{"id":"W6904639414","doi":"10.1371/journal.pone.0275923.s001","title":"Canadian Longitudinal Study on Aging (CLSA) participants’ flow from the baseline CLSA study through to follow-up 1 and inclusion into our analyses as relevant.","year":2022,"lang":"en","type":"article","venue":"Figshare","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Longitudinal study; Baseline (sea); Flow (mathematics); Inclusion (mineral); Longitudinal data","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":[0.004796753,0.0006578517,0.0008379542,0.002849313,0.005062193,0.001891656,0.00219421,0.001027417,0.02208493],"category_scores_gemma":[0.01853054,0.0004411314,0.001468351,0.004109282,0.0005125629,0.001218227,0.001519436,0.002695029,0.001887846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007699169,"about_ca_system_score_gemma":0.03259824,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8951119,"about_ca_topic_score_gemma":0.9408632,"domain_scores_codex":[0.9964023,0.0006972103,0.000344357,0.0005162649,0.0009364131,0.001103397],"domain_scores_gemma":[0.9961617,0.0003745075,0.0004318971,0.0004453633,0.00222579,0.0003607753],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.002391465,0.000470613,0.3354908,0.001661472,0.0006856944,0.0002945307,0.005897809,0.0006425453,0.0006449343,0.01640675,0.567315,0.06809831],"study_design_scores_gemma":[0.0005145529,0.0002968441,0.6835436,0.002291495,0.0006467701,0.0002110729,0.005172604,0.001777749,0.001183535,0.0053659,0.2987503,0.0002456431],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.1701593,0.002898763,0.009094224,0.008299605,0.001563142,0.006779996,0.7396198,0.0004813809,0.06110382],"genre_scores_gemma":[0.5897213,0.003684779,0.0263404,0.005346756,0.0003802385,0.01938996,0.2929166,0.0005484506,0.06167147],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1048881,"threshold_uncertainty_score":0.2110116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2779800203883893,"score_gpt":0.468910402187608,"score_spread":0.1909303817992187,"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."}}