{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001205988,0.001258256,0.001304712,0.0002498295,0.002161284,0.001028313,0.002121006,0.0008045078,0.001418927],"category_scores_gemma":[0.0002659262,0.0006761446,0.0006600632,0.001786357,0.0009143924,0.0009266128,0.001556782,0.0009371014,0.001037146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003978711,"about_ca_system_score_gemma":0.00006969091,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004017084,"about_ca_topic_score_gemma":0.003696738,"domain_scores_codex":[0.9926438,0.0006210541,0.001418056,0.00180177,0.001106868,0.002408411],"domain_scores_gemma":[0.9966228,0.0003897013,0.0009083326,0.000873698,0.0004019675,0.0008034952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006089772,0.002207939,0.612891,0.001112227,0.002387104,0.0005070713,0.006796266,0.00008692759,0.01661989,0.00587672,0.03005864,0.3208472],"study_design_scores_gemma":[0.001238541,0.002972382,0.6467291,0.0005212415,0.0006203152,0.00005328858,0.01805566,0.0005870941,0.004036067,0.0002713307,0.3225219,0.002393133],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9438226,0.001295016,0.00002410051,0.004907797,0.001777505,0.001281871,0.0001796513,0.0008307533,0.04588074],"genre_scores_gemma":[0.9818325,0.001164021,0.0001690788,0.0008948224,0.001699354,0.00008185508,0.00063522,0.00002709554,0.01349603],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3184541,"threshold_uncertainty_score":0.9997407,"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."}}