{"id":"W4251036510","doi":"10.1145/1734454.1734504","title":"Exploring interruption in HRI using wizard of oz","year":2010,"lang":"en","type":"article","venue":"Proceeding of the 5th ACM/IEEE international conference on Human-robot interaction - HRI '10","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Wizard of oz; Interrupt; Wizard; Computer science; Human–computer interaction; Robot; Human–robot interaction; Work (physics); Artificial intelligence; World Wide Web; Engineering; Embedded system","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001389827,0.0002501323,0.0003441693,0.001164772,0.0001537619,0.0003243556,0.002128957,0.00008833046,0.004361374],"category_scores_gemma":[0.001497962,0.0001960894,0.0002121829,0.0006297873,0.0001401337,0.002737779,0.0003998209,0.000584104,0.0001506588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001393485,"about_ca_system_score_gemma":0.00007397346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000115621,"about_ca_topic_score_gemma":0.0001193334,"domain_scores_codex":[0.9963671,0.00005723251,0.001366388,0.0004108969,0.00156151,0.0002368763],"domain_scores_gemma":[0.9962205,0.0002375773,0.001334555,0.0004698703,0.001669175,0.00006832851],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001178526,0.001514766,0.04376441,0.0001275417,0.000229649,0.000005377797,0.0143574,0.005269717,0.8129923,0.09480447,0.006685906,0.01906999],"study_design_scores_gemma":[0.005676813,0.001038433,0.1518394,0.004402939,0.0002365146,0.00009667806,0.05217275,0.2670551,0.4650378,0.03838375,0.01161829,0.002441613],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980913,0.000002156577,0.0003838855,0.0009225835,0.003487342,0.0002660821,0.00001562241,0.00003214349,0.01397718],"genre_scores_gemma":[0.9965058,0.000006527302,0.0005516597,0.0000844352,0.000218786,0.00003947273,0.00001053753,0.00001602381,0.002566762],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3479545,"threshold_uncertainty_score":0.9965488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.922273140928942,"score_gpt":0.6011702702636067,"score_spread":0.3211028706653353,"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."}}