{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001670458,0.0004000133,0.0002317881,0.0005412999,0.0006746883,0.001334125,0.0005975398,0.0004684542,0.01226372],"category_scores_gemma":[0.006578805,0.0001415479,0.0002705124,0.0004859573,0.0003812674,0.0008294596,0.001038052,0.0003632423,0.00611756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000363465,"about_ca_system_score_gemma":0.0004194176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005519552,"about_ca_topic_score_gemma":0.002279676,"domain_scores_codex":[0.998858,0.0005984785,0.0001038335,0.0001418674,0.000207287,0.00009052907],"domain_scores_gemma":[0.9965559,0.001572788,0.0004253518,0.000489176,0.0006450516,0.0003117557],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005093123,0.001262773,0.04859193,0.002050301,0.00005313104,0.000941209,0.0399242,0.0005639512,0.1193042,0.002457865,0.02252166,0.7618194],"study_design_scores_gemma":[0.0003553458,0.00575223,0.2751212,0.0008703924,0.0002720545,0.004180472,0.04315348,0.0037361,0.1454837,0.003288922,0.5175208,0.0002653125],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8303696,0.0006901092,0.05831804,0.001254693,0.0002722381,0.002059326,0.00092428,0.002257631,0.1038541],"genre_scores_gemma":[0.8409824,0.0004602961,0.1094555,0.0004835918,0.0001204176,0.0009833396,0.0008063219,0.0002446767,0.04646339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01226372,"threshold_uncertainty_score":0.04102623,"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."}}