{"id":"W7009644434","doi":"","title":"Ep.46 - Tom Mulcair: Hot Prosecutor or Wet Napkin?","year":2016,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Language change; Public policy; Advice (programming)","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007811822,0.0006645255,0.0004389045,0.0008952078,0.004782706,0.005677666,0.001205022,0.005525234,0.4235017],"category_scores_gemma":[0.00570502,0.0004294489,0.0003218162,0.001177939,0.001203579,0.003493476,0.001943586,0.004164385,0.276883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003633227,"about_ca_system_score_gemma":0.004558883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1444401,"about_ca_topic_score_gemma":0.3539188,"domain_scores_codex":[0.9992478,0.0000910301,0.00002858429,0.00008304605,0.0003475909,0.0002018105],"domain_scores_gemma":[0.9981263,0.0004007989,0.00008806308,0.00009346405,0.0009208881,0.0003705042],"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":[0.000002283414,0.000001480923,0.00002231474,0.00001209913,2.487877e-7,0.00002437375,0.00003471601,0.000002771456,0.0000119472,0.0009551475,0.9965793,0.002353264],"study_design_scores_gemma":[0.000002170902,0.000001773083,0.0002702348,0.00005977913,6.977919e-7,0.00002979569,0.0001608226,0.00000728757,0.00002246619,0.0002696846,0.9991722,0.000003104587],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0003427968,0.006673007,0.0001858264,0.05008172,0.009352941,0.00007730831,0.001960302,0.0003737047,0.9309525],"genre_scores_gemma":[0.001315263,0.0008373524,0.00003401739,0.01254814,0.0006158439,0.00002085262,0.000207687,0.0001461769,0.9842746],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.5764983,"threshold_uncertainty_score":0.8223048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007835112874374186,"score_gpt":0.2070952627195194,"score_spread":0.1992601498451453,"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."}}