{"id":"W2812765312","doi":"10.1111/cgf.13425","title":"ThreadReconstructor: Modeling Reply‐Chains to Untangle Conversational Text through Visual Analytics","year":2018,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Software Engineering Research","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Heuristics; Visual analytics; Visualization; Analytics; Human–computer interaction; Data visualization; Artificial intelligence; Data science; Machine learning; Information retrieval","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003718628,0.0002606541,0.0002538383,0.0004416936,0.0002924881,0.0003104909,0.001333151,0.0001099374,0.00002149728],"category_scores_gemma":[0.000119795,0.0002736744,0.0001372442,0.001441971,0.0001609275,0.0005836189,0.0009284234,0.0002925966,0.000166011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001087323,"about_ca_system_score_gemma":0.0001678297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005537766,"about_ca_topic_score_gemma":0.00003216431,"domain_scores_codex":[0.9974188,0.00004331804,0.0003709115,0.0007522642,0.0006787995,0.0007359197],"domain_scores_gemma":[0.9979165,0.0003615399,0.0000604537,0.0009072394,0.0005006232,0.0002536832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002759014,0.0001736455,0.04050317,0.00004474249,0.0002542403,0.00004667313,0.002972242,0.01762048,0.00008893947,0.9011476,0.0210803,0.01604037],"study_design_scores_gemma":[0.0002888345,0.0002892702,0.001204581,0.00003552285,0.000005256675,0.0000409094,0.00002611656,0.9842699,0.0002881908,0.009954737,0.0032627,0.000333942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06651947,0.00003843201,0.9294219,0.001618019,0.001550586,0.0002189072,0.000007218365,0.0005045607,0.0001209369],"genre_scores_gemma":[0.8370264,0.000007374361,0.1597012,0.002657693,0.0005134247,0.00001183652,0.000009167272,0.00003061209,0.00004226419],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9666495,"threshold_uncertainty_score":0.9999716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03116623171626524,"score_gpt":0.2922717656578509,"score_spread":0.2611055339415856,"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."}}