{"id":"W2095922715","doi":"10.1080/01690965.2013.813562","title":"Stress consistency and stress regularity effects in Russian","year":2013,"lang":"en","type":"article","venue":"Language Cognition and Neuroscience","topic":"Reading and Literacy Development","field":"Psychology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stress (linguistics); Consistency (knowledge bases); Syllable; Noun; Linguistics; Lexical decision task; Natural language processing; Psychology; Word (group theory); Spelling; Computer science; Artificial intelligence; Cognition","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008462174,0.00008239555,0.00009251583,0.00008261044,0.00007860793,0.00008993765,0.0000570548,0.00003805906,0.00009060442],"category_scores_gemma":[0.0000728108,0.00007067182,0.000009246442,0.0001346751,0.0001471818,0.0001329126,0.00003392389,0.0000995665,0.000017951],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004177757,"about_ca_system_score_gemma":0.000006994695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000149832,"about_ca_topic_score_gemma":0.00002707363,"domain_scores_codex":[0.999239,0.0000912326,0.000111528,0.0002920669,0.00008554629,0.0001806657],"domain_scores_gemma":[0.9996763,0.0000854564,0.00003387256,0.0001047482,0.00001169742,0.00008794682],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007045535,0.0008576836,0.4352549,0.0004887505,0.0000129322,0.001240883,0.05377954,0.000001085684,0.2585249,0.02053348,0.0005790105,0.2286564],"study_design_scores_gemma":[0.000513843,0.0000533216,0.9899532,0.0001107463,0.000004230615,0.00005174278,0.0008655045,0.00007067144,0.00777054,0.000381961,0.0001018116,0.0001224538],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9905953,0.0003242646,0.00005205408,0.0002113263,0.0002441907,0.0002164627,0.00001536431,0.00003117705,0.008309887],"genre_scores_gemma":[0.9979172,0.00002139425,0.00009042534,0.0009849678,0.00001411487,0.00004160164,0.000006891492,0.000004623016,0.0009188168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5546982,"threshold_uncertainty_score":0.2881913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01361796272382181,"score_gpt":0.28616604046997,"score_spread":0.2725480777461481,"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."}}