{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002536304,0.0006949677,0.0003503332,0.002188246,0.002130104,0.008163307,0.001486558,0.001335105,0.006801956],"category_scores_gemma":[0.008154728,0.0005794267,0.00041318,0.001710412,0.001862825,0.005647245,0.004460265,0.001075151,0.002739906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007886656,"about_ca_system_score_gemma":0.00101169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001043107,"about_ca_topic_score_gemma":0.001193992,"domain_scores_codex":[0.9963523,0.001580301,0.0003432742,0.0004094533,0.001075446,0.0002391689],"domain_scores_gemma":[0.9901943,0.002651611,0.001553889,0.003957222,0.000759021,0.0008840139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009987789,0.0002758446,0.02517141,0.00110711,0.0001566179,0.003539351,0.06116788,0.00421468,0.02248108,0.3185259,0.02959211,0.5327693],"study_design_scores_gemma":[0.00008878432,0.0004939877,0.01812344,0.0006361789,0.0001832823,0.003124033,0.01220288,0.009176926,0.01403194,0.05541582,0.8863773,0.0001454915],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3351875,0.005382049,0.35457,0.003782208,0.001201022,0.001361101,0.001743632,0.009509679,0.2872626],"genre_scores_gemma":[0.8063539,0.001521427,0.1198239,0.0004683253,0.0004245928,0.0003844051,0.001450965,0.0005068486,0.06906571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008163307,"threshold_uncertainty_score":0.02275485,"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."}}