{"id":"W2008129488","doi":"10.1371/journal.pone.0083546","title":"The Edge Factor in Early Word Segmentation: Utterance-Level Prosody Enables Word Form Extraction by 6-Month-Olds","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Language Development and Disorders","field":"Psychology","cited_by":124,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Utterance; Segmentation; Prosody; Lexicon; Text segmentation; Speech segmentation; Computer science; Word (group theory); Natural language processing; Speech recognition; Artificial intelligence; Linguistics; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001945461,0.0001582739,0.0001685533,0.00006723502,0.000153107,0.0000915796,0.0001822731,0.0001067702,0.0005990877],"category_scores_gemma":[0.00004950472,0.0001217257,0.00003394179,0.0001832453,0.00004123827,0.0002507566,0.00002401626,0.0001829697,0.0002968491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006001475,"about_ca_system_score_gemma":0.00001802093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001962691,"about_ca_topic_score_gemma":0.0006636868,"domain_scores_codex":[0.9987468,0.00008219666,0.0002793425,0.0002695395,0.0002602719,0.0003618657],"domain_scores_gemma":[0.9993757,0.0001619013,0.000110443,0.0002545786,0.00004238136,0.0000549824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009634204,0.004382314,0.4950917,0.0001398163,0.0006025084,0.0000173167,0.04892454,9.773693e-7,0.04172653,0.00131527,0.0242094,0.3826262],"study_design_scores_gemma":[0.004596483,0.0002347636,0.9562989,0.0001521976,0.00006185308,0.000002031152,0.003615656,0.00005706535,0.01775781,0.001180113,0.01534278,0.0007002865],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9874052,0.0004226378,0.0002417908,0.0007487118,0.000276788,0.000545473,0.00001346784,0.00006981957,0.01027611],"genre_scores_gemma":[0.9818942,0.00004633339,0.0005647274,0.0003207157,0.000141921,0.0003297402,0.00005631361,0.00002661906,0.01661943],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4612073,"threshold_uncertainty_score":0.655959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04134158131920072,"score_gpt":0.2740159240097129,"score_spread":0.2326743426905122,"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."}}