{"id":"W3139062549","doi":"10.1109/iv51561.2020.00077","title":"ConVisQA: A Natural Language Interface for Visually Exploring Online Conversations","year":2020,"lang":"en","type":"article","venue":"2020 24th International Conference Information Visualisation (IV)","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Conversation; Human–computer interaction; Natural language user interface; Asynchronous communication; User interface; Natural language; Interface (matter); World Wide Web; User needs; Natural (archaeology); Multimedia; Artificial intelligence; Linguistics; Programming language","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":[0.0003106132,0.0002432628,0.0002620752,0.0002509055,0.0001887463,0.0008543553,0.0009390269,0.00008130156,0.0003392936],"category_scores_gemma":[0.0007654615,0.0002487875,0.0001577934,0.0005242549,0.0000443645,0.006784702,0.0002185571,0.0002055057,0.0001930111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001159295,"about_ca_system_score_gemma":0.0001989862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003261938,"about_ca_topic_score_gemma":0.00002076623,"domain_scores_codex":[0.9977788,0.00007758989,0.0008948036,0.0003465182,0.0006589642,0.0002432889],"domain_scores_gemma":[0.9977584,0.0001424056,0.0005166008,0.0002484639,0.001164599,0.0001694779],"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":[0.0003347075,0.0002602256,0.001530019,0.0002325581,0.0006348708,0.000004801116,0.09827272,0.007898055,0.008775414,0.7247427,0.02694432,0.1303696],"study_design_scores_gemma":[0.0009589038,0.0001107867,0.0006730871,0.0000320398,0.00002134972,0.000003196906,0.004642583,0.9691867,0.001634721,0.0002931176,0.02214683,0.000296718],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01812853,0.00002633144,0.9641364,0.01405161,0.001057114,0.0004859473,0.0001670537,0.0003302469,0.0016168],"genre_scores_gemma":[0.9833787,0.00004364086,0.00962512,0.004411846,0.0002991063,0.0001188081,0.001958028,0.00001207996,0.0001526536],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9652502,"threshold_uncertainty_score":0.9999964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08330247308624492,"score_gpt":0.3314139008931629,"score_spread":0.2481114278069179,"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."}}