{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007845612,0.001755233,0.0008410733,0.001722181,0.0004106763,0.00164602,0.00187642,0.001016759,0.01218465],"category_scores_gemma":[0.03034258,0.0005787958,0.0007652154,0.0008241515,0.0004615275,0.002238706,0.002661451,0.0008106445,0.005344199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003927406,"about_ca_system_score_gemma":0.0007385883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006751392,"about_ca_topic_score_gemma":0.001161927,"domain_scores_codex":[0.9910075,0.006034911,0.0004578335,0.0008913139,0.001289106,0.0003193292],"domain_scores_gemma":[0.9725276,0.01993045,0.001236577,0.002793476,0.002660463,0.0008514792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003838583,0.001875801,0.0164299,0.002737583,0.000292356,0.0008829944,0.01118124,0.01548733,0.1033138,0.008412011,0.03735847,0.7981898],"study_design_scores_gemma":[0.001133907,0.005660086,0.05693559,0.0005829431,0.000269232,0.001749958,0.006968292,0.6120859,0.129085,0.027053,0.1575859,0.0008901911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04757226,0.0001134179,0.9177318,0.000164496,0.0000606447,0.002265502,0.003327616,0.02538709,0.003377318],"genre_scores_gemma":[0.2329173,0.0001020644,0.7515185,0.0001389933,0.00004731006,0.003796168,0.005145761,0.001620888,0.00471298],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01218465,"threshold_uncertainty_score":0.04149204,"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."}}