{"id":"W3098033776","doi":"10.47028/j.risenologi.2018.31.35","title":"ANALISIS KESESUAIAN LAHAN MENGGUNAKAN SISTEM INFORMASI GEOGRAFIS (SIG) UNTUK LOKASI PENGGEMUKAN SAPI DI KECAMATAN CIRACAP, KABUPATEN SUKABUMI SEBAGAI UPAYA SWASEMBADA DAGING SAPI","year":2018,"lang":"id","type":"article","venue":"Risenologi","topic":"Livestock Farming and Management","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Physics","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":[0.001972641,0.0003507954,0.0002997446,0.0028382,0.001350756,0.004639348,0.0004066214,0.0004650672,0.01613283],"category_scores_gemma":[0.007365909,0.0001872308,0.0002967256,0.006192919,0.001045233,0.002659412,0.001649528,0.001038628,0.002842238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00281647,"about_ca_system_score_gemma":0.003389128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03812362,"about_ca_topic_score_gemma":0.05525284,"domain_scores_codex":[0.9984315,0.0003357654,0.0001226696,0.0002152572,0.0006741593,0.0002205406],"domain_scores_gemma":[0.9934428,0.002835724,0.0006882042,0.0003482451,0.00237912,0.0003059902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003670563,0.0001241689,0.5380988,0.001946829,0.0001647221,0.001266856,0.08560387,0.0009591929,0.002514916,0.035778,0.03923169,0.2939439],"study_design_scores_gemma":[0.00001085191,0.00009298536,0.5220137,0.0007470739,0.0001271814,0.0005295184,0.1229012,0.0008150367,0.001936359,0.003180976,0.3476028,0.00004224102],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7885172,0.00453101,0.002857231,0.004101234,0.0002182256,0.0001539871,0.008372727,0.0001936799,0.1910548],"genre_scores_gemma":[0.9541566,0.002731843,0.002259257,0.0004283109,0.00005741354,0.0001086121,0.003417249,0.00009989551,0.0367408],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03812362,"threshold_uncertainty_score":0.0758034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02060269044535673,"score_gpt":0.2373137860182992,"score_spread":0.2167110955729425,"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."}}