{"id":"W2461453537","doi":"10.52034/lanstts.v3i.107","title":"Building specialized dictionaries using lexical functions","year":2021,"lang":"en","type":"article","venue":"Linguistica Antverpiensia New Series – Themes in Translation Studies","topic":"linguistics and terminology studies","field":"Arts and Humanities","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Variety (cybernetics); Lexical item; Linguistics; Natural language processing; Term (time); Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001607783,0.0003182152,0.0005876616,0.0001758187,0.001088549,0.0001519156,0.00009074363,0.00008181565,0.0004427475],"category_scores_gemma":[0.001548019,0.0002980741,0.0001492595,0.0001739084,0.001019345,0.0001500181,0.00009533169,0.000246109,0.00001213591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006742791,"about_ca_system_score_gemma":0.0001559545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001307194,"about_ca_topic_score_gemma":0.002240445,"domain_scores_codex":[0.9981195,0.0001003644,0.0006258257,0.000470605,0.0002629387,0.0004207487],"domain_scores_gemma":[0.9983616,0.0005450432,0.000124346,0.0002446842,0.0006552407,0.00006902489],"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":[0.0001449607,0.0001019908,0.00147646,0.0000698184,0.0004979014,0.00022433,0.04415788,0.000168609,0.0002265219,0.9482991,0.001286238,0.003346194],"study_design_scores_gemma":[0.001092756,0.00007435902,0.0004439917,0.0001704921,0.0003038973,0.00005237989,0.02021909,0.0005589689,0.0004299174,0.1037402,0.8723753,0.0005385957],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2975519,0.1449716,0.0276157,0.02375407,0.08185302,0.002386057,0.001002113,0.002541076,0.4183244],"genre_scores_gemma":[0.9712313,0.001424681,0.01617376,0.0003209173,0.005665069,0.00001761963,0.00003678474,0.0000503929,0.005079415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8710891,"threshold_uncertainty_score":0.9999471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1616469854535099,"score_gpt":0.3455365172164577,"score_spread":0.1838895317629478,"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."}}