{"id":"W4384208855","doi":"10.20885/teknisia.vol28.iss1.art5","title":"KAJIAN PERUBAHAN IKLIM DI DKI JAKARTA BERDASARKAN DATA CURAH HUJAN","year":2023,"lang":"en","type":"article","venue":"Teknisia","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Downscaling; Environmental science; Flooding (psychology); Climatology; Climate change; Monsoon; Wet season; Global warming; Climate model; Meteorology; Geography; Precipitation; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006733572,0.0001880716,0.0001816535,0.0001786711,0.0003993566,0.0003448723,0.004007323,0.00006587584,0.00003812306],"category_scores_gemma":[0.0001728721,0.0001814995,0.00004492906,0.001075018,0.0000679641,0.0006496665,0.002034674,0.0002948953,0.004204329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001970299,"about_ca_system_score_gemma":0.00009247822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003136153,"about_ca_topic_score_gemma":0.00006512772,"domain_scores_codex":[0.9980113,0.00008428732,0.0002455582,0.0008530004,0.0003299397,0.0004759433],"domain_scores_gemma":[0.9958444,0.0001625648,0.00009531783,0.003674885,0.00003885901,0.0001839595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005387712,0.0001585834,0.007622099,0.0000369166,0.00006711289,0.00006047449,0.002707522,0.0001450118,0.001381968,0.08230899,0.4798327,0.4256732],"study_design_scores_gemma":[0.0002914969,0.00006533845,0.04846916,0.00003062032,0.00001875685,0.00003229424,0.0001228495,0.1405899,0.00009907796,0.001545337,0.8083403,0.00039498],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3221243,0.0006970667,0.4584028,0.09523547,0.003182738,0.001748336,0.001465662,0.01930806,0.09783553],"genre_scores_gemma":[0.9718146,0.00004139822,0.01732788,0.0006598889,0.0003044233,0.00005274962,0.001607288,0.00004760825,0.008144148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6496903,"threshold_uncertainty_score":0.996571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06478516333902466,"score_gpt":0.3331030571871688,"score_spread":0.2683178938481441,"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."}}