{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["metaepi_narrow","insufficient_payload"],"category_scores_codex":[0.001821678,0.002124266,0.001952746,0.001494414,0.00640411,0.01078101,0.003921795,0.0008488022,0.01447295],"category_scores_gemma":[0.0005239376,0.002061502,0.0009635456,0.001015331,0.0007904519,0.006671759,0.003179708,0.00129399,0.003124996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003705217,"about_ca_system_score_gemma":0.0001637066,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008816984,"about_ca_topic_score_gemma":0.008290046,"domain_scores_codex":[0.9906821,0.0001654424,0.002013221,0.002672736,0.001918814,0.002547683],"domain_scores_gemma":[0.992034,0.0001403474,0.002022552,0.004327246,0.001159569,0.0003162799],"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":[0.0008229154,0.002624415,0.1755912,0.006593678,0.002643357,0.001781741,0.003600294,0.0002341498,0.002846142,0.05180353,0.7131897,0.0382689],"study_design_scores_gemma":[0.005725709,0.0003055584,0.04518107,0.001763839,0.002138383,0.00004796455,0.007260131,0.03348717,0.00162392,0.001496423,0.8942919,0.006677923],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1719918,0.001635432,0.003382432,0.02106334,0.004570803,0.005182606,0.0001592999,0.003483105,0.7885312],"genre_scores_gemma":[0.8002478,0.001285538,0.003620633,0.00648933,0.003985285,0.0001691543,0.001253758,0.0005385171,0.1824099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.628256,"threshold_uncertainty_score":0.9991499,"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."}}