{"id":"W2602799059","doi":"10.1162/coli_a_00290","title":"Identifying and Avoiding Confusion in Dialogue with People with Alzheimer's Disease","year":2017,"lang":"en","type":"article","venue":"Computational Linguistics","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto","funders":"National Institute on Deafness and Other Communication Disorders; National Institutes of Health; Alzheimer Society; AGE-WELL; Carnegie Mellon University; Natural Sciences and Engineering Research Council of Canada; University of Pittsburgh","keywords":"Computer science; Confusion; Dementia; Cognitive psychology; Vocabulary; Function (biology); Cognition; Process (computing); Parsing; Spoken language; Decision tree; Artificial intelligence; Natural language processing; Psychology; Disease; Linguistics; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002178435,0.0001219325,0.0001518039,0.00008797803,0.0005081863,0.0005253215,0.0003649056,0.00002703976,0.000001035726],"category_scores_gemma":[0.0006443737,0.0001011603,0.00001440145,0.00008372544,0.00008240357,0.0001269923,0.0001777682,0.0001143293,0.000008118946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000018448,"about_ca_system_score_gemma":0.000166923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001842805,"about_ca_topic_score_gemma":0.0002669663,"domain_scores_codex":[0.9990162,0.00004148197,0.0001645777,0.0003129062,0.0002823907,0.0001824356],"domain_scores_gemma":[0.9989007,0.00022481,0.000159655,0.0003177616,0.0002416332,0.0001554702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001959286,0.0001384136,0.5498988,0.0001087502,0.00006842176,0.000587602,0.002732982,0.02188277,0.00001766019,0.4210097,0.0001860728,0.003172927],"study_design_scores_gemma":[0.001389742,0.000100628,0.7458422,0.0002816281,0.00003234345,0.00002744157,0.00005930679,0.2158453,0.00002920638,0.03563805,0.0003971661,0.0003570908],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2097752,0.0005087041,0.7844238,0.00033774,0.001229742,0.0004319428,0.00002443186,0.0001265857,0.003141897],"genre_scores_gemma":[0.9628205,0.000003976223,0.03682455,0.00005812491,0.0002407908,0.000008387057,0.00002483959,0.000009266337,0.000009627535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7530453,"threshold_uncertainty_score":0.5065687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03038340881292735,"score_gpt":0.2762257908780106,"score_spread":0.2458423820650832,"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."}}