{"id":"W2789336736","doi":"10.3390/languages3010006","title":"On Recursive Modification in Child L1 French","year":2018,"lang":"en","type":"article","venue":"Languages","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; University of Toronto","funders":"","keywords":"Recursion (computer science); Merge (version control); Computer science; Universal grammar; Universality (dynamical systems); Grammar; Linguistics; Embedding; Schema (genetic algorithms); Minimalist program; Theoretical computer science; Syntax; Artificial intelligence; Programming language; Generative grammar","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":[0.002063981,0.0005584453,0.000646972,0.001551629,0.000781886,0.001856648,0.0004868152,0.001008746,0.008003206],"category_scores_gemma":[0.007227958,0.0003565773,0.0003537062,0.0008113196,0.002166578,0.001971241,0.001348425,0.0009823148,0.00150504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001623387,"about_ca_system_score_gemma":0.0008714163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03919265,"about_ca_topic_score_gemma":0.04495658,"domain_scores_codex":[0.9986268,0.0003914904,0.00006833182,0.0002852508,0.000400871,0.0002272444],"domain_scores_gemma":[0.9940253,0.003312567,0.001198566,0.0004446713,0.000843728,0.0001752369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008972866,0.0005949019,0.4866048,0.001077901,0.000131508,0.01069561,0.2261484,0.001466952,0.0540736,0.01843985,0.004729536,0.1951397],"study_design_scores_gemma":[0.00003815471,0.0007132572,0.9028254,0.0003374169,0.00009402681,0.009153781,0.02977443,0.001226375,0.01077808,0.004483935,0.04039135,0.0001838446],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9859811,0.0007026266,0.0006825435,0.000297508,0.000007628984,0.00001285712,0.000387342,0.00006766016,0.01186082],"genre_scores_gemma":[0.9953672,0.0004989162,0.0007850779,0.0001981083,0.000006566672,0.00002769523,0.0004523231,0.00004299079,0.0026212],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03919265,"threshold_uncertainty_score":0.07792902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009840454882455323,"score_gpt":0.293848008375082,"score_spread":0.2840075534926266,"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."}}