{"id":"W6901626573","doi":"10.6068/dp14ba8f54f2235","title":"Most Recent Data (2003). Statistics Canada. CANSIM: Science and Technology - Research and Development | Country: Canada | Table: Survey of innovation, selected service industries, percentage of business units that used programs sponsored by the federal or provincial and/or territorial governments | Variable: Government venture capital support, Provincial and/or territorial government, Scientific research and development services, All business units | Units: %, 2003. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-182.","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; Government (linguistics); Census; Descriptive statistics; Publication; Service (business); Business statistics; Unit (ring theory)","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":[0.002713221,0.002505535,0.003075763,0.01059091,0.00421916,0.005688819,0.005533165,0.001781582,0.09858789],"category_scores_gemma":[0.02704101,0.001904264,0.002203139,0.06102195,0.000803012,0.002651231,0.002397023,0.003728354,0.05968171],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.07089607,"about_ca_system_score_gemma":0.1843222,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9954395,"about_ca_topic_score_gemma":0.9932643,"domain_scores_codex":[0.9936394,0.0003558494,0.0007482789,0.0006813962,0.003193313,0.001381719],"domain_scores_gemma":[0.9393845,0.002418826,0.001593452,0.00142971,0.0528915,0.002282027],"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":[0.00001946607,0.000007584952,0.0008176654,0.0002370415,0.0000146295,0.000005809143,0.00001964473,0.00008823129,0.000007151695,0.0002613898,0.9973339,0.00118755],"study_design_scores_gemma":[0.0002087704,0.00001351979,0.02992098,0.0009435834,0.00008337619,0.00002444772,0.0006365278,0.0003639913,0.0001801603,0.0006398726,0.9668896,0.00009520026],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004264232,0.00004447423,0.00001493256,0.0001165835,0.00002427217,0.00001376504,0.998939,0.00004083529,0.0007636042],"genre_scores_gemma":[0.0008722577,0.000322724,0.0004072321,0.0002040226,0.00002121007,0.0001602799,0.9935241,0.000101687,0.004386625],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9291039,"threshold_uncertainty_score":0.5143894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0755891430442704,"score_gpt":0.2915929417521694,"score_spread":0.216003798707899,"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."}}