{"id":"W6901674235","doi":"10.6068/dp14ba8ed695259","title":"Most Recent Data (2005). Statistics Canada. CANSIM: Science and Technology - Research and Development | Country: Canada | Table: Survey of innovation, logging and manufacturing industries, percentage of plants with research and development services that were supplied from different locations | Variable: From the rest of Canada, Petroleum and coal products manufacturing, Plants that bought research and development services, All plants | Units: %, 2005. 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; Census; Official statistics; Publication; Summary statistics; Social research; Statistical analysis; Social statistics; Logging","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":[0.002394947,0.002580495,0.002910537,0.01011395,0.003954803,0.005639844,0.004965905,0.00172359,0.09871046],"category_scores_gemma":[0.02374379,0.001796771,0.002049744,0.05590888,0.0007768284,0.002715219,0.002293552,0.003371325,0.05910049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0654805,"about_ca_system_score_gemma":0.1657943,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9951187,"about_ca_topic_score_gemma":0.9932399,"domain_scores_codex":[0.9940661,0.0003159942,0.0006953034,0.0006406758,0.002953866,0.001328043],"domain_scores_gemma":[0.9473428,0.002036364,0.001422766,0.001193596,0.04594756,0.00205692],"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":[0.00001741899,0.000006876699,0.0007881909,0.0002331841,0.00001440259,0.000005715874,0.00001763261,0.00009189732,0.000007078654,0.0002580655,0.9974183,0.00114127],"study_design_scores_gemma":[0.0001658185,0.0000124894,0.02604659,0.0008568128,0.00007422797,0.00002366658,0.0005715948,0.0003317232,0.0001737004,0.000546655,0.9711059,0.00009067832],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004266117,0.0000476596,0.00001472769,0.0001102402,0.00002371241,0.00001141142,0.9989583,0.00004021901,0.0007510711],"genre_scores_gemma":[0.0007993405,0.0003186071,0.0003469572,0.0001907971,0.00001925172,0.0001156076,0.9938543,0.00008860283,0.004266625],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09871046,"threshold_uncertainty_score":0.4750965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09104403610200706,"score_gpt":0.2960932740495523,"score_spread":0.2050492379475452,"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."}}