{"id":"W7132980772","doi":"","title":"OPTIMISE Mortality Atlas","year":2018,"lang":"en","type":"other","venue":"TSpace","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; Cancer Care Ontario","keywords":"Population; Atlas (anatomy); Public health; Epidemiology; Analytics","routes":{"ca_aff":false,"ca_fund":true,"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":[0.0009254576,0.001004297,0.0007319242,0.003606649,0.0004179105,0.001803379,0.00119478,0.0005391941,0.09761678],"category_scores_gemma":[0.004551393,0.0005054898,0.0007983487,0.005284993,0.0001463044,0.001029299,0.001596319,0.001191426,0.05794312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001214625,"about_ca_system_score_gemma":0.002695845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06006307,"about_ca_topic_score_gemma":0.06284352,"domain_scores_codex":[0.9995418,0.00006579441,0.00005105027,0.0000995772,0.00017327,0.0000686834],"domain_scores_gemma":[0.9982283,0.000191376,0.000194165,0.0003517461,0.0008053276,0.0002290288],"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":[0.00006158361,0.000006825437,0.001720847,0.0001548076,0.00003310106,0.00001899478,0.0000320723,0.0005934642,0.00005386104,0.002048912,0.9832498,0.01202577],"study_design_scores_gemma":[0.0000541626,0.00001193554,0.007137773,0.0001313145,0.00002825068,0.00006547422,0.00004554689,0.0007471876,0.0001666513,0.001777636,0.9898201,0.00001404061],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.0007563981,0.0002915533,0.002293593,0.0004303232,0.0002230355,0.00003730914,0.9736225,0.004006151,0.01833907],"genre_scores_gemma":[0.005120137,0.0005478321,0.005097572,0.0001423805,0.0001915089,0.0001002115,0.9727756,0.001469639,0.01455518],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.09761678,"threshold_uncertainty_score":0.3265607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04891607314137633,"score_gpt":0.3817097443221595,"score_spread":0.3327936711807832,"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."}}