{"id":"W2100108602","doi":"10.1017/s0952675714000062","title":"Learning in Harmonic Serialism and the necessity of a richer base","year":2014,"lang":"en","type":"article","venue":"Phonology","topic":"Phonetics and Phonology Research","field":"Psychology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Markedness; Constraint (computer-aided design); Optimality theory; Phonotactics; Computer science; Grammar; Linguistics; Identity (music); Generative grammar; Harmonic; Vowel; Artificial intelligence; Natural language processing; Speech recognition; Phonology; Mathematics; Philosophy","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":[0.001212184,0.0000861708,0.0002719116,0.0001084254,0.00005571261,0.000007146758,0.0002128541,0.0001670161,0.000594484],"category_scores_gemma":[0.0002883481,0.00006258101,0.00003092418,0.000163425,0.000602335,0.00001812599,0.0001397555,0.0004771486,0.00008437719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009019439,"about_ca_system_score_gemma":0.00002374047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002040532,"about_ca_topic_score_gemma":0.000297232,"domain_scores_codex":[0.9981601,0.001061558,0.0002037487,0.0002283768,0.00005231321,0.0002938509],"domain_scores_gemma":[0.9989104,0.0006713543,0.00008301064,0.0002694613,0.00002920088,0.00003655818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.007658765,0.0008857151,0.4510469,0.000137627,0.0005487108,0.0000773554,0.1588725,0.00006266936,0.08168125,0.133337,0.006341114,0.1593503],"study_design_scores_gemma":[0.009216777,0.0004367543,0.9312347,0.00001165487,0.00002654464,0.00005902357,0.0007215533,0.001352153,0.002728022,0.04506034,0.008959515,0.0001929539],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9908882,0.0006995898,0.0001123019,0.002887713,0.0003405344,0.0001250898,0.000001397915,0.0000165629,0.004928614],"genre_scores_gemma":[0.9987074,0.00009279713,0.00004381647,0.000291048,0.00005332486,0.00003607921,0.000002194651,0.000009813007,0.0007635274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4801877,"threshold_uncertainty_score":0.6509183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02276526167262656,"score_gpt":0.3129698551361772,"score_spread":0.2902045934635507,"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."}}