{"id":"W4380995862","doi":"10.3390/app13127126","title":"An Optimized Approach to Translate Technical Patents from English to Japanese Using Machine Translation Models","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Topic Modeling","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Machine translation; Artificial intelligence; Natural language processing; Preprocessor; Evaluation of machine translation; Transformer; Translation (biology); Machine learning; Hyperparameter; BLEU; Example-based machine translation; Machine translation software usability; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009094221,0.001040028,0.0007154295,0.0009976398,0.0005246749,0.001066037,0.0007684338,0.0008045009,0.002268326],"category_scores_gemma":[0.00238336,0.0004129345,0.0009550292,0.001531912,0.0003003067,0.001348151,0.0007352554,0.0009515968,0.001562122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006620365,"about_ca_system_score_gemma":0.002473739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006452919,"about_ca_topic_score_gemma":0.01051932,"domain_scores_codex":[0.9994029,0.0001947784,0.00005839992,0.0001792376,0.0001093665,0.00005530253],"domain_scores_gemma":[0.9994649,0.0002163749,0.00004085881,0.00009784411,0.0001584378,0.00002148003],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002316724,0.000215602,0.001866879,0.0003832482,0.0001713597,0.0002739114,0.0002983518,0.2819573,0.02196294,0.01277311,0.01102062,0.6688449],"study_design_scores_gemma":[0.00003185165,0.00008299628,0.0006588984,0.00001466829,0.0000666445,0.0001177889,0.00008668216,0.977239,0.009163931,0.007337215,0.00517869,0.00002169128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02572651,0.0003968826,0.9668561,0.0002829983,0.00006895023,0.0001523154,0.0005777357,0.002991092,0.002947455],"genre_scores_gemma":[0.3497675,0.0005374878,0.639823,0.0001994473,0.00009007069,0.0003844877,0.003651144,0.0004613047,0.005085511],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006452919,"threshold_uncertainty_score":0.01283073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1024625656874686,"score_gpt":0.303765753619348,"score_spread":0.2013031879318794,"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."}}