{"id":"W1574238033","doi":"10.22329/celt.v2i0.3210","title":"16. Using Content-Specific Lyrics to Familiar Tunes in a Large Lecture Setting","year":2009,"lang":"en","type":"article","venue":"Collected Essays on Learning and Teaching","topic":"Music and Audio Processing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Lyrics; Psychology; Theme (computing); Set (abstract data type); Class (philosophy); Content (measure theory); Content analysis; Selection (genetic algorithm); Perception; Mathematics education; Pedagogy; Literature; Art; Computer science; Sociology; Social science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0008713243,0.0002440762,0.0003012542,0.0004192014,0.001102741,0.0004881948,0.0003020705,0.0001141037,0.000004777299],"category_scores_gemma":[0.0007090649,0.0002320307,0.00004650195,0.0007080077,0.00001609372,0.0002852438,0.0001176946,0.001059726,0.000004926069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001403718,"about_ca_system_score_gemma":0.00008368375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003241925,"about_ca_topic_score_gemma":0.00001232629,"domain_scores_codex":[0.997955,0.0003150075,0.000300754,0.0005979771,0.0002659325,0.0005652989],"domain_scores_gemma":[0.9992366,0.0002045206,0.0001284516,0.0002008463,0.00005942412,0.0001701932],"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":[0.0001015144,0.0003611453,0.02122118,0.00007935748,0.00003019243,0.0001688062,0.02628149,0.01602487,0.03916987,0.02348431,0.002068915,0.8710083],"study_design_scores_gemma":[0.006144202,0.00141589,0.0390075,0.003827068,0.00003735121,0.0002421213,0.004363524,0.8512213,0.00325829,0.005266761,0.08221732,0.002998627],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4853451,0.0003651892,0.5056341,0.001295086,0.0001396709,0.0001611108,6.717376e-7,0.0002684105,0.006790688],"genre_scores_gemma":[0.9779379,0.00001699466,0.0194711,0.002072639,0.00009916989,0.000003705667,0.000002634653,0.00001680367,0.0003790693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8680097,"threshold_uncertainty_score":0.9461936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02163022305647977,"score_gpt":0.2620237936043074,"score_spread":0.2403935705478277,"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."}}