{"id":"W2557254890","doi":"10.21992/t9ss57","title":"The Use of Translation Notes in Manga Scanlation","year":2016,"lang":"en","type":"article","venue":"TranscUlturAl A Journal of Translation and Cultural Studies","topic":"Comics and Graphic Narratives","field":"Arts and Humanities","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Foregrounding; Translation (biology); Linguistics; Computer science; Argument (complex analysis); Comics; Source text; Dynamic and formal equivalence; Comprehension; Field (mathematics); Natural language processing; Artificial intelligence; Machine translation; Philosophy; Mathematics; Medicine","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":[0.007401648,0.0005260658,0.000296249,0.001731427,0.007395316,0.004929778,0.0008948087,0.001095539,0.005863137],"category_scores_gemma":[0.01999267,0.0003002703,0.0002407264,0.00208452,0.01059709,0.005491027,0.006863329,0.001998199,0.001094946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002665141,"about_ca_system_score_gemma":0.002763545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002369235,"about_ca_topic_score_gemma":0.004516235,"domain_scores_codex":[0.9903792,0.007475738,0.0003670982,0.0005103971,0.0008395036,0.0004280137],"domain_scores_gemma":[0.9813988,0.01284112,0.002095216,0.002112774,0.0009930991,0.0005589439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001239946,0.0000438161,0.003817676,0.0002753615,0.00000495497,0.001424522,0.8771405,0.00007732229,0.005430762,0.04397827,0.001145876,0.06653698],"study_design_scores_gemma":[0.00002441626,0.0003168659,0.01126007,0.001048968,0.00002680614,0.003157807,0.658769,0.0008957104,0.008479756,0.0154408,0.3004967,0.00008318641],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8171895,0.001898206,0.02316054,0.00578852,0.0003952214,0.0002685982,0.00007459699,0.0002686194,0.1509563],"genre_scores_gemma":[0.9808267,0.0004083668,0.005560379,0.0002048121,0.00004897367,0.00008867132,0.0000326259,0.0001022214,0.01272719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007401648,"threshold_uncertainty_score":0.0391441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2362910110734578,"score_gpt":0.3023408812370902,"score_spread":0.06604987016363248,"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."}}