{"id":"W4392902959","doi":"10.1109/icassp48485.2024.10447655","title":"ASPED: An Audio Dataset for Detecting Pedestrians","year":2024,"lang":"en","type":"article","venue":"","topic":"Music and Audio Processing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Task (project management); Pedestrian; Pedestrian detection; Scale (ratio); Artificial intelligence; Speech recognition; Engineering; Geography; Cartography; Transport engineering","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":[0.0002799187,0.00007482919,0.00006935617,0.00005231105,0.0001450444,0.0006750045,0.000425011,0.0000283044,0.00003566194],"category_scores_gemma":[0.00003238459,0.00006021158,0.00002784335,0.0001959238,0.00001180672,0.0008829691,0.00009555616,0.00006550479,0.00004563212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001347179,"about_ca_system_score_gemma":0.00008829241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002130227,"about_ca_topic_score_gemma":0.00002560245,"domain_scores_codex":[0.9992481,0.0000117933,0.0001116835,0.0003381622,0.00009545337,0.000194742],"domain_scores_gemma":[0.9995225,0.00008896663,0.00001796736,0.0002917681,0.00001686578,0.00006193149],"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":[0.000002065041,0.00001884091,0.00001534362,0.0001092766,0.00001291384,0.00002289739,0.0005726045,0.00003654622,0.00256252,0.02632333,0.08466583,0.8856578],"study_design_scores_gemma":[0.0002006684,0.0001350664,0.00003441436,0.00006793095,0.00001429152,0.00005808363,0.00009382825,0.527548,0.01225792,0.0117378,0.4475358,0.0003161859],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001822052,0.0001169596,0.9940794,0.001341241,0.0004162774,0.00008251028,0.00005420609,0.000315158,0.001772187],"genre_scores_gemma":[0.6583656,0.000003900137,0.3368353,0.002572778,0.0006715111,0.00002692458,0.0001491289,0.00001970419,0.001355176],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8853416,"threshold_uncertainty_score":0.6509084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05140978155896479,"score_gpt":0.3252451145803849,"score_spread":0.27383533302142,"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."}}