{"id":"W4393414783","doi":"10.5281/zenodo.4386495","title":"SARdB: A Dataset for Audio Scene Source Counting and Analysis","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Audio analyzer; Artificial intelligence; Audio signal processing; Speech recognition; Audio signal; Speech coding","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001198973,0.005793558,0.002558547,0.004357251,0.00152291,0.002431638,0.004488285,0.003388274,0.03071029],"category_scores_gemma":[0.003453483,0.0007604035,0.00194209,0.004444688,0.0006734498,0.00167161,0.002790323,0.002381333,0.07725538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001508061,"about_ca_system_score_gemma":0.002177544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02027324,"about_ca_topic_score_gemma":0.03999144,"domain_scores_codex":[0.9979115,0.0003061256,0.0002238916,0.0006045274,0.0006902426,0.0002637363],"domain_scores_gemma":[0.9985475,0.000282379,0.00008826969,0.0004520578,0.0004693186,0.0001603549],"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":[0.0002395127,0.0001229544,0.0007532509,0.0009735157,0.00008072533,0.0001199101,0.00003509829,0.0009056726,0.001515734,0.0004399879,0.9812696,0.01354414],"study_design_scores_gemma":[0.0005085804,0.0001555803,0.008315168,0.0003837939,0.0001438737,0.0006639202,0.0002614445,0.005819379,0.005774049,0.002339088,0.9755,0.0001350854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.001374675,0.0004490974,0.001359789,0.0001052294,0.0001235946,0.00008186959,0.9905732,0.003972008,0.001960546],"genre_scores_gemma":[0.0006860339,0.00007043972,0.001143613,0.00003517885,0.00001053012,0.00007853316,0.9972501,0.0001049968,0.0006205364],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03071029,"threshold_uncertainty_score":0.1027361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03990741091793144,"score_gpt":0.2590414370910681,"score_spread":0.2191340261731367,"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."}}