{"id":"W1969359618","doi":"10.3758/s13423-015-0802-y","title":"Lexical stress assignment as a problem of probabilistic inference","year":2015,"lang":"en","type":"article","venue":"Psychonomic Bulletin & Review","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Inference; Stress (linguistics); Psychology; Conceptualization; Probabilistic logic; Bayesian inference; Natural language processing; Bayesian probability; Posterior probability; Word (group theory); Cognitive psychology; Artificial intelligence; Linguistics; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006897926,0.000891117,0.001807118,0.002385909,0.001162691,0.005528816,0.004227073,0.002945586,0.006147734],"category_scores_gemma":[0.03216944,0.001619213,0.001916362,0.003490213,0.004057792,0.01188636,0.002637586,0.004684716,0.001688996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001335959,"about_ca_system_score_gemma":0.001808157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001510985,"about_ca_topic_score_gemma":0.001381159,"domain_scores_codex":[0.9963012,0.001493471,0.0003248465,0.0009454985,0.0007879988,0.000146929],"domain_scores_gemma":[0.980918,0.0164892,0.0005668539,0.0008445019,0.001024746,0.0001566782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001707095,0.00008134064,0.002427896,0.001257329,0.0003186331,0.0002496211,0.0005776847,0.02703248,0.002276488,0.448393,0.01247516,0.5047398],"study_design_scores_gemma":[0.00001585225,0.000009685721,0.0005485007,0.00007604596,0.00003950337,0.0001181174,0.00007238158,0.04156958,0.0005663134,0.9505476,0.006413186,0.00002332996],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01118289,0.01058277,0.9651431,0.006705969,0.0005715451,0.00005948524,0.0002951864,0.0005872002,0.004871834],"genre_scores_gemma":[0.4423004,0.02447413,0.5160812,0.002022776,0.003722823,0.0003120405,0.001438134,0.0004903607,0.009158043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006897926,"threshold_uncertainty_score":0.03648013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03417122246720825,"score_gpt":0.3275356567295525,"score_spread":0.2933644342623443,"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."}}