{"id":"W6901641734","doi":"10.6068/dp14ba7d0678311","title":"Trend 2007 - 2011. Statistics Canada. CANSIM: Business, Consumer and Property Services - Professional, Scientific and Technical Services | Country: Canada | Table: Software development and computer services, operating expenses, by North American Industry Classification System (NAICS) | Variable: Advertising, marketing and promotions, Computer systems design and related services | Units: %, 2007-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-013.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Official statistics; Census; Service (business); Publication; Software; Information system; Summary statistics; Socioeconomic status","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":[0.002082692,0.002439303,0.002833954,0.01015111,0.003620872,0.005514823,0.005255255,0.001673101,0.09576799],"category_scores_gemma":[0.02249623,0.001726282,0.002154161,0.04682029,0.0006661094,0.0029357,0.002419413,0.003451952,0.06301156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05124925,"about_ca_system_score_gemma":0.1473751,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9925971,"about_ca_topic_score_gemma":0.9907156,"domain_scores_codex":[0.9950331,0.0002935454,0.0005476069,0.0006215274,0.002417566,0.0010866],"domain_scores_gemma":[0.9565231,0.001597343,0.001219976,0.001175102,0.03773314,0.001751322],"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":[0.00001666815,0.000004805557,0.0007543724,0.0002032319,0.0000157853,0.000005565694,0.00001579387,0.00008297737,0.000006903287,0.0003284533,0.9973367,0.001228687],"study_design_scores_gemma":[0.0001271359,0.00001050839,0.01795471,0.0009442315,0.00007133077,0.00002673295,0.0004673585,0.0004401243,0.0001583237,0.0007026214,0.9790172,0.0000796668],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004704384,0.00005443396,0.00002405589,0.0001394937,0.00003119501,0.00001293894,0.9987832,0.00005203183,0.0008556725],"genre_scores_gemma":[0.0006827367,0.0003034501,0.0003330104,0.0001652272,0.00002174373,0.0001099543,0.9945721,0.00010323,0.003708472],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09576799,"threshold_uncertainty_score":0.3718411,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02084669221382764,"score_gpt":0.2318237567041622,"score_spread":0.2109770644903345,"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."}}