{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004009366,0.0005402128,0.0003275921,0.0004398889,0.001755751,0.001126844,0.000645792,0.0005020086,0.03166818],"category_scores_gemma":[0.0002586754,0.0001983381,0.0003074205,0.0004387004,0.0004796706,0.0005765851,0.001518251,0.0009836996,0.004523946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008189813,"about_ca_system_score_gemma":0.00251926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002665988,"about_ca_topic_score_gemma":0.01135752,"domain_scores_codex":[0.9998609,0.00002315306,0.000005700134,0.00003462353,0.00003092698,0.00004478208],"domain_scores_gemma":[0.9998046,0.00003115153,0.00002201383,0.00001867306,0.00004984785,0.00007374535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000921783,0.002236546,0.03079776,0.002058889,0.00006138744,0.004067858,0.01003613,0.0007570898,0.09366406,0.01041122,0.02338357,0.8216038],"study_design_scores_gemma":[0.0001287056,0.002137917,0.1106252,0.0008597316,0.0001191329,0.003295886,0.01761457,0.001154886,0.04171601,0.00338031,0.8188474,0.0001202303],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7420259,0.007990492,0.01296209,0.004417436,0.0006538233,0.001211278,0.001518725,0.0007450085,0.2284752],"genre_scores_gemma":[0.5919384,0.006736574,0.02968811,0.001134474,0.0000931549,0.0006618634,0.0009403692,0.0001408603,0.3686661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03166818,"threshold_uncertainty_score":0.1059406,"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."}}