{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006365196,0.0001894114,0.0001634351,0.001922463,0.00109621,0.00321859,0.0003136284,0.0003453982,0.001597646],"category_scores_gemma":[0.003953311,0.0001486127,0.000102418,0.001742283,0.002699489,0.002633419,0.001323759,0.0003847244,0.0001976128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008217687,"about_ca_system_score_gemma":0.0002950677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002480178,"about_ca_topic_score_gemma":0.002372942,"domain_scores_codex":[0.9991902,0.0004382668,0.00006884887,0.0001080648,0.0001380288,0.00005664406],"domain_scores_gemma":[0.9980184,0.001236735,0.0002785407,0.0001796572,0.0002341982,0.0000525274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003415383,0.00003442937,0.03004589,0.0007951666,0.00003833778,0.002438295,0.6192946,0.0007142697,0.0312724,0.2084823,0.002174604,0.1043683],"study_design_scores_gemma":[0.00003967904,0.000144335,0.2182481,0.000919542,0.00009130092,0.00544324,0.4233948,0.00518735,0.009694329,0.08756931,0.2491113,0.0001565934],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9143651,0.002532686,0.01040849,0.0016404,0.00004294727,0.00003456691,0.0003988495,0.0001009411,0.07047615],"genre_scores_gemma":[0.9982263,0.0001664701,0.0006992078,0.00002671563,0.00001546781,0.00001053492,0.00006891443,0.00001635856,0.0007700016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00321859,"threshold_uncertainty_score":0.005962372,"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."}}