{"id":"W4408353776","doi":"10.1109/icassp49660.2025.10890051","title":"Infant Cry Detection Using Causal Temporal Representation","year":2025,"lang":"en","type":"article","venue":"","topic":"Infant Health and Development","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Representation (politics); Artificial intelligence","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":[0.0005171677,0.000652355,0.0004038979,0.001708073,0.0002681127,0.0005882689,0.0007112019,0.0005948514,0.001947023],"category_scores_gemma":[0.002885375,0.0001743536,0.0005357256,0.001251919,0.0002354849,0.0006943197,0.0007256898,0.0007313257,0.0007500491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004130303,"about_ca_system_score_gemma":0.000870064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005616949,"about_ca_topic_score_gemma":0.00790347,"domain_scores_codex":[0.9995357,0.0001086292,0.00002347166,0.0001579512,0.0001016252,0.00007269515],"domain_scores_gemma":[0.998958,0.0004123891,0.000195012,0.0001388949,0.0002447936,0.0000508583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001069232,0.0003816609,0.02814511,0.0003855489,0.0001784958,0.0005292107,0.0003565208,0.09762044,0.06179307,0.01006905,0.01391717,0.7855545],"study_design_scores_gemma":[0.00002820634,0.0001788211,0.0171214,0.00005922943,0.00007668557,0.00044603,0.0002092936,0.9427758,0.01907196,0.01291617,0.007066415,0.000049999],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09432857,0.0009106375,0.8938048,0.0003800762,0.000105511,0.0001011113,0.002506801,0.003835213,0.004027232],"genre_scores_gemma":[0.7762738,0.0008218675,0.2113173,0.0002001743,0.0002020664,0.0001584656,0.007690825,0.0002650685,0.003070396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005616949,"threshold_uncertainty_score":0.01116854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08687777103630563,"score_gpt":0.4858018024211699,"score_spread":0.3989240313848643,"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."}}