{"id":"W2903461828","doi":"","title":"Intelligent and Affectively Aligned Evaluation of Online Health Information for Older Adults.","year":2017,"lang":"en","type":"article","venue":"National Conference on Artificial Intelligence","topic":"Health Literacy and Information Accessibility","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; University of British Columbia","funders":"","keywords":"Computer science; Human–computer interaction; Data science; World Wide Web; Internet privacy","routes":{"ca_aff":true,"ca_fund":false,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.006762704,0.0001539283,0.0002809196,0.0002123242,0.001347008,0.00007501472,0.0002891296,0.0001554677,0.0004108834],"category_scores_gemma":[0.006545785,0.0001371823,0.00005204606,0.00009574193,0.0001484438,0.001521796,0.00007193785,0.0002292564,0.00007916818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000317904,"about_ca_system_score_gemma":0.001828949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002915226,"about_ca_topic_score_gemma":0.0003360611,"domain_scores_codex":[0.996454,0.0003894058,0.001727392,0.0002359323,0.000896764,0.0002965111],"domain_scores_gemma":[0.9913723,0.0005798271,0.001814918,0.0003039792,0.005785284,0.0001437326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003658131,0.0001375886,0.0007094528,0.0003988216,0.000009662492,1.081814e-8,0.007058561,0.00007091367,0.00000980221,0.2130381,0.0002991378,0.7779022],"study_design_scores_gemma":[0.0007862575,0.0005301561,0.1873713,0.001491083,0.0000213305,5.541795e-7,0.01364793,0.6599566,0.001848432,0.1312036,0.002787648,0.0003550963],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6764327,0.00009649555,0.2731629,0.01791622,0.001922793,0.01059729,0.001571778,0.0001123647,0.01818748],"genre_scores_gemma":[0.995358,0.00005267088,0.001908574,0.001982363,0.0001210802,0.0002629273,0.0002748885,0.000006318137,0.00003312832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7775471,"threshold_uncertainty_score":0.9999531,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2888817990805223,"score_gpt":0.5453131383301174,"score_spread":0.2564313392495952,"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."}}