{"id":"W3014611853","doi":"","title":"PERANCANGAN APLIKASI PERHITUNGAN BIAYA PERAWATAN TANAMAN KELAPA SAWIT PADA PT. LANGKAT NUSANTARA KEPONG (LNK) KEBUN BEKIUN SEBAGAI SOLUSI EFISIENSI BIAYA PERAWATAN","year":2017,"lang":"id","type":"article","venue":"","topic":"Management and Optimization Techniques","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Palm oil; Agricultural science; Database; Agricultural engineering; Mathematics; Business; Computer science; Engineering; Environmental science","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":[0.0003611909,0.0006138564,0.0003121117,0.0009076365,0.001079386,0.002150947,0.0005517066,0.0003114346,0.02414519],"category_scores_gemma":[0.0005926862,0.0003425897,0.0003773756,0.001683693,0.0003070538,0.001073523,0.0009039634,0.0008182937,0.004041487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001885533,"about_ca_system_score_gemma":0.00339866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01410509,"about_ca_topic_score_gemma":0.03499579,"domain_scores_codex":[0.999746,0.00002030081,0.00002092713,0.00006395548,0.00008716313,0.00006165838],"domain_scores_gemma":[0.9995759,0.00006906231,0.00006504486,0.00004082565,0.0001943397,0.00005481053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001199179,0.0006321807,0.08923459,0.001539044,0.0001529801,0.004769031,0.003131568,0.006445278,0.04839884,0.02217874,0.02174497,0.8005736],"study_design_scores_gemma":[0.0001775973,0.0008667737,0.2129173,0.0005621695,0.0005060127,0.008940633,0.01206888,0.01238665,0.04849454,0.007420073,0.6954247,0.000234618],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7498691,0.005249725,0.0182585,0.00178245,0.0004205586,0.0005610785,0.003165146,0.001395259,0.2192982],"genre_scores_gemma":[0.8609655,0.003801863,0.02383938,0.0002242116,0.00003049586,0.0002349338,0.001992631,0.0002202123,0.1086908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02414519,"threshold_uncertainty_score":0.08077371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01876443006040039,"score_gpt":0.2394713582572236,"score_spread":0.2207069281968232,"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."}}