{"id":"W2209710632","doi":"10.1111/jlme.12315","title":"Privacy and Biobanking in China: A Case of Policy in Transition","year":2015,"lang":"en","type":"article","venue":"The Journal of Law Medicine & Ethics","topic":"Ethics in Clinical Research","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"National Human Genome Research Institute; Chinese Academy of Medical Sciences; Chinese Universities Scientific Fund; Ministry of Science and Technology of the People's Republic of China; Chinese Academy of Sciences; Academy of Medical Sciences","keywords":"Biobank; China; Government (linguistics); Economic growth; Urbanization; Population; Epidemiological transition; Public health; Population ageing; Industrialisation; Business; Health care; Environmental health; Political science; Medicine; Economics; Law","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":["metaresearch","research_integrity"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03796849,0.00009830557,0.0005016227,0.0003446746,0.00003955153,0.000004050461,0.0001795013,0.0003847086,0.000009939995],"category_scores_gemma":[0.05496302,0.00005610535,0.00004148213,0.0004813474,0.001191235,0.00008895885,0.00006240259,0.006687829,4.809462e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000141063,"about_ca_system_score_gemma":0.001554862,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007300002,"about_ca_topic_score_gemma":0.004686477,"domain_scores_codex":[0.9967275,0.0007489343,0.0009893351,0.0000925742,0.001237826,0.0002038541],"domain_scores_gemma":[0.9864843,0.01192894,0.0003238684,0.0002718457,0.0007456134,0.0002454687],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.005966762,0.0008441424,0.008958937,0.002850397,0.0001449933,0.01196925,0.7038325,0.0002642384,0.008781463,0.2509787,0.0002151318,0.005193423],"study_design_scores_gemma":[0.02847691,0.01106386,0.01707246,0.02292802,0.0004264934,0.04182403,0.03097786,0.0009152595,0.001981694,0.8429626,0.001093961,0.000276837],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8174056,0.001423065,0.0001851689,0.1786616,0.00007531204,0.0001541357,9.055812e-7,0.000002771478,0.002091468],"genre_scores_gemma":[0.9937083,0.002840754,0.0003851418,0.00262111,0.0004072663,6.017061e-7,4.647949e-7,0.00001278487,0.00002362637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6728547,"threshold_uncertainty_score":0.9993105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5892120725566127,"score_gpt":0.6150427715447462,"score_spread":0.0258306989881335,"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."}}