{"id":"W2946846535","doi":"10.5539/jas.v11n8p280","title":"Analysis on Production Efficiency of Laying Hens in China—Based on the Survey Data of Five Provinces","year":2019,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Animal Nutrition and Physiology","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Laying; Rationalization (economics); China; Production (economics); Field survey; Survey data collection; Business; Geography; Agricultural science; Agricultural economics; Engineering; Economics; Biology; Mathematics; Statistics; Cartography; Management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001022471,0.0003079289,0.0002703977,0.001895698,0.0002941857,0.0004109858,0.0003956061,0.0001616484,0.001173427],"category_scores_gemma":[0.001825005,0.0001868477,0.0005979383,0.003069266,0.0002330765,0.0003547783,0.0003457405,0.0001554739,0.0001362038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001501851,"about_ca_system_score_gemma":0.001473554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2039228,"about_ca_topic_score_gemma":0.1833464,"domain_scores_codex":[0.9992629,0.0001236936,0.00009106168,0.0001797702,0.0001874506,0.0001551508],"domain_scores_gemma":[0.9983353,0.0004336837,0.0004037404,0.0001482678,0.0005399641,0.0001391598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002904269,0.00001959442,0.9941373,0.00003826976,0.00007383229,0.00005030403,0.0001132453,0.001539059,0.0003271768,0.00006200776,0.0001978413,0.00341227],"study_design_scores_gemma":[0.000002358664,0.0000187366,0.9977036,0.000003121386,0.00001453888,0.00001670758,0.0002580644,0.001644147,0.0001192647,0.00001008716,0.0002061375,0.000003244595],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973983,0.0000554166,0.0003119761,0.00001981374,0.000001108087,0.00001199956,0.001697878,0.000006322148,0.0004972157],"genre_scores_gemma":[0.9966748,0.000055239,0.0002777188,0.000007131568,0.000001179317,0.00001501953,0.002605432,0.000001467159,0.0003620796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2039228,"threshold_uncertainty_score":0.4054717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03464541338830605,"score_gpt":0.2596719249064069,"score_spread":0.2250265115181008,"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."}}