{"id":"W6901666475","doi":"10.6068/dp14ba847e17675","title":"Trend 1982 - 1996. Statistics Canada. CANSIM: Business, Consumer and Property Services - Personal Services | Country: Canada | Table: Selected service industries | Variable: Number of firms (businesses), Services to buildings and dwellings | Units: $CAD, 1982-1996. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-012.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Service (business); Economic statistics; Summary statistics; Official statistics; Descriptive statistics; Socioeconomic status; Statistical analysis","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":[0.001866708,0.002492263,0.002661802,0.009256269,0.003604995,0.005053137,0.005293512,0.001442603,0.09772173],"category_scores_gemma":[0.01603168,0.001758907,0.001778408,0.04535634,0.000672756,0.002759685,0.002212961,0.003070076,0.06299792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05977672,"about_ca_system_score_gemma":0.1451745,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9958119,"about_ca_topic_score_gemma":0.9942818,"domain_scores_codex":[0.9954951,0.0002406417,0.0004394466,0.0006041723,0.002141813,0.001078835],"domain_scores_gemma":[0.9663169,0.0009873459,0.001010643,0.0009230271,0.02908228,0.001679795],"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":[0.00002008373,0.000005693626,0.0009287324,0.0001907419,0.00001647288,0.000007134648,0.00002233509,0.0001060607,0.00000918594,0.0004368018,0.9967429,0.001513934],"study_design_scores_gemma":[0.0001082527,0.00001068605,0.0216041,0.0006209071,0.00005476717,0.00002456559,0.0004872046,0.0004098988,0.0001672357,0.0005599858,0.9758819,0.00007044327],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005967089,0.00004891706,0.00002393139,0.000128066,0.00002564226,0.0000131584,0.9985023,0.00005400103,0.001144389],"genre_scores_gemma":[0.000968483,0.0003127167,0.000343892,0.0001509994,0.00001727833,0.0001094419,0.99146,0.0001175383,0.006519644],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09772173,"threshold_uncertainty_score":0.4337125,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01919020114369424,"score_gpt":0.234388926057539,"score_spread":0.2151987249138447,"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."}}