{"id":"W2777947904","doi":"10.33369/dr.v12i1.3381","title":"PENINGKATAN POPULASI DAN PEMANFAATAN AYAM LOKAL BENGKULU MELALUI PENERAPAN TEKNOLOGI MIKRONUTRISI DAN PENETASAN SEDERHANA UNTUK PENINGKATAN PENDAPATAN MASYARAKAT","year":2017,"lang":"id","type":"article","venue":"Dharma Raflesia Jurnal Ilmiah Pengembangan dan Penerapan IPTEKS","topic":"Food and Agricultural Sciences","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Hatching; Population; Biology; Toxicology; Animal science; Production rate; Production (economics); Agricultural science; Biotechnology; Horticulture; Demography; Engineering; Economics; Sociology","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","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity"],"category_scores_codex":[0.003039576,0.003737489,0.003576978,0.0004687434,0.01473249,0.009213034,0.01231066,0.00170441,0.003742968],"category_scores_gemma":[0.0005926277,0.002154553,0.002181639,0.001686185,0.003051512,0.005513584,0.003271866,0.004172871,0.0006987336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000785774,"about_ca_system_score_gemma":0.0004162925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004855542,"about_ca_topic_score_gemma":0.01358236,"domain_scores_codex":[0.9798358,0.001931492,0.003390345,0.00532735,0.003968959,0.005546069],"domain_scores_gemma":[0.988402,0.0005686978,0.004001721,0.002643467,0.0009396456,0.003444484],"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":[0.001303409,0.002856203,0.09433373,0.0004920095,0.001234691,0.003029335,0.003745714,0.0001272115,0.7514704,0.004008458,0.08553573,0.0518631],"study_design_scores_gemma":[0.007466767,0.00785093,0.6303406,0.001605911,0.001696678,0.003017651,0.0499717,0.001521658,0.09651338,0.00034732,0.1892675,0.01039994],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9606125,0.005955177,0.000004418359,0.01194612,0.004020144,0.002462273,0.0008900109,0.0007118334,0.01339754],"genre_scores_gemma":[0.9785026,0.002726228,0.0003811292,0.0009362327,0.006631056,0.00017115,0.002406208,0.0001218854,0.008123552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.654957,"threshold_uncertainty_score":0.9996616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03405241216458341,"score_gpt":0.2656533542440389,"score_spread":0.2316009420794554,"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."}}