{"id":"W4409603265","doi":"10.61091/jcmcc127b-122","title":"A Multi-Level Accompaniment Effect Generation Mechanism Incorporating AI Computing in Piano Art Instruction","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Advanced Technology in Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Piano; Mechanism (biology); Computer science; Computer architecture; Multimedia; Human–computer interaction; Art; Physics; Art history","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0003016646,0.0004151844,0.0002879369,0.000263509,0.0002545942,0.0003358499,0.0007595248,0.000385756,0.003467234],"category_scores_gemma":[0.0007086542,0.0001692509,0.0003995091,0.000168326,0.0003293121,0.0008138383,0.0008369907,0.0005063974,0.0004281278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002332437,"about_ca_system_score_gemma":0.0003106004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000892128,"about_ca_topic_score_gemma":0.0008865474,"domain_scores_codex":[0.9998696,0.00002114398,0.000006322473,0.00004522164,0.00003951945,0.00001816939],"domain_scores_gemma":[0.9998879,0.0000377446,0.00001130716,0.00001917144,0.00002534669,0.0000184723],"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":[0.0003762842,0.000287856,0.003422856,0.0002541668,0.0001023729,0.0003936139,0.0003535402,0.2600402,0.1294093,0.02157259,0.003415304,0.580372],"study_design_scores_gemma":[0.00002965349,0.0002092251,0.001239589,0.00001736534,0.00004041553,0.0001218161,0.00002947471,0.9634479,0.02530899,0.004900978,0.004631971,0.00002268838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08223415,0.0003556812,0.9075076,0.000191189,0.0001207389,0.0001247621,0.00005605331,0.002088995,0.007320846],"genre_scores_gemma":[0.8464763,0.0002256708,0.146339,0.0001156872,0.00004276645,0.0001111905,0.00009570085,0.00008527197,0.0065084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003467234,"threshold_uncertainty_score":0.01159906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809789887973678,"score_gpt":0.2862343711705426,"score_spread":0.2681364722908058,"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."}}