{"id":"W1967451718","doi":"10.1109/hicss.2010.70","title":"Artifacts as Instant Messaging Buddies","year":2010,"lang":"en","type":"article","venue":"","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artifact (error); Instant messaging; Computer science; Interpersonal communication; Conversation; Leverage (statistics); Human–computer interaction; Communication in small groups; Surprise; Multimedia; World Wide Web; Psychology; Artificial intelligence; Communication","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0010783,0.00007705089,0.0001041894,0.0002145445,0.0001450978,0.0004647035,0.0004206246,0.00003136211,0.009999782],"category_scores_gemma":[0.0004386865,0.00004938715,0.00005568581,0.0003273037,0.00005001824,0.0009915401,0.0001286064,0.0001209571,0.007179356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004958034,"about_ca_system_score_gemma":0.00002246066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002373656,"about_ca_topic_score_gemma":0.0001166289,"domain_scores_codex":[0.9984767,0.00001491765,0.0003210731,0.0001585876,0.0008642576,0.000164435],"domain_scores_gemma":[0.9992467,0.0001412635,0.00008514245,0.0003175766,0.0001228905,0.00008645919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002822363,0.00008282036,0.05585402,0.0000034583,0.00001824026,0.00002084755,0.001590619,0.0000137439,0.01975605,0.7198021,0.07383215,0.1289977],"study_design_scores_gemma":[0.0001540013,0.00001362596,0.06131577,0.000001934901,0.000005359309,0.00000409232,0.002424076,0.0004340956,0.00491926,0.01288055,0.9177107,0.0001365946],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.553584,0.000002330909,0.0003060956,0.001685785,0.000153786,0.00004307088,8.018762e-7,0.0000524393,0.4441717],"genre_scores_gemma":[0.9522181,0.00000168322,0.0009544967,0.001545339,0.00001799301,0.000004423403,0.000001401013,0.000003052652,0.04525351],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8438785,"threshold_uncertainty_score":0.9935937,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2826265967768418,"score_gpt":0.4738223583465218,"score_spread":0.19119576156968,"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."}}