{"id":"W2095578834","doi":"10.1503/cmaj.070074","title":"Memory for MMSE","year":2007,"lang":"en","type":"article","venue":"Canadian Medical Association Journal","topic":"Earthquake and Disaster Impact Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Spell; Forehead; Anticipation (artificial intelligence); Face (sociological concept); Computer science; Medicine; Artificial intelligence; Philosophy; Anatomy; Linguistics; Theology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.00107298,0.001298436,0.001391135,0.002056263,0.001432175,0.00214247,0.001408257,0.0007988358,0.2769372],"category_scores_gemma":[0.02609783,0.0002038903,0.0007539827,0.001514807,0.0003553183,0.002726221,0.001436224,0.001541509,0.1480754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008501152,"about_ca_system_score_gemma":0.002153918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009401632,"about_ca_topic_score_gemma":0.00785916,"domain_scores_codex":[0.9989078,0.0001736557,0.0002425692,0.0002544968,0.0002976564,0.000123832],"domain_scores_gemma":[0.9935738,0.001111331,0.0005400712,0.001094601,0.003349305,0.0003308137],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00112087,0.0003355498,0.0240968,0.0006900883,0.0001055549,0.0004330294,0.0008261371,0.0002793176,0.0003247279,0.002638116,0.6533324,0.3158175],"study_design_scores_gemma":[0.0004024922,0.001093921,0.1416196,0.002537554,0.0002993637,0.003401385,0.002093607,0.001275145,0.002730283,0.01986021,0.8244007,0.0002859236],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05773958,0.005898569,0.006796963,0.01016594,0.002848207,0.001854186,0.2091159,0.006835191,0.6987455],"genre_scores_gemma":[0.3288155,0.006650078,0.01359768,0.007470038,0.001745278,0.005891398,0.1085292,0.002463263,0.5248376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2769372,"threshold_uncertainty_score":0.9264472,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01735429929552241,"score_gpt":0.3111830593061699,"score_spread":0.2938287600106475,"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."}}