{"id":"W4390615466","doi":"10.33558/bentang.v12i1.7904","title":"Akurasi Data Curah Hujan Satelit Terhadap Data Pengukuran di Daerah Tangkapan Air (DTA) Waduk Sutami","year":2024,"lang":"en","type":"article","venue":"Bentang Jurnal Teoritis dan Terapan Bidang Rekayasa Sipil","topic":"Multimedia Learning Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Environmental science; Meteorology; Precipitation; Hydrology (agriculture); Flood myth; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004759745,0.0005310532,0.0004497847,0.001941238,0.000641772,0.001506846,0.0004444291,0.0003000949,0.04557912],"category_scores_gemma":[0.001631203,0.0002864179,0.0002614903,0.004227457,0.0001820709,0.001105581,0.000607426,0.0006970575,0.0203583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005694504,"about_ca_system_score_gemma":0.001707851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01665306,"about_ca_topic_score_gemma":0.013928,"domain_scores_codex":[0.999512,0.00003785739,0.00007380811,0.0001076872,0.0002176918,0.00005110491],"domain_scores_gemma":[0.9984341,0.000218351,0.0001492232,0.0001581634,0.0009445602,0.00009547551],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001233019,0.0003519982,0.0729266,0.002547018,0.0001704439,0.001921362,0.002906099,0.001945915,0.008757462,0.005952703,0.4934995,0.4077879],"study_design_scores_gemma":[0.00006231958,0.0001268629,0.1105603,0.0002692883,0.00008624792,0.0005557939,0.00335508,0.001539348,0.004970404,0.0009373291,0.8774486,0.00008838649],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.2234543,0.003847798,0.007169313,0.001780036,0.001892664,0.0005674989,0.5623536,0.002558399,0.1963764],"genre_scores_gemma":[0.375144,0.004867747,0.01089935,0.0004100041,0.0003772714,0.0008275004,0.4442062,0.0006696917,0.1625982],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04557912,"threshold_uncertainty_score":0.1524773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05706790692350466,"score_gpt":0.3114233651436592,"score_spread":0.2543554582201545,"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."}}