{"id":"W4386576630","doi":"10.18653/v1/2023.findings-eacl.25","title":"PREME: Preference-based Meeting Exploration through an Interactive Questionnaire","year":2023,"lang":"en","type":"article","venue":"","topic":"Speech and dialogue systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Preference; Correctness; Task (project management); Computer science; Work (physics); Volume (thermodynamics); Human–computer interaction; Data science; Multimedia; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002612963,0.00009697676,0.00009354355,0.00008272319,0.0001143141,0.000201324,0.0003735874,0.00004679959,0.0000128321],"category_scores_gemma":[0.0001222347,0.00008240964,0.00003105606,0.0005349438,0.00001549899,0.002940371,0.00007211349,0.0000656585,0.0003902419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004103901,"about_ca_system_score_gemma":0.00008765145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002846476,"about_ca_topic_score_gemma":0.0001812389,"domain_scores_codex":[0.998932,0.0001401696,0.0001762035,0.000327381,0.0002276127,0.0001966135],"domain_scores_gemma":[0.9993063,0.00006563432,0.00007112615,0.0003761839,0.0001192682,0.00006153849],"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":[0.0003078613,0.001030885,0.01449695,0.0003778544,0.0001483811,0.0001594311,0.07990717,0.03019694,0.03392055,0.4866511,0.1300825,0.2227204],"study_design_scores_gemma":[0.001226094,0.0007182534,0.008930359,0.000475188,0.000009154891,0.000007163584,0.001654567,0.824936,0.1048622,0.04911081,0.007311641,0.0007585685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01762639,0.00003797033,0.9630854,0.001526274,0.0009833366,0.0002939487,0.000001468398,0.001734981,0.01471029],"genre_scores_gemma":[0.9859642,0.000005418409,0.01316261,0.0003527339,0.0001280444,0.00006669848,0.00002495634,0.000006929673,0.0002884346],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9683378,"threshold_uncertainty_score":0.50159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1111433524017075,"score_gpt":0.3009810372162546,"score_spread":0.1898376848145471,"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."}}