{"id":"W6928796025","doi":"10.4224/40000408","title":"100 years of innovation for Canada","year":2017,"lang":"en","type":"report","venue":"NRC Digital Repository","topic":"Genomics and Rare Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Work (physics); Agency (philosophy); Field (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007855238,0.0001302712,0.0001894876,0.0000393463,0.0000533839,0.00006331971,0.0002255001,0.0001922755,6.642348e-7],"category_scores_gemma":[0.0007394359,0.000141413,0.0001024468,0.00002523806,0.00004854276,0.000003402872,0.00008496371,0.00004688399,2.677321e-7],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001074532,"about_ca_system_score_gemma":0.009615864,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02610609,"about_ca_topic_score_gemma":0.009342832,"domain_scores_codex":[0.9989391,0.000003553624,0.0003279649,0.0002629168,0.0003367039,0.0001297708],"domain_scores_gemma":[0.9978017,0.000007989415,0.0004873713,0.0004705651,0.001190433,0.0000419903],"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":[0.0001182607,0.0001084436,0.00240019,0.0005223211,0.0004469138,0.00006805207,0.000006017481,0.00001136811,0.04650339,0.00003785584,0.8736122,0.07616502],"study_design_scores_gemma":[0.0001248283,0.00009069256,0.003434394,0.00004544173,0.00003144411,0.00003922238,0.000007866991,6.723221e-7,0.01477523,0.00002204427,0.9812513,0.0001768842],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6092434,0.00353734,0.00007583109,0.00001824163,0.004753688,0.0006145548,0.002455937,0.00001068972,0.3792903],"genre_scores_gemma":[0.936666,0.00008658085,0.00002703653,0.00001937019,0.0007603075,0.00002378223,0.001526005,0.00003573335,0.06085521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3274226,"threshold_uncertainty_score":0.9959987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01376838702614191,"score_gpt":0.2575683101334492,"score_spread":0.2437999231073072,"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."}}