{"id":"W2911679033","doi":"10.5683/sp/eug3dt","title":"Canadian Longitudinal Tract Database","year":2018,"lang":"en","type":"dataset","venue":"Borealis","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Western University","funders":"","keywords":"Apportionment; Census; Census tract; Documentation; Geocoding; Database; Geography; Cartography; Computer science; Demography; Political science; Sociology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001521304,0.001363397,0.001635269,0.007633701,0.003040066,0.003062096,0.003928835,0.001111819,0.06758665],"category_scores_gemma":[0.01375423,0.0006990536,0.001301183,0.02339583,0.0004842922,0.001052966,0.001720325,0.002270922,0.02584748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02689879,"about_ca_system_score_gemma":0.06667694,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9789377,"about_ca_topic_score_gemma":0.9861822,"domain_scores_codex":[0.9973277,0.0001956286,0.0003074104,0.0005226753,0.001120613,0.0005258377],"domain_scores_gemma":[0.9858983,0.0007436245,0.0005918012,0.0008516689,0.01105324,0.0008614394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003808298,0.000008650443,0.002092137,0.0002505569,0.00003981265,0.00002170089,0.00003737317,0.0001694381,0.00001992915,0.001068491,0.993068,0.003185793],"study_design_scores_gemma":[0.0000877922,0.000008125419,0.02724869,0.0004590066,0.00007483592,0.00005403312,0.000230917,0.0004758263,0.0001212776,0.0007937116,0.9703812,0.00006462645],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001434221,0.00007205205,0.00005999289,0.00008412113,0.00001633041,0.00002645674,0.9983462,0.00004792945,0.001203528],"genre_scores_gemma":[0.001457867,0.0002356257,0.0004214303,0.00008344399,0.00001131133,0.0001718674,0.9951649,0.00004170324,0.002411881],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06758665,"threshold_uncertainty_score":0.2260998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01334915370990164,"score_gpt":0.2317350499328069,"score_spread":0.2183858962229052,"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."}}