{"id":"W3134436819","doi":"10.20961/sepa.v17i2.40066","title":"ANALISIS OPTIMASI PENGGUNAAN FAKTOR PRODUKSI KOPI BUBUK PADA AGROINDUSTRI XYZ DI KOTA JAMBI","year":2021,"lang":"en","type":"article","venue":"SEPA Jurnal Sosial Ekonomi Pertanian dan Agribisnis","topic":"Agricultural Research and Practices","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Raw material; Coffee shop; Agricultural science; Production (economics); Quarter (Canadian coin); Green coffee; Mathematics; Coffee grounds; Coffee bean; COCOA BEAN; Research Object; Toxicology; Food science; Environmental science; Business; Geography; Biology; Economics; Advertising","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001070093,0.0009064625,0.0007377238,0.002409628,0.0003603959,0.002444088,0.0003919379,0.0004663854,0.00591402],"category_scores_gemma":[0.002234962,0.0003739484,0.0006803549,0.002147692,0.0002398917,0.0009060567,0.0003195388,0.0004716087,0.001754485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006685806,"about_ca_system_score_gemma":0.0005063687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009874311,"about_ca_topic_score_gemma":0.009194341,"domain_scores_codex":[0.9992636,0.00009256044,0.00005090167,0.0001773379,0.0003252759,0.00009026774],"domain_scores_gemma":[0.9983155,0.0009542708,0.0001673382,0.00007540351,0.0004474211,0.00004012648],"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":[0.003401714,0.0009282944,0.2424281,0.002795705,0.0007309478,0.001444998,0.001373725,0.108394,0.08658476,0.001993027,0.007047069,0.5428777],"study_design_scores_gemma":[0.00005002944,0.001418349,0.6612692,0.0001878363,0.0004932735,0.0009682741,0.003333554,0.231037,0.07713332,0.001072738,0.02277827,0.0002581983],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9707215,0.003354694,0.01262454,0.0001998931,0.00007403312,0.00008369433,0.002454712,0.0003344498,0.01015246],"genre_scores_gemma":[0.974668,0.001439314,0.01315777,0.0000261766,0.00001494379,0.00006197091,0.003125215,0.00009892126,0.007407585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009874311,"threshold_uncertainty_score":0.01978439,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02875813636659187,"score_gpt":0.2505455116587921,"score_spread":0.2217873752922003,"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."}}