{"id":"W6907627917","doi":"10.25318/2710000101-eng","title":"Average business enterprise in-house research and development expenditures, by industry group based on the North American Industry Classification System (NAICS) and country of control","year":2019,"lang":"en","type":"dataset","venue":"Statistics Canada Dissemination","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Table (database); Production (economics); Control (management); Agriculture; Logging; Agribusiness; Fishing; Forest industry","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.000657565,0.001117856,0.001081524,0.006501629,0.0007770693,0.001907212,0.001469574,0.0006100374,0.03311945],"category_scores_gemma":[0.006708135,0.0005539525,0.0007407262,0.01857871,0.0002619564,0.001077865,0.0008607182,0.001357681,0.02937713],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004937737,"about_ca_system_score_gemma":0.008986478,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5991458,"about_ca_topic_score_gemma":0.6301104,"domain_scores_codex":[0.9984925,0.0000905835,0.0001996153,0.000312948,0.0006232637,0.0002811312],"domain_scores_gemma":[0.9951037,0.0005553456,0.000675725,0.0003543342,0.002958509,0.0003523407],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000388239,0.00001826805,0.007324111,0.0002516699,0.00003533631,0.00001568724,0.0000238278,0.0003015794,0.00003264707,0.0006221912,0.9887339,0.002602038],"study_design_scores_gemma":[0.000114061,0.00001909087,0.08657086,0.0003425048,0.00006415579,0.00005921083,0.0003138225,0.0006812133,0.0003215747,0.0004695804,0.911005,0.00003888656],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0005681599,0.00005608274,0.00002902304,0.0000450911,0.00001549731,0.000007988243,0.9980761,0.00004207913,0.001160021],"genre_scores_gemma":[0.00175926,0.0001304617,0.0001039486,0.00003072766,0.000009701732,0.00003932242,0.995546,0.00002137572,0.002359091],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.9993424,"threshold_uncertainty_score":0.8064297,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01870162958370577,"score_gpt":0.2844958923723588,"score_spread":0.265794262788653,"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."}}