{"id":"W2168899303","doi":"10.1109/hicss.2006.107","title":"CrystalChat: Visualizing Personal Chat History","year":2006,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"University of Calgary","keywords":"Conversation; Instant messaging; Computer science; Social media; World Wide Web; Mores; Tone (literature); Visualization; Multimedia; Human–computer interaction; Psychology; Communication; Artificial intelligence; Linguistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001136567,0.00007821231,0.00007574315,0.00008911743,0.00005499711,0.00007833694,0.000338061,0.0000270522,0.0003890855],"category_scores_gemma":[0.00000775014,0.00007225587,0.00004354654,0.0001457824,0.00003812071,0.00045315,0.0001140351,0.00003723031,0.0001555108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009703456,"about_ca_system_score_gemma":0.00005433682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008699363,"about_ca_topic_score_gemma":0.00001799526,"domain_scores_codex":[0.9992468,0.00001969109,0.0001325654,0.0002146592,0.0002228032,0.0001634524],"domain_scores_gemma":[0.9996517,0.00001620217,0.00004186214,0.0001977095,0.00004314467,0.0000493655],"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":[4.130315e-7,0.00005509188,0.0001909514,0.000006254041,0.000003403286,0.000007770638,0.0002774061,0.000008910637,0.0007770685,0.8358946,0.1613824,0.001395763],"study_design_scores_gemma":[0.0001534652,0.00001575441,0.0003062134,0.000006117707,0.000002491977,0.000007862609,0.00005691799,0.2853082,0.0003585674,0.0008584698,0.7127634,0.0001625642],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008432401,0.0002319276,0.9366394,0.0004592181,0.0002739943,0.00003235607,0.000001469731,0.0002796412,0.06123873],"genre_scores_gemma":[0.872494,0.00002300504,0.02630972,0.005674391,0.0003886687,0.000005231653,0.00006280653,0.00002203977,0.0950201],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9103297,"threshold_uncertainty_score":0.4260213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02384777817345053,"score_gpt":0.2624374877862888,"score_spread":0.2385897096128383,"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."}}