{"id":"W2345756554","doi":"10.29244/jai.2016.4.1.27-42","title":"Perencanaan Pembangunan Ekonomi Wilayah Berbasis Pertanian dalam Rangka Pengurangan Kemiskinan di Kalimantan Barat","year":2017,"lang":"en","type":"article","venue":"Jurnal Agribisnis Indonesia","topic":"Economic Growth and Fiscal Policies","field":"Economics, Econometrics and Finance","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Encana (Canada)","funders":"","keywords":"Agriculture; Poverty; Investment (military); Business; Government (linguistics); Agricultural economics; Government budget; Descriptive statistics; Economic growth; Economics; Public finance; Geography; Political 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"],"consensus_categories":[],"category_scores_codex":[0.0005450745,0.0006039526,0.001107882,0.000345209,0.001658437,0.0008927078,0.001599571,0.000359645,0.00008694241],"category_scores_gemma":[0.0001344214,0.0006307732,0.0005587902,0.0001386516,0.0003846543,0.000961289,0.0002718526,0.0006089406,0.0004434703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003644055,"about_ca_system_score_gemma":0.00007665061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002440499,"about_ca_topic_score_gemma":0.001268543,"domain_scores_codex":[0.996566,0.00003889372,0.00124916,0.0009605364,0.0001066293,0.001078808],"domain_scores_gemma":[0.996662,0.00007118533,0.001201931,0.001456604,0.00006531041,0.0005430266],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005348468,0.0001106775,0.9488984,0.0000388652,0.0001104948,0.00004376179,0.0006569457,0.00001012026,0.000126629,0.04677267,0.0008747135,0.002303281],"study_design_scores_gemma":[0.001525434,0.0001272861,0.974041,0.00004254343,0.00003459271,0.00007687121,0.0001881357,0.000366306,0.000380066,0.000897398,0.02150596,0.0008143754],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8710778,0.0007263758,0.00005411093,0.002980666,0.0008950426,0.0003146238,0.0002234635,0.00008130454,0.1236466],"genre_scores_gemma":[0.996923,0.0003034749,0.00003089614,0.0005368752,0.001043319,0.00004968432,0.00004977126,0.00009640854,0.0009665638],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1258452,"threshold_uncertainty_score":0.9996412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02868786612568129,"score_gpt":0.2264744750065841,"score_spread":0.1977866088809029,"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."}}