{"id":"W4409672385","doi":"10.31234/osf.io/cpv3s_v1","title":"Cross-linguistic relations between quantifiers and numerals in language acquisition: Evidence from Japanese","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Connaught Fund; University of Toronto","keywords":"Numeral system; Linguistics; Computer science; Natural language processing; Psychology; Artificial intelligence; Philosophy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006602134,0.0002625764,0.000472831,0.000136171,0.0001019491,0.0005272867,0.0009919794,0.0003226846,0.00007075403],"category_scores_gemma":[0.0004248394,0.0001995616,0.0000915192,0.0001443795,0.00009691629,0.0003287129,0.0009492217,0.0003339659,0.0001681709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009596398,"about_ca_system_score_gemma":0.0001188219,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004566256,"about_ca_topic_score_gemma":0.0001110547,"domain_scores_codex":[0.9976941,0.0002311255,0.0005594724,0.0008992715,0.0003177324,0.0002983138],"domain_scores_gemma":[0.9973443,0.001183752,0.0002229623,0.001008666,0.0001078828,0.0001324796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005097872,0.0000761999,0.8954988,0.0003206523,0.0002570241,0.0004109886,0.03493621,0.0004446074,0.00123301,0.05428733,0.001114472,0.0113698],"study_design_scores_gemma":[0.00127428,0.00006880001,0.7898107,0.003409715,0.00005849345,0.00001000947,0.0002888743,0.009390903,0.0001087855,0.1941665,0.0001645925,0.001248378],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7871639,0.01044325,0.1617821,0.003451711,0.002134535,0.001033972,0.0001657487,0.0005194152,0.03330542],"genre_scores_gemma":[0.9937995,0.00002940319,0.002829749,0.0001170268,0.0005350768,0.0000599681,0.00001932789,0.00001082267,0.002599127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2066357,"threshold_uncertainty_score":0.8137885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03729169733243068,"score_gpt":0.314151829013276,"score_spread":0.2768601316808453,"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."}}