{"id":"W4225109875","doi":"10.1145/3491101.3504030","title":"Computational Approaches for Understanding, Generating, and Adapting User Interfaces","year":2022,"lang":"en","type":"article","venue":"CHI Conference on Human Factors in Computing Systems Extended Abstracts","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Human–computer interaction; User interface; User interface design; Domain (mathematical analysis); Modalities; Task (project management); Interface (matter); Post-WIMP; Natural user interface; Automation; User experience design; Semantics (computer science); Data science","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":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0002776305,0.0002916695,0.0003377827,0.0003051096,0.001400877,0.0004089969,0.0002931494,0.00006616546,0.00003695531],"category_scores_gemma":[0.0002067212,0.0002944081,0.00006459897,0.0001390849,0.0001060777,0.0001884042,0.0001378361,0.0005958699,0.000002580138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003345035,"about_ca_system_score_gemma":0.00006966655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008396753,"about_ca_topic_score_gemma":0.00003377783,"domain_scores_codex":[0.9975693,0.0002751972,0.0006387087,0.000726225,0.0003788888,0.0004117294],"domain_scores_gemma":[0.9981856,0.001017819,0.0004667288,0.0002010841,0.00003341415,0.0000953645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005236484,0.0003513376,0.001203303,0.0001147098,0.00002706491,0.00002018994,0.00673633,0.6100847,0.01522618,0.3653141,0.0002727733,0.0005969471],"study_design_scores_gemma":[0.001744007,0.0008055167,0.008309198,0.000401303,0.00002274047,0.0001452188,0.02822288,0.9386408,0.008929758,0.01102184,0.0006691644,0.001087544],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904758,0.00001029317,0.004549258,0.00006803792,0.0009010654,0.0006184421,0.00005993474,0.0001311481,0.00318601],"genre_scores_gemma":[0.9992094,8.166808e-7,0.0002107297,0.00008663406,0.0001051674,0.00004108371,0.00002982426,0.00003382864,0.0002824891],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3542923,"threshold_uncertainty_score":0.9999508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3300195516627227,"score_gpt":0.3501695269123303,"score_spread":0.02014997524960754,"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."}}