{"id":"W2992710831","doi":"","title":"The RACAD speech corpus of New Brunswick Acadian French: Design and applications","year":2008,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Linguistic Variation and Morphology","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton; University of New Brunswick","funders":"Defense Advanced Research Projects Agency; University of Cambridge; New Brunswick Innovation Foundation; Université de Moncton","keywords":"Phone; Computer science; Speech corpus; Speech recognition; Variation (astronomy); Corpus linguistics; Natural language processing; Word (group theory); Text corpus; Linguistics; Artificial intelligence; Speech synthesis","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000290677,0.0000567883,0.00008304616,0.00006085971,0.0008112512,0.00002159852,0.000197419,0.000117517,0.00006962249],"category_scores_gemma":[0.0008117554,0.00005202614,0.00001654708,0.0001892456,0.0004170521,0.00001910156,0.000009251778,0.0001069458,0.00002677842],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009877732,"about_ca_system_score_gemma":0.005997412,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5127739,"about_ca_topic_score_gemma":0.4133835,"domain_scores_codex":[0.9993083,0.00006279004,0.0001425536,0.0001081461,0.0001166438,0.0002615439],"domain_scores_gemma":[0.9988543,0.0002947111,0.00006823603,0.0001411722,0.0001255358,0.0005159901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005667875,0.00002027467,0.01063392,0.000009051567,0.00004298681,0.00006774308,0.0159548,0.0002737738,0.0003320117,0.7553288,0.1678362,0.04949474],"study_design_scores_gemma":[0.0002153937,0.00002229232,0.04016324,0.000003933728,0.00003285343,0.00002305333,0.0007542758,0.0003358533,0.00002152193,0.01028308,0.9480083,0.0001361734],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06273962,0.00444104,0.6335693,0.03504073,0.004717553,0.005209402,0.0001960104,0.0002715962,0.2538147],"genre_scores_gemma":[0.9719434,0.001164148,0.009083699,0.00113795,0.0006517937,0.000007960559,0.000006258384,0.00001201735,0.01599271],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9092038,"threshold_uncertainty_score":0.9996377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04087017483337119,"score_gpt":0.2790398882007486,"score_spread":0.2381697133673774,"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."}}