{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008878655,0.0008730611,0.0009742225,0.003731561,0.006892656,0.01018202,0.001709816,0.001325202,0.02520455],"category_scores_gemma":[0.006810686,0.0008077202,0.0003316844,0.006402899,0.006342734,0.007608767,0.003988268,0.002732955,0.002742399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02900908,"about_ca_system_score_gemma":0.03299578,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8831508,"about_ca_topic_score_gemma":0.9055544,"domain_scores_codex":[0.9980376,0.0002877105,0.0001000049,0.0004648827,0.0007352231,0.0003745109],"domain_scores_gemma":[0.9974018,0.0004889816,0.0001012786,0.0003606892,0.001464191,0.0001829905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003627486,0.00003757657,0.01106223,0.0005611613,0.0000554563,0.0008200018,0.0669765,0.0006742708,0.007707059,0.6395315,0.09886058,0.1733509],"study_design_scores_gemma":[0.00005425202,0.00003354521,0.03969666,0.0006108419,0.0001506272,0.001107353,0.05170834,0.00317525,0.004702744,0.1794651,0.7190298,0.0002654007],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1200899,0.002853999,0.01319707,0.01032904,0.0005144388,0.00008680228,0.00529119,0.0008424286,0.8467953],"genre_scores_gemma":[0.9552189,0.001744054,0.004839959,0.0006803013,0.0001686216,0.0000362549,0.003034906,0.0008976877,0.03337929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1168492,"threshold_uncertainty_score":0.2350746,"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."}}