{"id":"W4394963892","doi":"10.25071/180k9w08","title":"John Robarts: True to his own vision of Canada","year":2024,"lang":"en","type":"article","venue":"Canada Watch","topic":"Canadian Identity and History","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Optometry; Artificial intelligence; Computer science; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001791707,0.00006028349,0.0001168409,0.00009368028,0.000406054,0.0001178725,0.0002029904,0.00004102695,0.001424987],"category_scores_gemma":[0.0001021039,0.00007446214,0.00003040283,0.0004098063,0.00009001271,0.00008221298,0.00001866931,0.00008637929,0.00000919646],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.005474562,"about_ca_system_score_gemma":0.02872877,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9999973,"about_ca_topic_score_gemma":1,"domain_scores_codex":[0.9988488,0.00003684075,0.0001458489,0.0001703216,0.0005108975,0.0002872859],"domain_scores_gemma":[0.9993308,0.00005362226,0.00001971885,0.0001449896,0.00006467106,0.000386165],"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":[0.000001412119,0.000001867481,0.00002187253,0.0000213957,0.000007133459,0.00007189117,0.001663656,0.0000188727,0.00003647822,0.002444836,0.9899423,0.005768349],"study_design_scores_gemma":[0.00002447528,0.000007865438,0.0003135927,0.00003710602,0.000007118866,6.51933e-7,0.0008135003,0.000009871957,0.00003510644,0.00008375195,0.9985715,0.00009552354],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1735924,0.00476626,0.00004529992,0.4070648,0.01546845,0.0006696888,0.0007287904,0.0001167851,0.3975475],"genre_scores_gemma":[0.8941579,0.00004614993,0.00002465725,0.001223583,0.0002633519,0.000006223502,0.000005203632,0.00001205095,0.1042608],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7205656,"threshold_uncertainty_score":0.9994878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004233108534070981,"score_gpt":0.2091401666256253,"score_spread":0.2049070580915543,"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."}}