{"id":"W174563456","doi":"","title":"Evaluating automatic syllabification algorithms for English","year":2007,"lang":"en","type":"article","venue":"ePrints Soton (University of Southampton)","topic":"Phonetics and Phonology Research","field":"Psychology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Killam Trusts","keywords":"Syllabification; Computer science; Syllable; Artificial intelligence; Natural language processing; Word (group theory); Task (project management); Set (abstract data type); Margin (machine learning); Speech recognition; Algorithm; Mathematics; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00365291,0.0009975529,0.0008851822,0.002766626,0.0006311818,0.001345878,0.00146553,0.001109899,0.002971826],"category_scores_gemma":[0.01534786,0.0004071871,0.0008674477,0.001455411,0.0004557849,0.001586556,0.001061768,0.001083032,0.001196017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001379581,"about_ca_system_score_gemma":0.001142753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006926607,"about_ca_topic_score_gemma":0.00661168,"domain_scores_codex":[0.9974928,0.0006850585,0.0003129387,0.0006707325,0.0006524411,0.0001859837],"domain_scores_gemma":[0.9885039,0.008824531,0.0002927016,0.0006732269,0.001550292,0.0001553907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001060923,0.0004828278,0.006000732,0.0008778934,0.0003003432,0.0001540139,0.0005317269,0.107521,0.02377691,0.005362585,0.004669083,0.8492619],"study_design_scores_gemma":[0.0002156931,0.0005525074,0.01263601,0.00008588236,0.0001221466,0.0002686593,0.0004261895,0.9346835,0.04117979,0.004870604,0.00488375,0.00007520382],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6838409,0.004128863,0.2892359,0.0002332781,0.0002726955,0.0006811079,0.002067919,0.009223531,0.01031583],"genre_scores_gemma":[0.6533222,0.0007538134,0.3355908,0.00008335152,0.00005607528,0.0005081495,0.006884194,0.0004941515,0.002307192],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006926607,"threshold_uncertainty_score":0.0193187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1165340597800772,"score_gpt":0.3979764743016492,"score_spread":0.2814424145215719,"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."}}