{"id":"W3041036493","doi":"10.2196/21591","title":"Figure Correction: Detecting Screams From Home Audio Recordings to Identify Tantrums: Exploratory Study Using Transfer Machine Learning","year":2020,"lang":"en","type":"erratum","venue":"JMIR Formative Research","topic":"Music and Audio Processing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Psychology; Transfer of learning; Exploratory research; Transfer (computing); Speech recognition; Audiology; Communication; Computer science; Developmental psychology; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.003464787,0.002256283,0.001518296,0.003575315,0.003470181,0.003731997,0.003691646,0.007236606,0.1176203],"category_scores_gemma":[0.08872852,0.001046964,0.001625052,0.001858216,0.002952846,0.002324889,0.002086406,0.009840778,0.05244738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003204085,"about_ca_system_score_gemma":0.004853277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02682395,"about_ca_topic_score_gemma":0.0297149,"domain_scores_codex":[0.995095,0.0007412388,0.000868687,0.0005081451,0.002468506,0.0003184674],"domain_scores_gemma":[0.9462853,0.01379681,0.002522028,0.002778814,0.03305959,0.001557487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00002945129,0.000004761504,0.00005704013,0.0000940309,0.000005638411,0.0003951126,0.0000451315,0.00002459466,0.00005921169,0.000364693,0.9942002,0.004720066],"study_design_scores_gemma":[0.00005675383,0.00003092575,0.0007300368,0.0006392071,0.00003280953,0.00244056,0.0002399958,0.0003523759,0.0006277934,0.001339459,0.993454,0.00005627888],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0002990091,0.0006800437,0.002018903,0.05773862,0.9312634,0.00006237792,0.002194979,0.001310039,0.00443271],"genre_scores_gemma":[0.0329763,0.007748262,0.01654474,0.1345908,0.4081065,0.0005684071,0.006005173,0.008790514,0.3846692],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1176203,"threshold_uncertainty_score":0.3934793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08660039819768711,"score_gpt":0.3823959879464306,"score_spread":0.2957955897487435,"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."}}