{"id":"W1538278780","doi":"10.3968/4892","title":"Linguistic Functional Feature Analysis of English Legal Memorandum","year":2014,"lang":"en","type":"article","venue":"Higher education of social science","topic":"Discourse Analysis in Language Studies","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Memorandum; Systemic functional linguistics; Systemic functional grammar; Linguistics; Theme (computing); Sentence; Mood; Perspective (graphical); Feature (linguistics); Psychology; Sociology; Computer science; Political science; Grammar; Social psychology; Artificial intelligence; Law; Philosophy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003207928,0.0000679496,0.0002105978,0.0003010721,0.0003484362,0.00006693084,0.0001845753,0.00001927319,0.002716509],"category_scores_gemma":[0.0002856625,0.00005764723,0.000127618,0.0005901353,0.001214941,0.0001593264,0.00002981531,0.00005167184,0.00000399572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003142761,"about_ca_system_score_gemma":0.0001531319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002074785,"about_ca_topic_score_gemma":0.00006641952,"domain_scores_codex":[0.9991068,0.00002557083,0.0001632777,0.000163228,0.0004261835,0.0001149068],"domain_scores_gemma":[0.9981284,0.00004550394,0.0001828462,0.0001192521,0.001494609,0.00002942706],"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":[0.000003209992,0.00009355231,0.0005546345,0.000008466444,0.0001451304,1.925936e-8,0.04377013,0.000008398849,0.00006443428,0.950308,0.004646514,0.0003975227],"study_design_scores_gemma":[0.0001200744,0.00003102393,0.184658,0.00001426415,0.001358103,2.346473e-8,0.01379381,0.00003684736,0.0001929415,0.001265937,0.7983556,0.0001734059],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03470455,0.0001249945,0.0000137908,0.0002004228,0.00295199,0.0000363194,0.00001247896,0.00001667327,0.9619388],"genre_scores_gemma":[0.9648326,0.000001258835,0.00004735826,0.0001137948,0.00242156,0.000005129574,0.00001414267,0.000003159801,0.032561],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.949042,"threshold_uncertainty_score":0.9981952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01817127735979387,"score_gpt":0.2914345968462626,"score_spread":0.2732633194864688,"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."}}