{"id":"W3201262565","doi":"10.1075/li.00058.ior","title":"Minimodel of semantic synthesis of Russian sentences","year":2021,"lang":"en","type":"article","venue":"Lingvisticae Investigationes","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Lexicalization; Lexicographical order; Computer science; Representation (politics); Natural language processing; Artificial intelligence; Matching (statistics); Linguistics; Mathematics; Philosophy","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":[0.0007072476,0.0004469545,0.000458172,0.0007079404,0.000598949,0.001705657,0.0007853474,0.0008588278,0.005584404],"category_scores_gemma":[0.001670069,0.0002624354,0.001195403,0.0004115265,0.000979424,0.001892478,0.0009279422,0.0006715776,0.0008039997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001533543,"about_ca_system_score_gemma":0.0008555007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00264187,"about_ca_topic_score_gemma":0.003351272,"domain_scores_codex":[0.9994087,0.0002277142,0.00004107025,0.000162451,0.0001007616,0.00005926689],"domain_scores_gemma":[0.9996415,0.000170764,0.00003455627,0.0000601277,0.00007472573,0.0000183405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007604223,0.00004082057,0.0004001868,0.0001408953,0.00003135648,0.0002259321,0.0005349971,0.08900186,0.00734384,0.8786515,0.0009377386,0.02261481],"study_design_scores_gemma":[0.00003160837,0.00005907902,0.0001988596,0.00003110524,0.00002848408,0.00007681943,0.0001828026,0.4650048,0.004519181,0.5199307,0.009919508,0.00001710303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0879826,0.0002479312,0.8890057,0.0005992241,0.00005657933,0.0001485511,0.0008882162,0.0009620793,0.02010911],"genre_scores_gemma":[0.749392,0.0001521303,0.2383447,0.0001086106,0.00004429481,0.0002662425,0.001535366,0.0002132212,0.009943544],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005584404,"threshold_uncertainty_score":0.01868165,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01747218787358303,"score_gpt":0.2576618983789042,"score_spread":0.2401897105053211,"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."}}