{"id":"W4393021754","doi":"10.58169/jwikal.v1i2.85","title":"Pemanfaatan Analisis Sig Untuk Pemetaan Potensi Air Tanah Di Kabupaten Keerom","year":2022,"lang":"id","type":"article","venue":"JURNAL WILAYAH KOTA DAN LINGKUNGAN BERKELANJUTAN","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Encana (Canada); WiLAN (Canada)","funders":"","keywords":"Forestry; Physics; Humanities; 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":[0.0042797,0.001053177,0.0008812085,0.003723999,0.001696332,0.006332146,0.001037364,0.001184482,0.02796838],"category_scores_gemma":[0.01325579,0.0004811303,0.001409713,0.005233829,0.0009948546,0.004755056,0.002297858,0.002094552,0.0102172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002346425,"about_ca_system_score_gemma":0.004533778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03409402,"about_ca_topic_score_gemma":0.04341914,"domain_scores_codex":[0.9953436,0.0007966405,0.0004067193,0.0009037438,0.002061014,0.0004881341],"domain_scores_gemma":[0.9874693,0.003500208,0.001047594,0.0009753865,0.006553065,0.0004544728],"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":[0.00115536,0.0004306009,0.3959853,0.005763318,0.0006751702,0.001166145,0.01164779,0.004455996,0.01125187,0.01731353,0.1293617,0.4207932],"study_design_scores_gemma":[0.00005086909,0.0002979397,0.3558544,0.001671048,0.0005204512,0.0006984719,0.0354276,0.005818296,0.01315446,0.008537649,0.5777522,0.0002166247],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4952435,0.01201385,0.05617948,0.0170719,0.002280789,0.001691247,0.1013691,0.005707361,0.3084429],"genre_scores_gemma":[0.7788352,0.006895847,0.0399664,0.002538527,0.0004538563,0.001102533,0.05796597,0.001409031,0.1108327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03409402,"threshold_uncertainty_score":0.09356356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01434284218366586,"score_gpt":0.2516627936304627,"score_spread":0.2373199514467968,"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."}}