{"id":"W6944948578","doi":"10.25318/2710010901-eng","title":"Private non-profit organizations research and development intramural expenditures by sources of funds","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":"Private sector; Government (linguistics); Investment (military); Key (lock); Identification (biology)","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":[0.001568633,0.001706287,0.00191998,0.008053531,0.00169501,0.003042657,0.002867778,0.001170536,0.07722087],"category_scores_gemma":[0.01449378,0.001046445,0.001143452,0.02367095,0.000542584,0.001420269,0.001565428,0.002332594,0.06243708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01675861,"about_ca_system_score_gemma":0.04262561,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8778354,"about_ca_topic_score_gemma":0.914659,"domain_scores_codex":[0.9964037,0.0002715634,0.0004286984,0.0005648534,0.001483057,0.0008480886],"domain_scores_gemma":[0.9869652,0.001040639,0.00106959,0.0007823236,0.00913491,0.001007343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00002121166,0.000004645563,0.0005628598,0.0001816689,0.000008966083,0.000004405303,0.000008426961,0.00005426069,0.000009170561,0.0003939576,0.9977406,0.001009752],"study_design_scores_gemma":[0.0001338471,0.000008734331,0.01351113,0.0004552028,0.00003130573,0.00002134363,0.0001327871,0.0001774309,0.000134505,0.0004900692,0.9848782,0.00002536616],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004107629,0.00003884525,0.00002197603,0.00007078056,0.00001619825,0.00001403827,0.9985421,0.00004239543,0.001212589],"genre_scores_gemma":[0.0005836979,0.0001805035,0.0002424179,0.00007377398,0.00001362846,0.00009667534,0.9932045,0.00005090591,0.005553955],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1221646,"threshold_uncertainty_score":0.2583296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01857444370952437,"score_gpt":0.3200903001949453,"score_spread":0.301515856485421,"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."}}