{"id":"W2094860966","doi":"10.1145/1240624.1240631","title":"Matching attentional draw with utility in interruption","year":2007,"lang":"en","type":"article","venue":"","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Annoyance; Matching (statistics); Set (abstract data type); Perception; Workload; Computer science; SIGNAL (programming language); Guideline; Cognitive psychology; Psychology; Computer vision; Mathematics; Medicine; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003774045,0.0005069895,0.0004072108,0.0007705707,0.0003638366,0.001409655,0.0006787999,0.000759856,0.001853879],"category_scores_gemma":[0.06181099,0.0002945808,0.0001995308,0.0004566642,0.0003923553,0.001321765,0.0009813827,0.000501068,0.0002235144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005373665,"about_ca_system_score_gemma":0.0003537794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006161386,"about_ca_topic_score_gemma":0.0006650935,"domain_scores_codex":[0.9958223,0.00182029,0.0003041287,0.0004651798,0.00139774,0.0001904233],"domain_scores_gemma":[0.965991,0.02658496,0.003336057,0.001378658,0.001895291,0.0008139662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.008287926,0.005657351,0.1274812,0.001425136,0.0002790517,0.0002711885,0.005149478,0.009775552,0.2845187,0.005758535,0.001015201,0.5503806],"study_design_scores_gemma":[0.001358507,0.03156101,0.7746986,0.000452869,0.0009677383,0.0008471037,0.003382756,0.04869402,0.1135648,0.01642852,0.007755818,0.0002884355],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9538298,0.0002246607,0.04064049,0.0001992098,0.00002528371,0.0002806489,0.00002168632,0.000207729,0.004570497],"genre_scores_gemma":[0.9887326,0.00004996432,0.01073358,0.00007027871,0.00001129088,0.0001240776,0.00001612655,0.00001394713,0.0002481164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003774045,"threshold_uncertainty_score":0.01995933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3439639588478714,"score_gpt":0.478092445742744,"score_spread":0.1341284868948726,"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."}}