{"id":"W4387216938","doi":"10.59697/jik.v4i2.337","title":"SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LAHAN PERTANIAN YANG TEPAT UNTUK MENINGKATKAN HASIL PANEN CABAI MENGGUNAKAN METODE MOORA","year":2020,"lang":"en","type":"article","venue":"Jurnal Informatika Kaputama (JIK)","topic":"Decision Support System Applications","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Selection (genetic algorithm); Mathematics; Correctness; Ranking (information retrieval); Horticulture; Agricultural engineering; Mathematical optimization; Computer science; Algorithm; Engineering; Artificial intelligence; Biology","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":[0.001312339,0.0006625052,0.0006758181,0.001132725,0.001384364,0.004654273,0.0006458683,0.000675895,0.01277445],"category_scores_gemma":[0.00225587,0.000280704,0.0003475815,0.00168616,0.0006013414,0.002868273,0.001526769,0.001010303,0.003843683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008836426,"about_ca_system_score_gemma":0.002060754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002579817,"about_ca_topic_score_gemma":0.00318062,"domain_scores_codex":[0.9991779,0.0001733209,0.00007252117,0.000171445,0.0003143994,0.00009034259],"domain_scores_gemma":[0.9990363,0.0003235772,0.00007477883,0.0001089344,0.0003924776,0.00006386993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006490543,0.0003398305,0.01063668,0.001407053,0.0001076534,0.001443578,0.00288887,0.007023267,0.02670522,0.04069506,0.01440185,0.8937019],"study_design_scores_gemma":[0.0002526968,0.001387376,0.03619383,0.0009796147,0.0004930197,0.003782939,0.01114846,0.09366114,0.1155252,0.056795,0.6793665,0.000414234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4375999,0.008670182,0.2775643,0.004266283,0.001074143,0.0008456975,0.002100799,0.007204807,0.2606739],"genre_scores_gemma":[0.7772525,0.004028827,0.1382239,0.0003808186,0.0001541405,0.0003839436,0.002129422,0.0004066847,0.07703969],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01277445,"threshold_uncertainty_score":0.04273474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04063850811124614,"score_gpt":0.2312003517783173,"score_spread":0.1905618436670711,"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."}}