{"id":"W6968514805","doi":"10.5281/zenodo.3249347","title":"Interacting with Musebots (that don t really listen)","year":2019,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"AI in Service Interactions","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Set (abstract data type); Perspective (graphical); Identification (biology); Focus (optics); Work (physics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001651769,0.0009121764,0.000764919,0.0003338279,0.001847925,0.002292196,0.00123297,0.001951241,0.04551007],"category_scores_gemma":[0.00710639,0.0004175476,0.00075577,0.0002423449,0.00149779,0.003844085,0.003601213,0.001677391,0.0124439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002877235,"about_ca_system_score_gemma":0.0003210755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009438192,"about_ca_topic_score_gemma":0.000948119,"domain_scores_codex":[0.9987347,0.000520501,0.0000409702,0.0002520861,0.0002852253,0.0001665196],"domain_scores_gemma":[0.9977909,0.0009932513,0.0001022913,0.0003943382,0.0002484751,0.0004706692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.005175327,0.0009357735,0.01338105,0.001831439,0.0003896345,0.002986886,0.0312829,0.005469342,0.2801819,0.09917159,0.1742684,0.3849257],"study_design_scores_gemma":[0.0005617351,0.002583482,0.02617464,0.0008111828,0.0006482779,0.005120059,0.02202544,0.08467693,0.07931195,0.1068555,0.6704572,0.00077365],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2930938,0.002349815,0.4463361,0.007577912,0.005389064,0.0005805794,0.001151785,0.02824928,0.2152717],"genre_scores_gemma":[0.7944263,0.0004927084,0.08243369,0.004363075,0.0007682682,0.0005999518,0.001576926,0.003509252,0.1118299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04551007,"threshold_uncertainty_score":0.1522464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02951816595620755,"score_gpt":0.2440815847150704,"score_spread":0.2145634187588629,"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."}}