{"id":"W2096979061","doi":"10.1017/s0305000904006622","title":"Large constituent families help children parse compounds","year":2005,"lang":"en","type":"article","venue":"Journal of Child Language","topic":"Language Development and Disorders","field":"Psychology","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Affect (linguistics); Lexicon; Psychology; Meaning (existential); Parsing; Linguistics; Analogy; Segmentation; Developmental psychology; Chemistry; Natural language processing; Communication; Artificial intelligence; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007170168,0.0004422006,0.0001822705,0.0003978518,0.000479159,0.0006852275,0.0001993294,0.0004671761,0.005313725],"category_scores_gemma":[0.005820545,0.0004099843,0.0003140605,0.0002009942,0.0006746385,0.001169948,0.0007623433,0.0003611289,0.0004260992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004186442,"about_ca_system_score_gemma":0.0005793935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003209108,"about_ca_topic_score_gemma":0.007814909,"domain_scores_codex":[0.9995387,0.0001472375,0.00002583403,0.0001290489,0.0001027443,0.00005646816],"domain_scores_gemma":[0.9949273,0.003210759,0.00117211,0.000283324,0.0001969101,0.0002095542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006275986,0.0004198152,0.5716949,0.0006771027,0.0001464755,0.005111456,0.06189219,0.001968966,0.1622766,0.007485857,0.004002775,0.1836963],"study_design_scores_gemma":[0.0000745914,0.0007805662,0.896257,0.0002123285,0.000395683,0.005896883,0.0212187,0.003795283,0.03646626,0.009793903,0.02500691,0.0001018074],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9963288,0.0001303606,0.0007445121,0.0001240403,0.000003445879,0.000005192509,0.00004542961,0.00004611542,0.002572098],"genre_scores_gemma":[0.9952735,0.0002090575,0.003495954,0.00005246008,0.00000371245,0.000008280046,0.00008187161,0.00003127021,0.000843806],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005313725,"threshold_uncertainty_score":0.01777619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005737437596276991,"score_gpt":0.2675994268399669,"score_spread":0.2618619892436899,"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."}}