{"id":"W4200482297","doi":"10.1145/3494987","title":"SpeeChin","year":2021,"lang":"en","type":"article","venue":"Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies","topic":"Speech Recognition and Synthesis","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Speech recognition; Session (web analytics); Convolutional neural network; Natural language processing; Syllable; Chin; Artificial intelligence; World Wide Web","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":[0.0001491977,0.0001547635,0.0002267728,0.0001348492,0.0001453827,0.0001533233,0.001921419,0.000108834,0.00001811738],"category_scores_gemma":[0.002119716,0.000106586,0.00009803689,0.0004842621,0.0001446164,0.0004128109,0.002145173,0.0003062338,0.00001811848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000035589,"about_ca_system_score_gemma":0.00002753893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006480373,"about_ca_topic_score_gemma":0.000001773467,"domain_scores_codex":[0.9989569,0.000009778609,0.0001985344,0.0004003067,0.0002202994,0.0002141444],"domain_scores_gemma":[0.9986998,0.0002198724,0.0001753212,0.0005988989,0.0002833457,0.00002276466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004203711,0.0002676016,0.0006633347,0.00007271338,0.00008390589,0.00001247604,0.0004045853,0.000001470065,0.2079885,0.01481072,0.003499654,0.772153],"study_design_scores_gemma":[0.0001264749,0.0001442056,0.0004653957,0.0002060127,0.000008130844,0.00009797884,0.002290273,0.0001550666,0.9274625,0.06592895,0.002991936,0.000123072],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9769568,0.0004201602,0.00008144099,0.00575763,0.0002632824,0.0002433352,0.000004061244,0.0005429486,0.01573035],"genre_scores_gemma":[0.9784609,0.0003787848,0.02007384,0.0001917996,0.00001746829,0.00008539871,1.391984e-7,0.00001011402,0.0007815705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7720299,"threshold_uncertainty_score":0.4346451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01334488509768427,"score_gpt":0.2456831710635254,"score_spread":0.2323382859658411,"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."}}