{"id":"W2125075470","doi":"10.1017/s071498081500029x","title":"Mining a Unique Canadian Resource: The Canadian Longitudinal Study on Aging","year":2015,"lang":"en","type":"article","venue":"Canadian Journal on Aging / La Revue canadienne du vieillissement","topic":"Health disparities and outcomes","field":"Social Sciences","cited_by":109,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre; Simon Fraser University; Centres Intégré Universitaires de Santé et de Services Sociaux; University of Manitoba; Université de Sherbrooke; McMaster University; Dalhousie University","funders":"Canadian Institutes of Health Research","keywords":"Resource (disambiguation); Information resource; Computer science; Content (measure theory); World Wide Web; Data science; Knowledge management; Computer network; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.003564875,0.000555687,0.001120489,0.006260132,0.008256455,0.004267635,0.00300085,0.0009942581,0.005710519],"category_scores_gemma":[0.01344878,0.0006396893,0.0007651512,0.03450701,0.0008890803,0.002175176,0.004005099,0.001826749,0.001023568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0519009,"about_ca_system_score_gemma":0.1566955,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9982297,"about_ca_topic_score_gemma":0.9994245,"domain_scores_codex":[0.9961354,0.0004691655,0.0002822804,0.0003914964,0.001791615,0.0009301747],"domain_scores_gemma":[0.9919751,0.0003957492,0.0006500101,0.0005052011,0.005277048,0.001196905],"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":[0.00008147945,0.0001081895,0.4434675,0.0004473725,0.0002770452,0.0002499241,0.009025305,0.0003588647,0.0001355536,0.006607719,0.4754986,0.0637425],"study_design_scores_gemma":[0.00004687836,0.00001945987,0.7556897,0.001147975,0.0002741734,0.0001347923,0.03288352,0.0008046303,0.0002070069,0.001938481,0.2066787,0.0001744968],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.4097229,0.01581718,0.004420643,0.06495687,0.002006808,0.001760115,0.4256626,0.0002762434,0.07537652],"genre_scores_gemma":[0.7766896,0.01757291,0.01623145,0.009743532,0.0003188868,0.002064642,0.1537456,0.000346754,0.02328675],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0519009,"threshold_uncertainty_score":0.3765692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06058991985176281,"score_gpt":0.3118158426594409,"score_spread":0.2512259228076781,"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."}}