{"id":"W4385971767","doi":"10.1371/journal.pone.0289748","title":"Structural-demographic analysis of the Qing Dynasty (1644–1912) collapse in China","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Demographic Trends and Gender Preferences","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"George Brown College","funders":"Österreichische Forschungsförderungsgesellschaft; V. Kann Rasmussen Foundation","keywords":"Elite; China; Politics; State (computer science); Political economy; Population; History; Ecological succession; Development economics; Geography; Political science; Sociology; Demography; Economics; Law; Biology; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004752781,0.000151321,0.000146418,0.001437897,0.0007774457,0.0004687729,0.0002994165,0.000186649,0.002142815],"category_scores_gemma":[0.001012354,0.0001103714,0.0002287554,0.001769909,0.0005807558,0.0003256264,0.0005577857,0.0002951951,0.00008934959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002654731,"about_ca_system_score_gemma":0.001580124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1091987,"about_ca_topic_score_gemma":0.1190851,"domain_scores_codex":[0.9998865,0.00002199773,0.00000602609,0.00001669423,0.00002007888,0.00004870371],"domain_scores_gemma":[0.9996241,0.0000491002,0.0001268971,0.00002065116,0.00008447272,0.00009464159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008269589,0.00003861342,0.9660702,0.00004221172,0.00005811173,0.0003488316,0.006420469,0.004073759,0.0008448961,0.009731332,0.00110606,0.01118289],"study_design_scores_gemma":[0.000002716316,0.00002650872,0.9942021,0.000006263996,0.000007079007,0.00002622826,0.001885869,0.002144033,0.00006547834,0.000434484,0.001192472,0.000006735827],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984677,0.000044583,0.00007906456,0.0001068436,0.000001664585,0.000004085125,0.0002087951,0.000002047677,0.001085165],"genre_scores_gemma":[0.9993927,0.00003981372,0.00003382351,0.00000840371,0.000003408734,0.00000357095,0.0002606458,8.73518e-7,0.0002565751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1091987,"threshold_uncertainty_score":0.2171261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0643878431512158,"score_gpt":0.2826143547484923,"score_spread":0.2182265115972765,"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."}}