{"id":"W2403980317","doi":"","title":"An Algorithmic Approach to Composing for Flexible Intonation Ensembles","year":2003,"lang":"en","type":"article","venue":"The Journal of the Abraham Lincoln Association","topic":"Music Technology and Sound Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Intonation (linguistics); Computer science; Context (archaeology); Representation (politics); Rhythm; Natural language processing; Harmonic; Natural (archaeology); Artificial intelligence; Speech recognition; Linguistics; History; Acoustics; Physics","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":[0.003327181,0.0000826837,0.0001579654,0.00008692923,0.0005924625,0.00008344726,0.000860594,0.00008475605,5.63423e-7],"category_scores_gemma":[0.0005695728,0.00004871665,0.00008723569,0.0003516752,0.00002167164,0.0003092492,0.0000654395,0.0001997469,0.000001876597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002876844,"about_ca_system_score_gemma":0.00007793671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006109898,"about_ca_topic_score_gemma":0.000009496037,"domain_scores_codex":[0.9987912,0.0003408365,0.0002935687,0.00009155313,0.0003146205,0.00016829],"domain_scores_gemma":[0.9984324,0.000363271,0.0005786101,0.0002728979,0.0003228941,0.00002994819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002277452,0.001699618,0.01256153,0.0000726428,0.001162778,0.000001365956,0.08325168,0.04493096,0.03407063,0.6510655,0.06122262,0.109733],"study_design_scores_gemma":[0.005181137,0.001917094,0.06945359,0.0002150049,0.0005240105,0.0002618176,0.005531007,0.1448181,0.05842422,0.5817534,0.1307865,0.001134135],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1268388,0.0001427199,0.86475,0.005661651,0.0009652627,0.0003751586,0.000001609689,0.00005418169,0.001210565],"genre_scores_gemma":[0.9339314,0.00001532087,0.06477767,0.000697188,0.0001596705,0.000008767909,3.417843e-7,0.000006247909,0.0004033789],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8070926,"threshold_uncertainty_score":0.4556802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01648564146027957,"score_gpt":0.265192429831125,"score_spread":0.2487067883708454,"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."}}