{"id":"W6976569392","doi":"10.6068/dp14ba845f7c498","title":"Trend 2001 - 2011. Statistics Canada. CANSIM: Science and Technology - Research and Development | Country: Canada | Table: Business enterprise extramural payments for research and development, by location of recipient and North American Industry Classification System (NAICS) | Variable: Information and cultural industries (x 1,000,000), Foreign | Units: $CAD, 2001-2011. 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":"Census; Economic statistics; Publication; Official statistics; Payment; Business statistics; Statistical analysis; Statistics education; Summary statistics","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":[0.002206889,0.002546802,0.00284404,0.009852051,0.003619305,0.005723103,0.005129522,0.001742896,0.1074453],"category_scores_gemma":[0.02259295,0.001804012,0.002054114,0.04762837,0.0006994455,0.00296069,0.00242351,0.00336481,0.07017561],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05006505,"about_ca_system_score_gemma":0.1399186,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9917318,"about_ca_topic_score_gemma":0.9892217,"domain_scores_codex":[0.9953108,0.0002940159,0.0005356714,0.0006023215,0.002205183,0.001051843],"domain_scores_gemma":[0.9572271,0.001687744,0.001251277,0.001298253,0.03674506,0.001790662],"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":[0.00001734579,0.000004433335,0.0006378747,0.0002036488,0.00001400036,0.000005217064,0.00001393667,0.00008411334,0.000006850014,0.0003185449,0.9975182,0.001175922],"study_design_scores_gemma":[0.0001326295,0.000009696988,0.01522811,0.0007879127,0.00005684173,0.00002280502,0.0003734574,0.0003716537,0.0001573671,0.000662088,0.9821241,0.00007333124],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003585425,0.00004334395,0.00002080659,0.0001191215,0.00002986937,0.00001194419,0.9988709,0.00005656568,0.0008115323],"genre_scores_gemma":[0.0005869718,0.000273404,0.0003277436,0.0001484734,0.00002037584,0.000119957,0.9944028,0.0001157457,0.004004554],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.949935,"threshold_uncertainty_score":0.3632491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08924269036811698,"score_gpt":0.3129913695640036,"score_spread":0.2237486791958866,"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."}}