{"id":"W4285245912","doi":"10.18653/v1/2022.lchange-1.2","title":"Language Acquisition, Neutral Change, and Diachronic Trends in Noun Classifiers","year":2022,"lang":"en","type":"article","venue":"","topic":"Language and cultural evolution","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Classifier (UML); Ambiguity; Computer science; Categorization; Artificial intelligence; Noun; Population; Mandarin Chinese; Natural language processing; Speech recognition; Machine learning; Linguistics","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":[0.002304634,0.0001652705,0.0001754571,0.0007770516,0.0005068028,0.001491327,0.0004814652,0.0005092256,0.002690828],"category_scores_gemma":[0.01104147,0.0002244117,0.0001988486,0.0004990296,0.001593766,0.001920147,0.0007058605,0.000588576,0.0002725753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001395291,"about_ca_system_score_gemma":0.0005727867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01100372,"about_ca_topic_score_gemma":0.01417053,"domain_scores_codex":[0.9992247,0.0002409497,0.00005205831,0.0002580389,0.000133989,0.00009028584],"domain_scores_gemma":[0.994282,0.003066703,0.00104023,0.0005666233,0.0007416807,0.00030283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004615731,0.0002744415,0.843775,0.0001023187,0.0001191355,0.0005679791,0.01954614,0.004748484,0.04881978,0.02249258,0.0002975718,0.05879511],"study_design_scores_gemma":[0.00001410588,0.0002887265,0.9702519,0.0000176167,0.0000262524,0.0003560645,0.004438618,0.009842926,0.003692196,0.009415044,0.001620885,0.00003563003],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980161,0.00003392678,0.0004032743,0.00005018387,0.000001319096,0.000003396985,0.00003986458,0.000004210416,0.001447705],"genre_scores_gemma":[0.9993199,0.00002864809,0.0002389353,0.000007532302,0.000001362182,0.000004790142,0.00005991028,0.000003197551,0.0003356994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01100372,"threshold_uncertainty_score":0.02187932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03106964159270493,"score_gpt":0.3062419235609651,"score_spread":0.2751722819682602,"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."}}