{"id":"W7017202562","doi":"","title":"AICan 2020 - CIFAR Pan-Canadian AI Strategy Impact Report (2020)","year":2022,"lang":"en","type":"other","venue":"ANU Open Research (Australian National University)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Set (abstract data type); Field (mathematics); Perspective (graphical); Identification (biology); Process (computing); Key (lock)","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":[0.002518221,0.00179854,0.0007921658,0.004479855,0.004377076,0.008095982,0.002418293,0.003041004,0.1837027],"category_scores_gemma":[0.004859607,0.00071668,0.0009643206,0.00533388,0.0009427606,0.001673429,0.002595454,0.002599675,0.08252695],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03921263,"about_ca_system_score_gemma":0.1434111,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9617171,"about_ca_topic_score_gemma":0.9652892,"domain_scores_codex":[0.9967775,0.0001195656,0.00005887793,0.0001048993,0.002028919,0.000910231],"domain_scores_gemma":[0.9919239,0.0001436896,0.00007969631,0.0001321885,0.006206018,0.001514496],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00004033256,0.00002108117,0.0002832563,0.00008648267,0.000007386691,0.00001858174,0.00002627377,0.0001708856,0.0000840662,0.004146434,0.9858339,0.009281281],"study_design_scores_gemma":[0.0000147367,0.000007482232,0.001486971,0.00005694476,0.000006421253,0.00001140758,0.0001008702,0.0002111205,0.0001641636,0.0007078177,0.9972165,0.00001562441],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.001667946,0.001728733,0.00176215,0.007867119,0.003032724,0.000580259,0.1483883,0.002530353,0.8324425],"genre_scores_gemma":[0.009292645,0.001690932,0.004814581,0.003205989,0.0001739609,0.000312815,0.06767287,0.0009509341,0.9118853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9974818,"threshold_uncertainty_score":0.6145468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1286315244748886,"score_gpt":0.4188284766486461,"score_spread":0.2901969521737575,"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."}}