{"id":"W6976380284","doi":"10.6068/dp14ba89564ac43","title":"Trend 1999 - 2011. Statistics Canada. CANSIM: Seniors - Income, Pensions and Wealth | Country: Canada | Table: Business enterprise research and development (BERD) characteristics, by industry group based on the North American Industry Classification System (NAICS) | Variable: Research and development professionals (full-time equivalent), Other utilities | Units: $CAD, 1999-2011. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-185.","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; Official statistics; Government (linguistics); Summary statistics; Population; Personal income; Socioeconomic status; Social security; Investment (military)","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":[0.001685723,0.00223026,0.002395586,0.007617007,0.002871221,0.004609959,0.005110649,0.001560331,0.08011919],"category_scores_gemma":[0.01709688,0.00154812,0.002019588,0.03561869,0.0005610259,0.002361066,0.002328705,0.002969747,0.05537067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03767049,"about_ca_system_score_gemma":0.08516968,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9886696,"about_ca_topic_score_gemma":0.9875028,"domain_scores_codex":[0.9968786,0.0001987694,0.0003621653,0.0004528397,0.001346575,0.0007610089],"domain_scores_gemma":[0.9723306,0.001080692,0.0009933236,0.000966719,0.02330718,0.001321523],"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":[0.00002004804,0.000005520566,0.0008717283,0.000184583,0.0000167591,0.000005733297,0.00001583145,0.00008170668,0.000006633638,0.0002841679,0.9974188,0.001088505],"study_design_scores_gemma":[0.0001664186,0.00001091009,0.02236284,0.0008710458,0.00006224727,0.00002667039,0.0004314666,0.0004261969,0.0001724589,0.0006269868,0.9747627,0.00008009029],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004027228,0.00003105144,0.00001289089,0.00007239602,0.00001584862,0.000008256511,0.9993054,0.00003421853,0.0004797404],"genre_scores_gemma":[0.0004356071,0.0001362103,0.0001608964,0.00007370036,0.00001062367,0.00006795095,0.9970016,0.00004669711,0.002066628],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08011919,"threshold_uncertainty_score":0.2733198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08077646987224317,"score_gpt":0.3126577050172299,"score_spread":0.2318812351449867,"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."}}