{"id":"W2914892640","doi":"10.1111/desc.12803","title":"Infants’ statistical word segmentation in an artificial language is linked to both parental speech input and reported production abilities","year":2019,"lang":"en","type":"article","venue":"Developmental Science","topic":"Language Development and Disorders","field":"Psychology","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Agence Nationale de la Recherche","keywords":"Babbling; Speech segmentation; Psychology; Novelty; Language development; Word (group theory); Speech production; Affect (linguistics); Language acquisition; Text segmentation; Cognitive psychology; Categorization; Language production; Preference; Developmental psychology; Linguistics; Communication; Speech recognition; Segmentation; Cognition; Artificial intelligence; Computer science; Social psychology; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006666053,0.0001532994,0.000150286,0.0002864808,0.0001242523,0.0001115257,0.0001693662,0.00005468078,0.001329659],"category_scores_gemma":[0.0001107868,0.0001458836,0.0000103672,0.0007548323,0.0002126766,0.0004792797,0.0001053431,0.0001064636,0.0002446684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002000025,"about_ca_system_score_gemma":0.0001881566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004895716,"about_ca_topic_score_gemma":0.0008701467,"domain_scores_codex":[0.9980379,0.0000470254,0.0003749256,0.0007078478,0.0004257071,0.0004065548],"domain_scores_gemma":[0.9995281,0.00003006422,0.00007122367,0.000182189,0.00003906234,0.0001493559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001339159,0.0001640121,0.664337,0.00001353947,0.000009003279,0.00004753995,0.1212289,0.000001702168,0.1084175,0.000244964,0.000220623,0.1051814],"study_design_scores_gemma":[0.0003423218,0.00007083453,0.9363701,0.00001948693,0.000002917966,0.00003944237,0.05172844,0.00001973215,0.01085588,0.0001822685,0.00009938625,0.000269154],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960586,0.00001422756,0.0000401115,0.0001618193,0.0006854791,0.0006263411,0.00001135995,0.00005369086,0.002348402],"genre_scores_gemma":[0.9879354,0.000001754577,0.01074503,0.0004125166,0.00003378779,0.00004516711,0.00006688305,0.00001092387,0.0007484871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2720332,"threshold_uncertainty_score":0.9995832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02339194310229384,"score_gpt":0.331063413541838,"score_spread":0.3076714704395441,"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."}}