{"id":"W2416738010","doi":"10.1177/0023830916650714","title":"Prediction of Agreement and Phonetic Overlap Shape Sublexical Identification","year":2016,"lang":"en","type":"article","venue":"Language and Speech","topic":"Phonetics and Phonology Research","field":"Psychology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"Economic and Social Research Council; Consejo Superior de Investigaciones Científicas; European Research Council; Ministerio de Ciencia e Innovación","keywords":"Adjective; Identification (biology); Context (archaeology); Agreement; Phrase; Linguistics; Psychology; Parsing; Lexicon; Natural language processing; Computer science; Noun; Speech recognition","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001934658,0.0002714286,0.0004154728,0.0005099652,0.0002403026,0.001777062,0.0004552927,0.0007717972,0.006020288],"category_scores_gemma":[0.02044877,0.0004762677,0.0003302163,0.0002610071,0.0009235462,0.001498711,0.00143016,0.0007028003,0.001546611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002575856,"about_ca_system_score_gemma":0.0003334211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001208192,"about_ca_topic_score_gemma":0.001135973,"domain_scores_codex":[0.9986004,0.0003621901,0.00008652292,0.0004887998,0.0003018901,0.0001601321],"domain_scores_gemma":[0.9859449,0.00959918,0.001560292,0.001356288,0.0009999651,0.0005392808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00351179,0.0003313196,0.4258888,0.000290303,0.0001404419,0.0006624865,0.005564366,0.005583982,0.5040382,0.006276093,0.0005378412,0.04717441],"study_design_scores_gemma":[0.0001129626,0.0005095938,0.8962738,0.00002531059,0.00008276427,0.0004076668,0.001978123,0.04788943,0.03860953,0.01290656,0.001131169,0.00007317809],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860366,0.0000406927,0.009171511,0.00004936512,0.00001086347,0.0000214591,0.00008312997,0.0001021847,0.004484265],"genre_scores_gemma":[0.9987761,0.000008801974,0.0008889704,0.00001195387,0.000003380901,0.000009645307,0.00008312907,0.00005571859,0.0001622816],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006020288,"threshold_uncertainty_score":0.02013981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03082872949351307,"score_gpt":0.3165306262144638,"score_spread":0.2857018967209508,"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."}}