{"id":"W4225822829","doi":"10.25105/petro.v10i3.8906","title":"PENGGUNAAN PETA SOI DALAM OPTIMASI PENENTUAN LOKASI SUMUR SISIPAN DENGAN MODEL RESERVOIR 3-DIMENSI: STUDI KASUS PENGEMBANGAN LAPANGAN CAL LAPISAN CA","year":2021,"lang":"id","type":"article","venue":"Petro Jurnal Ilmiah Teknik Perminyakan","topic":"Geological and Geophysical Studies","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Physics; Forestry; Geography","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":[0.0005558883,0.0006960841,0.0007759897,0.0003755247,0.0004198274,0.002450721,0.0008519827,0.0008686756,0.005897487],"category_scores_gemma":[0.001297533,0.0005304127,0.00103696,0.0004338045,0.0003371491,0.001438501,0.000657314,0.0009584863,0.001140407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009583556,"about_ca_system_score_gemma":0.001430969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02290916,"about_ca_topic_score_gemma":0.02231728,"domain_scores_codex":[0.9997823,0.00004487672,0.00001481798,0.00005625625,0.00005724661,0.00004464382],"domain_scores_gemma":[0.9994837,0.0002632778,0.00003846006,0.0000406115,0.0001313,0.00004259719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008590754,0.0004567744,0.02480241,0.0006045186,0.0002010759,0.0002020141,0.0002889543,0.8790669,0.02918349,0.002625529,0.002261651,0.05944757],"study_design_scores_gemma":[0.0000378033,0.0005416151,0.01060106,0.00005860744,0.0001176568,0.00005872493,0.0004118321,0.9657429,0.0163328,0.001243049,0.004797623,0.00005626105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.889467,0.001602626,0.07070808,0.0005335829,0.0001190868,0.0001844724,0.001425176,0.001163318,0.03479668],"genre_scores_gemma":[0.975648,0.0005067929,0.01328366,0.00006561918,0.000007893719,0.00007344629,0.0006886608,0.0001559705,0.009569967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02290916,"threshold_uncertainty_score":0.04555166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03515704785527567,"score_gpt":0.2508749774909367,"score_spread":0.2157179296356611,"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."}}