{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.002720004,0.001334373,0.001516843,0.001056421,0.00461375,0.001277898,0.005984324,0.0003202718,0.0002685152],"category_scores_gemma":[0.0004666514,0.00147033,0.0008181849,0.003595281,0.0004160307,0.001047166,0.003814166,0.004066471,0.0003745391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007569204,"about_ca_system_score_gemma":0.0008500029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008547434,"about_ca_topic_score_gemma":0.0001001027,"domain_scores_codex":[0.9889742,0.001556929,0.001916674,0.002859844,0.0026005,0.002091871],"domain_scores_gemma":[0.9920256,0.0006520333,0.001587663,0.003980515,0.0004792914,0.001274951],"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":[0.001550503,0.01036896,0.2424045,0.00107715,0.008898943,0.00762807,0.0512622,0.07902913,0.04777513,0.06903259,0.2979783,0.1829945],"study_design_scores_gemma":[0.004515537,0.004167475,0.2129645,0.0004695298,0.002620306,0.003658454,0.007680839,0.1570497,0.001972784,0.001123074,0.5975328,0.006244996],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.94523,0.002909885,0.01047522,0.02194971,0.004673726,0.001395726,0.002219545,0.00154551,0.009600677],"genre_scores_gemma":[0.9865463,0.0001627792,0.002486182,0.00274798,0.001316447,0.0001487677,0.001207437,0.0002673207,0.005116756],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2995544,"threshold_uncertainty_score":0.9999408,"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."}}