{"id":"W2914250502","doi":"10.15393/uchz.art.2019.271","title":"CANADIAN STRESS: FROM SPECIFICITY TO THE WHOLE LEXICON","year":2019,"lang":"en","type":"article","venue":"Proceedings of Petrozavodsk State University","topic":"Multicultural Socio-Legal Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Lexicon; Linguistics; Stress (linguistics); Natural language processing; Psychology; Computer science; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002653707,0.0001231207,0.0001928394,0.00008212015,0.0007700686,0.00007714552,0.0006206285,0.00006251912,0.0003219784],"category_scores_gemma":[0.0001190711,0.0001025518,0.00007655497,0.0004097515,0.0001861999,0.0003882263,0.000147831,0.0001681921,0.000247942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009200019,"about_ca_system_score_gemma":0.0001618248,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7465872,"about_ca_topic_score_gemma":0.6333566,"domain_scores_codex":[0.9987912,0.00002093893,0.0001118937,0.0002795872,0.0003800768,0.0004163543],"domain_scores_gemma":[0.9990572,0.00007282442,0.0001079608,0.00007508722,0.0004078311,0.0002791017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000143422,0.00008822489,0.7117758,0.00004455424,0.0002115882,0.00000717646,0.1995741,0.00005897904,0.004254945,0.03799868,0.04376546,0.00207706],"study_design_scores_gemma":[0.0002691363,0.00005030796,0.1430689,0.0000383158,0.00002624971,6.106435e-8,0.1724274,0.00001305986,0.0006921728,0.0004240839,0.682763,0.000227283],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9028376,0.00002867505,0.000002929361,0.01060121,0.0001744853,0.0003544224,0.0002858724,0.00004227373,0.08567253],"genre_scores_gemma":[0.9864188,0.00006934957,0.0003098184,0.00009900312,0.0001037389,4.947167e-7,0.000004545656,0.000006357687,0.0129879],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6389976,"threshold_uncertainty_score":0.5922823,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01370145500343598,"score_gpt":0.2253518766487254,"score_spread":0.2116504216452895,"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."}}