{"id":"W2133414707","doi":"10.1146/annurev-linguistics-011415-040616","title":"Constructing a Proto-Lexicon: An Integrative View of Infant Language Development","year":2015,"lang":"en","type":"article","venue":"Annual Review of Linguistics","topic":"Language Development and Disorders","field":"Psychology","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Lexicon; Linguistics; Computer science; Language acquisition; Language development; Text segmentation; Natural language processing; First language; String (physics); Word (group theory); Artificial intelligence; Psychology; Segmentation","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":[0.0008614794,0.0001918521,0.000577698,0.00007271095,0.00002268992,0.000005870209,0.0002475589,0.00007087312,0.000304372],"category_scores_gemma":[0.004745284,0.0001454123,0.00006633822,0.0002443189,0.000133988,0.00003147888,0.0000736406,0.0001402953,0.00003830019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003594644,"about_ca_system_score_gemma":0.0004652553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001298448,"about_ca_topic_score_gemma":0.00006319152,"domain_scores_codex":[0.9983172,0.0001516401,0.0008143156,0.0002312695,0.0002714279,0.0002141826],"domain_scores_gemma":[0.9980233,0.00009533056,0.0004531609,0.0003159943,0.0009853583,0.0001268769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001384019,0.0007192398,0.01955276,0.01265792,0.0004403289,0.0001497977,0.2589657,3.135769e-7,0.00002347738,0.09710459,0.01065933,0.5995882],"study_design_scores_gemma":[0.00276672,0.0008941175,0.001572216,0.02724589,0.0003194883,0.00006349355,0.2443474,0.000009795171,0.003053316,0.001132809,0.7173226,0.001272149],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08977737,0.318423,0.001325415,0.0001938097,0.003308238,0.004798684,0.000306633,0.0001980459,0.5816688],"genre_scores_gemma":[0.8569019,0.004826113,0.1308764,0.002880184,0.0008288838,0.0004429624,0.000709039,0.0001209328,0.002413488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7671246,"threshold_uncertainty_score":0.5929741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03011639510493548,"score_gpt":0.3789047949501207,"score_spread":0.3487883998451853,"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."}}