{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001543331,0.0003609717,0.0002480866,0.00001572182,0.0002056901,0.0000448118,0.0007127695,0.0002884645,0.01309051],"category_scores_gemma":[0.00002325675,0.0003423103,0.0000911899,0.0001216692,0.000365364,0.000161224,0.0002994551,0.0003057844,0.001164462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001214756,"about_ca_system_score_gemma":0.00006202161,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9804156,"about_ca_topic_score_gemma":0.9616608,"domain_scores_codex":[0.9981354,0.00003449935,0.0002357509,0.0005903636,0.0004166394,0.0005873609],"domain_scores_gemma":[0.9982672,0.00001664132,0.0001154726,0.001021379,0.000002551429,0.000576785],"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":[0.000004388015,0.00005552248,0.002688839,0.00000756605,0.00001335801,0.0001710528,0.000007638724,0.0001699441,9.192323e-7,9.142009e-7,0.9965187,0.0003612075],"study_design_scores_gemma":[0.0000915372,0.00005471287,0.01640793,0.00001428239,0.00006706702,0.00004468225,0.000008262434,0.0001088104,0.000001099939,0.00002020119,0.9827926,0.000388814],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002155447,0.0000231392,0.00004316265,0.0001028766,0.000164806,0.0001753373,0.9964169,0.00002110506,0.0028371],"genre_scores_gemma":[0.00004857423,0.0002383642,0.001345869,0.0006405084,0.0002185603,0.00002008391,0.9969025,0.00003139594,0.0005541166],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01875481,"threshold_uncertainty_score":0.9999029,"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."}}