{"id":"W4229446789","doi":"10.5539/jas.v14n6p68","title":"Improving Maize Production and Farmers’ Income Using System Dynamics Model","year":2022,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Agricultural Development and Management","field":"Agricultural and Biological Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institut Teknologi Sepuluh Nopember","keywords":"Production (economics); Agriculture; Agricultural economics; Population; Business; Agricultural engineering; Consumption (sociology); Organic farming; System dynamics; Economics; Agricultural science; Environmental science; Engineering; Geography; Computer science; Microeconomics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009396204,0.0001467725,0.0001909817,0.00004304095,0.001265405,0.0001579147,0.0004752657,0.0000240937,0.00001286365],"category_scores_gemma":[0.0000376532,0.00004629158,0.00007347915,0.001075966,0.0001090787,0.0008545492,0.0004793474,0.0002132555,8.391801e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007020637,"about_ca_system_score_gemma":0.0000329546,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007965168,"about_ca_topic_score_gemma":0.0000450098,"domain_scores_codex":[0.9981601,0.00004727238,0.000378129,0.0002688895,0.000827982,0.0003176823],"domain_scores_gemma":[0.9990407,0.00002014697,0.0004982122,0.00003288453,0.0002631592,0.0001448582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00003151806,0.00006371964,0.00471751,0.00002971616,0.0000249872,0.00001271333,0.0003781575,0.01411533,0.9645517,0.001555324,0.0002377808,0.01428155],"study_design_scores_gemma":[0.0003108368,0.0006209521,0.9087244,0.000122964,0.000130293,0.002185221,0.04287177,0.04073208,0.003121385,0.0001448509,0.0002575319,0.0007776942],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979911,0.00007838802,0.00004520228,0.0009092832,0.0005694979,0.0001924973,0.000005636795,0.00002709713,0.0001812937],"genre_scores_gemma":[0.9980789,0.0000146557,0.00138135,0.00003384076,0.0001697964,0.000004223084,0.00000342377,6.086469e-7,0.0003131597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9614303,"threshold_uncertainty_score":0.9732597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361917549879909,"score_gpt":0.1932237990465459,"score_spread":0.1796046235477468,"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."}}