{"id":"W7005387561","doi":"","title":"Profile of census tracts in Calgary, 2006 Census : map volume","year":2009,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Comparative Animal Anatomy Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Census; Volume (thermodynamics); Field (mathematics); Population; Data collection","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":[0.0004149525,0.0009260697,0.0008018665,0.008150545,0.0009543315,0.001306595,0.001635731,0.0006126261,0.04340612],"category_scores_gemma":[0.003861312,0.0007852041,0.000488479,0.02004829,0.0002846031,0.0008647285,0.00116328,0.001243321,0.02928533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003891927,"about_ca_system_score_gemma":0.01052362,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7001312,"about_ca_topic_score_gemma":0.7545763,"domain_scores_codex":[0.9993947,0.00004489814,0.00008570092,0.0001277949,0.0002041979,0.0001428111],"domain_scores_gemma":[0.9976276,0.0001447932,0.0003908824,0.0001401458,0.001401408,0.000295141],"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":[0.0001286336,0.00006519373,0.06220831,0.0008241233,0.00005473808,0.00009758314,0.0006247113,0.0009774421,0.000396389,0.0006689059,0.9051843,0.02876967],"study_design_scores_gemma":[0.00004091745,0.00003048973,0.7011941,0.000350899,0.00002472864,0.0001463585,0.001089272,0.000647956,0.0002039592,0.000186543,0.296057,0.00002786421],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.009403431,0.0005007036,0.0002324269,0.0001433416,0.00006976711,0.0001953439,0.9771973,0.0003059164,0.01195167],"genre_scores_gemma":[0.03298988,0.002274384,0.002141122,0.0002655941,0.00005268595,0.00076178,0.9085278,0.0001971538,0.05278961],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2998688,"threshold_uncertainty_score":0.6032695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0101790680885527,"score_gpt":0.220610936205079,"score_spread":0.2104318681165263,"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."}}