{"id":"W3135356928","doi":"10.3758/s13421-021-01163-4","title":"When statistics collide: The use of transitional and phonotactic probability cues to word boundaries","year":2021,"lang":"en","type":"article","venue":"Memory & Cognition","topic":"Language Development and Disorders","field":"Psychology","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Concordia University; National Institute of Child Health and Human Development; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Phonotactics; Speech segmentation; Segmentation; Psychology; Statistics; Word (group theory); Speech recognition; Text segmentation; Linguistics; Natural language processing; Computer science; Artificial intelligence; Mathematics; Phonology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001314404,0.00007864962,0.0001072186,0.00002944597,0.0001482472,0.00009411501,0.00003368657,0.00003926895,0.002148743],"category_scores_gemma":[0.000229462,0.00006503602,0.00002106526,0.0001092566,0.0001876557,0.00009740477,0.00001724422,0.0000675525,0.00003136384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001416206,"about_ca_system_score_gemma":0.0001169085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007924845,"about_ca_topic_score_gemma":0.0006982186,"domain_scores_codex":[0.9992595,0.0001381759,0.000171658,0.0001792393,0.0001344802,0.0001169199],"domain_scores_gemma":[0.9993343,0.0002610075,0.00004333898,0.0001240653,0.000200773,0.00003652214],"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":[0.003376344,0.002289182,0.01993858,0.0007083633,0.001039686,0.0003368725,0.2991612,0.00004509415,0.01009493,0.02326496,0.2112101,0.4285347],"study_design_scores_gemma":[0.004632507,0.0002828447,0.7173917,0.0001869137,0.0006258762,0.0001399478,0.02363295,0.00005649043,0.01007839,0.1376337,0.1044084,0.0009302471],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9880576,0.000166198,0.007807212,0.001696193,0.0002977471,0.0003431553,0.0003094888,0.00002424376,0.001298173],"genre_scores_gemma":[0.9868483,0.000008581607,0.01002306,0.001467759,0.00003799353,0.00006862936,0.0003204849,0.000009961238,0.00121525],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6974531,"threshold_uncertainty_score":0.9987634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05927292374173348,"score_gpt":0.2944711447584696,"score_spread":0.2351982210167361,"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."}}