{"id":"W3112291925","doi":"10.46880/methoda.vol9no3.pp156-164","title":"JARINGAN SYARAF TIRUAN MEMPREDIKSI LAJU PERTUMBUHAN PENDUDUK KOTA BINJAI METODE BACKPROPAGATION","year":2019,"lang":"id","type":"article","venue":"Majalah Ilmiah METHODA","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Forestry; Mathematics; Geography","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","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00410275,0.000897898,0.001050301,0.0005644459,0.0007299097,0.001198241,0.003357964,0.0005013353,0.0007101096],"category_scores_gemma":[0.0008424324,0.0009134439,0.0003968464,0.001769692,0.0002078413,0.001225689,0.001676539,0.001691676,0.008039977],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002665188,"about_ca_system_score_gemma":0.0004850169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001280451,"about_ca_topic_score_gemma":0.00006666023,"domain_scores_codex":[0.9921697,0.001740659,0.001123541,0.002427915,0.001172427,0.001365793],"domain_scores_gemma":[0.9932828,0.001115556,0.0007746981,0.003850517,0.0003711146,0.0006053008],"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":[0.0002573041,0.001937144,0.03479274,0.00139659,0.001213148,0.0001335244,0.02303069,0.003105506,0.1381541,0.1386142,0.03740242,0.6199627],"study_design_scores_gemma":[0.003435111,0.001248363,0.04373197,0.0007383536,0.0005973014,0.0002675981,0.001029632,0.3057546,0.01952195,0.002691575,0.6175247,0.003458803],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.325785,0.001328007,0.570474,0.01095444,0.007508191,0.002909149,0.0002464457,0.002020704,0.07877401],"genre_scores_gemma":[0.6717426,0.0001917547,0.2352563,0.001548133,0.0008628091,0.0002096548,0.0004546106,0.0002362898,0.08949787],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6165039,"threshold_uncertainty_score":0.9998386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02113525040026833,"score_gpt":0.2934320522441293,"score_spread":0.272296801843861,"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."}}