{"id":"W2262989991","doi":"10.1080/15475441.2015.1073153","title":"How Transitional Probabilities and the Edge Effect Contribute to Listeners’ Phonological Bootstrapping Success","year":2016,"lang":"en","type":"article","venue":"Language Learning and Development","topic":"Language Development and Disorders","field":"Psychology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Speech segmentation; Utterance; Computer science; Phonotactics; Speech recognition; Bootstrapping (finance); Natural language processing; Text segmentation; Salient; Segmentation; Artificial intelligence; Natural language; Natural (archaeology); Phonology; Linguistics; Mathematics","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":[0.0006153181,0.0001965873,0.0002548717,0.00006982031,0.0002859225,0.0001176412,0.00009868636,0.00009495251,0.0002315249],"category_scores_gemma":[0.0002260695,0.00009700532,0.00003523132,0.00008401448,0.000209002,0.00006650568,0.00006820219,0.0001486308,0.0000330921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003291542,"about_ca_system_score_gemma":0.00003458701,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003976902,"about_ca_topic_score_gemma":0.00004112915,"domain_scores_codex":[0.9986812,0.0002594992,0.000174804,0.0003595649,0.0001624605,0.0003625056],"domain_scores_gemma":[0.9991559,0.0005559747,0.00005068664,0.00009675443,0.00003116219,0.0001095308],"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":[0.00250677,0.000109375,0.1087957,0.0001862473,0.0006049672,0.0002580396,0.2711895,0.000002982767,0.003929172,0.01153371,0.001164766,0.5997188],"study_design_scores_gemma":[0.03640956,0.0007667,0.6318282,0.0006674386,0.0001343036,0.000401655,0.09371193,0.00001617304,0.004170984,0.000534269,0.2289854,0.00237338],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850403,0.001846076,0.003315029,0.007199316,0.0001567465,0.0004257815,0.000003842312,0.0001246078,0.001888303],"genre_scores_gemma":[0.9889273,0.00002237211,0.0005139649,0.0004899495,0.0000617236,0.0001861105,0.00001335701,0.00001309197,0.009772121],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5973455,"threshold_uncertainty_score":0.3955762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009228659381102473,"score_gpt":0.2531996825512671,"score_spread":0.2439710231701646,"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."}}