{"id":"W1628303704","doi":"","title":"Innovation of China’s Grass-Root Agricultural Extension Team With ICTs","year":2014,"lang":"en","type":"article","venue":"Cross-cultural communication","topic":"Technology and Security Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"ICTS; Agricultural extension; Extension (predicate logic); China; Agriculture; Business; Root (linguistics); Agricultural machinery; Service (business); Agricultural communication; Political science; Information and Communications Technology; Marketing; Geography; Computer science","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":[0.002425137,0.0002520759,0.00013459,0.0007613124,0.003485567,0.002033018,0.0006989121,0.0007299265,0.004721709],"category_scores_gemma":[0.001761044,0.0001322575,0.0002783872,0.0009591285,0.001792453,0.001565392,0.003003119,0.0008775147,0.0004947702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005282078,"about_ca_system_score_gemma":0.01531429,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01890957,"about_ca_topic_score_gemma":0.02352064,"domain_scores_codex":[0.9983285,0.0004342382,0.00006213795,0.000194758,0.0003488405,0.0006315318],"domain_scores_gemma":[0.9978561,0.0002595129,0.0001774509,0.0001062682,0.0003308795,0.001269746],"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":[0.0004715174,0.0008750827,0.1527631,0.001112824,0.0000805263,0.007815763,0.1214135,0.003484171,0.0188144,0.08619652,0.04424769,0.5627248],"study_design_scores_gemma":[0.0001680844,0.001262999,0.2415653,0.0006961504,0.00008718272,0.001712049,0.08641881,0.008067084,0.005853545,0.008813032,0.6451902,0.0001655831],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8728153,0.001995434,0.004395668,0.01236718,0.0005337433,0.0003719314,0.00007083672,0.0001426966,0.1073072],"genre_scores_gemma":[0.9644884,0.0009573961,0.00276483,0.0007631844,0.00007473372,0.00007853589,0.00005930089,0.00001164171,0.03080195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01890957,"threshold_uncertainty_score":0.0383243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01430516131654269,"score_gpt":0.2769089437839937,"score_spread":0.262603782467451,"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."}}