{"id":"W4381594444","doi":"10.3389/fnins.2023.1141886","title":"Dynamic networks differentiate the language ability of children with cochlear implants","year":2023,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Concordia University","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Deutsche Forschungsgemeinschaft; Oberkotter Foundation","keywords":"Audiology; Coherence (philosophical gambling strategy); Electroencephalography; Language development; Cochlear implant; Reading (process); Population; Cohort; Psychology; Language acquisition; Speech recognition; Developmental psychology; Medicine; Computer science; Mathematics; Neuroscience; Statistics","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":[0.0003877596,0.0001117247,0.0001592111,0.0001292011,0.0001660354,0.00003989271,0.0005050488,0.00003834672,0.000002093431],"category_scores_gemma":[0.0004207082,0.00006816566,0.00003812771,0.001211518,0.0005655032,0.0001318321,0.0001082898,0.0002119994,0.000003009702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002621606,"about_ca_system_score_gemma":0.00003084617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005668141,"about_ca_topic_score_gemma":0.000006037963,"domain_scores_codex":[0.9984487,0.000191069,0.0002033659,0.0004609237,0.0003316356,0.0003643285],"domain_scores_gemma":[0.9993401,0.000126688,0.00008303762,0.0003866721,0.00001216994,0.00005133965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005971085,0.0001062198,0.6019571,0.00002748559,0.000001655697,0.00001945048,0.001210099,0.01278064,0.3763202,0.0001353205,0.0002184309,0.007163721],"study_design_scores_gemma":[0.0001772414,0.00008142737,0.9152828,0.00001803098,0.000002628887,0.000008732002,0.00008879406,0.0819365,0.002198563,0.0001212016,0.000006424189,0.00007762227],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957461,0.00002089157,0.002875639,0.0001730103,0.0007083131,0.0003343139,0.00001620256,0.00007126371,0.00005422776],"genre_scores_gemma":[0.9995545,0.0000565625,0.0001255225,0.0001499891,0.000008559378,0.00001435526,0.000001561897,0.00001154649,0.00007742471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3741216,"threshold_uncertainty_score":0.2779715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01073647249051078,"score_gpt":0.2541731654601272,"score_spread":0.2434366929696164,"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."}}