{"id":"W2980976638","doi":"","title":"An Analysis of Marketing Channels of Biological Reagentsin China and Canada","year":2014,"lang":"zh","type":"article","venue":"亚洲社会药学","topic":"Corporate Identity and Reputation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"China; Business; Marketing; Geography; Political science; Law","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016161,0.0001397735,0.0004411872,0.0002790716,0.0001276726,0.00009787906,0.000151985,0.00008931028,0.0002334823],"category_scores_gemma":[0.0003908072,0.0001252567,0.00008657423,0.000793195,0.00008539815,0.0003176321,0.0001128839,0.00007262793,0.000001870536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002013599,"about_ca_system_score_gemma":0.00004306413,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6354228,"about_ca_topic_score_gemma":0.4469013,"domain_scores_codex":[0.998733,0.0001260081,0.0004444357,0.0002741856,0.0002308527,0.0001914953],"domain_scores_gemma":[0.9988399,0.0001085653,0.000650017,0.0002093878,0.0001708624,0.00002123283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001611799,0.0001580712,0.9766698,0.0006740046,0.000625967,0.000006703688,0.00009966334,0.004362156,0.00131458,0.004994524,0.00052723,0.01040613],"study_design_scores_gemma":[0.0002025381,0.00002378891,0.9323698,0.00007359152,0.000769651,2.996818e-7,0.0002329503,0.06471846,0.00003610369,0.0007332565,0.0006879067,0.0001516111],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960285,0.0001448923,0.0003578275,0.0002141145,0.0003159227,0.0001046984,0.00001219125,0.00001103756,0.002810825],"genre_scores_gemma":[0.9992527,0.00005982407,0.00005808769,0.0000958004,0.0002865444,0.000001322482,0.0001040956,0.000008289332,0.0001333448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1885215,"threshold_uncertainty_score":0.5631913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01378570717159423,"score_gpt":0.211407786174708,"score_spread":0.1976220790031138,"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."}}