{"id":"W6939147289","doi":"10.6068/dp14ba7c6ba9461","title":"Trend 1994 - 2004. Statistics Canada. CANSIM: Seniors - Work and Retirement | Country: Canada | Table: Federal personnel engaged in science and technology activities, by type of science and personnel category | Variable: Administration of extramural related scientific activities programs, Other personnel, Natural sciences and engineering | Units: # Persons, 1994-2004. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-186.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Mycorrhizal Fungi and Plant Interactions","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Census; Economic statistics; Official statistics; Population; Socioeconomic status; Work (physics); Statistics education; Population 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":[0.00220503,0.002366875,0.002873532,0.0081019,0.003559621,0.004792842,0.005506002,0.001612199,0.08874623],"category_scores_gemma":[0.01922611,0.001730397,0.002133283,0.03523258,0.0005741467,0.00230537,0.002362622,0.003062282,0.05459755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04671356,"about_ca_system_score_gemma":0.1190283,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9930533,"about_ca_topic_score_gemma":0.9913501,"domain_scores_codex":[0.9959587,0.0002641854,0.000485972,0.0005588588,0.001781367,0.000950882],"domain_scores_gemma":[0.967071,0.001177462,0.001120305,0.001027151,0.0279174,0.001686623],"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":[0.000024857,0.000006108276,0.001023859,0.0002200322,0.00001971231,0.000005127112,0.00002034235,0.00007558719,0.000006665925,0.0002790962,0.9969655,0.001353164],"study_design_scores_gemma":[0.000197765,0.00001483181,0.02827953,0.001002844,0.00008640734,0.00002398896,0.0004987977,0.0003983553,0.0001545342,0.0006249897,0.9686297,0.00008822946],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005241157,0.00004527939,0.00002203518,0.0001094166,0.00002565141,0.0000142206,0.9989938,0.00004941313,0.0006878097],"genre_scores_gemma":[0.0007521654,0.0002632074,0.000327298,0.0001568456,0.00001994398,0.0001472647,0.9943928,0.00009260011,0.003847946],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08874623,"threshold_uncertainty_score":0.3389322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02142254699146514,"score_gpt":0.2231038093149749,"score_spread":0.2016812623235098,"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."}}