{"id":"W4226351384","doi":"10.5281/zenodo.5624567","title":"Building the MetaMIDI Dataset: Linking Symbolic and Audio Musical Data","year":2021,"lang":"en","type":"paratext","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Musical; Computer science; Art; Visual arts","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":[0.0009900408,0.002227929,0.001002657,0.007829553,0.001402245,0.002490646,0.00302596,0.001850145,0.01341787],"category_scores_gemma":[0.004940085,0.0005755153,0.001207572,0.006910943,0.0006958903,0.002930455,0.003785959,0.002068205,0.02198165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001182452,"about_ca_system_score_gemma":0.001798497,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01605259,"about_ca_topic_score_gemma":0.03041882,"domain_scores_codex":[0.998109,0.0001901741,0.0001869121,0.0005678468,0.000736423,0.0002096427],"domain_scores_gemma":[0.998536,0.0002391875,0.0001011718,0.0005719796,0.0003402361,0.0002114082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004211343,0.000533923,0.008133659,0.001300912,0.0001053008,0.000531206,0.0002819685,0.005060597,0.007797198,0.00393839,0.8861879,0.08570787],"study_design_scores_gemma":[0.0003319654,0.0002039158,0.02236019,0.0003060037,0.00008085281,0.0007253757,0.0009400398,0.02742084,0.01185592,0.007246356,0.9283465,0.0001821358],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02175106,0.0009635799,0.01467258,0.0005589762,0.000398989,0.000453,0.9309686,0.01940311,0.01083015],"genre_scores_gemma":[0.006011655,0.0001362911,0.01278743,0.00009077055,0.00004215619,0.0002580683,0.9788391,0.0003124786,0.001522051],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01605259,"threshold_uncertainty_score":0.04488724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08047406364333035,"score_gpt":0.2969442459572856,"score_spread":0.2164701823139552,"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."}}