{"id":"W6939413207","doi":"10.6068/dp14ba8cbb2af94","title":"Most Recent Data (2005). Statistics Canada. CANSIM: Science and Technology - Innovation | Country: Canada | Table: Survey of innovation, logging and manufacturing industries, percentage of plants with expenditures on new machinery or equipment that were supplied from different locations | Variable: From the United States, Furniture and related product manufacturing, 25% to 49% of expenditures, Non-innovative plants | Units: %, 2005. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-181.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"History of Computing Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Census; Official statistics; Product (mathematics); Publication; Summary statistics; Business statistics; Statistical analysis; 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":[0.002172715,0.002519809,0.003032561,0.009758961,0.003657227,0.005292639,0.004990903,0.001564255,0.08314566],"category_scores_gemma":[0.01975491,0.001745547,0.002062483,0.05598422,0.0007060385,0.002450723,0.002162916,0.003159916,0.05117465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06144243,"about_ca_system_score_gemma":0.1526978,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9950566,"about_ca_topic_score_gemma":0.9929436,"domain_scores_codex":[0.9944347,0.0003018091,0.0006326368,0.0005991074,0.002780227,0.001251518],"domain_scores_gemma":[0.9537963,0.001654766,0.001333206,0.001007535,0.04041498,0.001793191],"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":[0.00002043545,0.000008422818,0.001033361,0.0002504591,0.00001743994,0.000006313864,0.00001955934,0.0001031962,0.000007300528,0.0002749583,0.9970104,0.001248157],"study_design_scores_gemma":[0.0001704442,0.00001495299,0.03367911,0.0008429814,0.00008327069,0.00002543238,0.0006413074,0.0003799548,0.0001846884,0.0005034807,0.9633857,0.00008876079],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005332948,0.00004990795,0.00001441919,0.0001035151,0.00002049535,0.0000111874,0.9989911,0.00003707819,0.0007189898],"genre_scores_gemma":[0.0008170237,0.0002918372,0.000297076,0.0001527412,0.00001690448,0.00009786797,0.994346,0.0000700831,0.003910609],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08314566,"threshold_uncertainty_score":0.4457981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03412578613030923,"score_gpt":0.2508407005230258,"score_spread":0.2167149143927165,"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."}}