0.0003497774,0.0003963918,0.000210051,0.0005316482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003694659,"about_ca_system_score_gemma":0.0000635484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002681905,"about_ca_topic_score_gemma":0.00004034265,"domain_scores_codex":[0.9985785,0.00008934287,0.0001987992,0.0005251162,0.0002485101,0.0003597165],"domain_scores_gemma":[0.998427,0.00007543642,0.00009634847,0.001169683,0.000073639,0.0001578797],"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":[0.00004139264,0.000522288,0.08261064,0.00007487068,0.0001399078,0.00008466514,0.007306345,0.0001326431,0.0103646,0.1666251,0.0973008,0.6347967],"study_design_scores_gemma":[0.001142318,0.0007690543,0.4747405,0.0001019709,0.00005293464,0.00009919444,0.0001717611,0.0872961,0.00316442,0.001982243,0.4293255,0.001154017],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.692494,0.00007880964,0.288232,0.002917457,0.0004186512,0.0001939596,0.0000229086,0.000723308,0.01491889],"genre_scores_gemma":[0.9849442,0.000006193558,0.01369859,0.0003566888,0.0003212475,0.00002575941,0.00005596946,0.00001955513,0.0005718314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6336427,"threshold_uncertainty_score":0.6833439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01028718361609056,"score_gpt":0.2599549192476049,"score_spread":0.2496677356315143,"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."}}