{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004975947,0.00008605343,0.0001413152,0.0001874261,0.000855453,0.000006822964,0.00005096049,0.000151108,0.0003954564],"category_scores_gemma":[0.0001517583,0.00007424733,0.00002593139,0.0003558456,0.00001618811,0.0001160629,0.00006811872,0.0003030976,0.0001472937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002693477,"about_ca_system_score_gemma":0.000851383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005955597,"about_ca_topic_score_gemma":0.001452929,"domain_scores_codex":[0.9985802,0.0002147664,0.0005080181,0.0001995734,0.000139679,0.0003577376],"domain_scores_gemma":[0.9993238,0.0001558117,0.0001137308,0.0001840932,0.0001420212,0.00008058271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003403368,0.00004636395,0.9554408,0.0004909353,0.00003755277,0.000008917776,0.002688907,0.00005436148,0.005576087,0.006229044,0.0167438,0.01234288],"study_design_scores_gemma":[0.002954043,0.00007424021,0.7881744,0.0004931603,0.00004291626,0.000005551822,0.00727492,0.01333289,0.007622375,0.004250899,0.1753068,0.0004678355],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8561168,0.00004382318,0.03925899,0.001123599,0.002799702,0.0009596759,0.000002634829,0.0002132237,0.0994816],"genre_scores_gemma":[0.9832507,0.00001150607,0.004671,0.004051081,0.0001595708,0.00007086308,0.00001455652,0.000008047835,0.007762723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1672664,"threshold_uncertainty_score":0.9003121,"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."}}