{"id":"W7099508265","doi":"","title":"Copyright © Canadian Academy of Oriental and Occidental Culture Socioeconomic-Demographic Characteristics and Supporting Resources of the Chinese Elderly","year":2014,"lang":"en","type":"article","venue":"","topic":"Legal case studies and regulations","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Government (linguistics); China; Work (physics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004708841,0.0002187468,0.0002559149,0.001447017,0.002281098,0.001450891,0.0004642573,0.0002428899,0.06174586],"category_scores_gemma":[0.001505252,0.0001466891,0.0002246262,0.003592058,0.000730628,0.0004431074,0.0006587476,0.0002653798,0.003092706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005500276,"about_ca_system_score_gemma":0.01454034,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.8749443,"about_ca_topic_score_gemma":0.9303295,"domain_scores_codex":[0.9997452,0.0000219728,0.00002677624,0.00003386313,0.00006380142,0.0001083449],"domain_scores_gemma":[0.9990408,0.0001092919,0.0001183958,0.00004874175,0.0004228399,0.0002600315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001035772,0.00009581446,0.8298793,0.0003526763,0.00004039412,0.0008685148,0.007020386,0.0001053935,0.0003601235,0.003761137,0.07733177,0.08008092],"study_design_scores_gemma":[0.000005241748,0.00001257314,0.9546881,0.00015963,0.00001636754,0.0002196204,0.008731557,0.00007061791,0.00006559479,0.0001935037,0.03582421,0.00001299067],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8276994,0.00407751,0.0001678339,0.00544759,0.0004277125,0.00009014217,0.01400487,0.00003664685,0.1480484],"genre_scores_gemma":[0.9352152,0.005354343,0.000495729,0.001117911,0.0001016398,0.00007891622,0.004252171,0.00001788914,0.05336617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8749443,"threshold_uncertainty_score":0.2515844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004765887156246031,"score_gpt":0.2607220525891931,"score_spread":0.255956165432947,"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."}}