{"id":"W2765320236","doi":"10.1159/000478104","title":"Acoustic Realization and Inventory Size: Kannada and Malayalam Alveolar/Retroflex Laterals and /ɻ/","year":2017,"lang":"en","type":"article","venue":"Phonetica","topic":"Phonetics and Phonology Research","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Kannada; Realization (probability); Malayalam; Speech recognition; Computer science; Mathematics; Acoustics; Natural language processing; Artificial intelligence; Statistics; Physics","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":[0.0002951021,0.0001529145,0.0002136404,0.00006086022,0.0003784705,0.0001693347,0.0001736757,0.0001733259,0.0004805],"category_scores_gemma":[0.0002128131,0.0001475158,0.00001760132,0.0000362977,0.0006060115,0.00007838469,0.0002451497,0.0002156703,0.00003522191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009766636,"about_ca_system_score_gemma":0.00002016451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007063149,"about_ca_topic_score_gemma":0.0001633127,"domain_scores_codex":[0.9988324,0.0001111236,0.0001754403,0.000399177,0.0001226648,0.0003591851],"domain_scores_gemma":[0.9990131,0.0001445245,0.00008987584,0.0005058486,0.00004774645,0.000198898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006828793,0.0003193495,0.806752,0.000330061,0.0004891834,0.0002614405,0.0148708,0.000004029205,0.1051757,0.008490735,0.04043602,0.02218788],"study_design_scores_gemma":[0.001043556,0.0001944735,0.9889275,0.00002517834,0.00003888801,0.00007038136,0.0000707962,0.0003799548,0.0004528494,0.005861418,0.002746007,0.0001890152],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887806,0.002177923,0.00004392671,0.001109459,0.0003427472,0.0002050571,0.0000181927,0.00002594907,0.007296119],"genre_scores_gemma":[0.9947354,0.0009662997,0.00006765919,0.0002240025,0.00008873304,0.00002515094,0.000006785133,0.00002325379,0.003862678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1821755,"threshold_uncertainty_score":0.6015522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03252996141715812,"score_gpt":0.3519800040041234,"score_spread":0.3194500425869652,"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."}}