{"id":"W2108731642","doi":"10.5539/ijel.v4n2p78","title":"The Features of Maritime English Discourse","year":2014,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Linguistic Variation and Morphology","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Linguistics; Scope (computer science); Conversation; Perspective (graphical); Transcription (linguistics); English for specific purposes; Natural language processing; Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001842552,0.00007615762,0.0001591264,0.00009122158,0.0001671779,0.0001066837,0.0007357407,0.00008323146,0.00008114792],"category_scores_gemma":[0.4481348,0.00005724911,0.0001188181,0.00007528728,0.0003431985,0.00003538636,0.00005322808,0.0002510055,0.000002145847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006479936,"about_ca_system_score_gemma":0.0002581299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001080817,"about_ca_topic_score_gemma":0.0001852581,"domain_scores_codex":[0.9983392,0.0002025538,0.0004927202,0.00007776137,0.0007257147,0.0001620399],"domain_scores_gemma":[0.9646427,0.001381135,0.0006070829,0.0001158207,0.0331518,0.0001014857],"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":[0.00005934802,0.00006852917,0.001496491,0.000002331324,0.0001014998,0.000008631396,0.008403004,0.00005167281,0.000002871909,0.9581458,0.02892328,0.002736524],"study_design_scores_gemma":[0.0004068194,0.00005055502,0.00139579,0.00002423282,0.00003302391,6.984835e-7,0.002602804,0.00001690532,0.00005730613,0.01068134,0.9846658,0.0000647047],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.003885262,0.0002433065,0.00161016,0.0007619633,0.1250607,0.0001012867,0.00003137355,0.00003499019,0.868271],"genre_scores_gemma":[0.9518237,0.0001596203,0.001190778,0.0001973284,0.04569789,7.244028e-7,0.000003633902,0.000008901618,0.0009174866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9557425,"threshold_uncertainty_score":0.5565138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01151227404578625,"score_gpt":0.3193244466536569,"score_spread":0.3078121726078707,"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."}}