{"id":"W2037202300","doi":"10.1353/lan.2011.0016","title":"The Surfeit of the Stimulus: Analytic Biases Filter Lexical Statistics in Turkish Laryngeal Alternations","year":2011,"lang":"en","type":"article","venue":"Language","topic":"Music and Audio Processing","field":"Computer Science","cited_by":224,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Phonotactics; Lexicon; Turkish; Linguistics; Vowel harmony; Alternation (linguistics); Psychology; Phrase; Phonology; Computer science; Natural language processing","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001719498,0.00005594958,0.00007460446,0.00002668831,0.00008297973,0.00004789557,0.0006195189,0.00001915021,0.00004706677],"category_scores_gemma":[0.0001450821,0.00003075756,0.00002851937,0.0002122857,0.00007702377,0.0001027097,0.0001568052,0.00009331354,0.000005462743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000105529,"about_ca_system_score_gemma":0.00004330902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003596086,"about_ca_topic_score_gemma":0.0006248003,"domain_scores_codex":[0.9993799,0.00005363337,0.0001505689,0.0001151791,0.0001603269,0.000140401],"domain_scores_gemma":[0.9993507,0.0002057235,0.00006919847,0.0003227671,0.00002827643,0.00002334927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00006456085,0.001063159,0.1151946,0.0002207283,0.000200241,0.0006035739,0.1499164,0.00129251,0.004846499,0.2767287,0.03984748,0.4100216],"study_design_scores_gemma":[0.001751971,0.0002065574,0.5570686,0.0004984152,0.00007856215,0.00005382477,0.002489197,0.3548251,0.04937948,0.02839094,0.004422707,0.0008347079],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6848574,0.0007098683,0.3015816,0.001186406,0.0005383092,0.0002090863,0.0000592455,0.00005611228,0.010802],"genre_scores_gemma":[0.9929959,0.00000447032,0.005969419,0.0004180616,0.00003174565,0.000002705197,0.000001447837,0.000003590266,0.0005726827],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.441874,"threshold_uncertainty_score":0.1254257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.036742338371428,"score_gpt":0.2724039918726861,"score_spread":0.2356616535012581,"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."}}