{"id":"W2213110262","doi":"10.2196/publichealth.4779","title":"Machine Translation of Public Health Materials From English to Chinese: A Feasibility Study","year":2015,"lang":"en","type":"article","venue":"JMIR Public Health and Surveillance","topic":"Interpreting and Communication in Healthcare","field":"Health Professions","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Public health; Computer science; Translation (biology); Machine translation; Natural language processing; Medicine; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.01503283,0.00026979,0.0009598188,0.0002255475,0.0007360152,0.00005225246,0.000451785,0.0001775842,0.0001301379],"category_scores_gemma":[0.002906444,0.0002304422,0.00004611084,0.0007045498,0.00006466491,0.0002131282,0.0001984396,0.00059566,0.00002350358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000474036,"about_ca_system_score_gemma":0.003369979,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01142917,"about_ca_topic_score_gemma":0.006574864,"domain_scores_codex":[0.9854126,0.01045311,0.001939062,0.0005939969,0.000547415,0.001053822],"domain_scores_gemma":[0.9938661,0.001071875,0.0007970954,0.0011481,0.00114555,0.001971228],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001390151,0.0003593721,0.8680542,0.0004589513,0.00001560098,1.806069e-7,0.1044507,4.095326e-7,0.000002869859,0.0001582111,0.002452882,0.02390768],"study_design_scores_gemma":[0.002691785,0.001480625,0.879618,0.0001153467,4.731685e-7,8.629905e-7,0.03444858,0.0002233524,2.317389e-7,0.0002580059,0.08087113,0.000291577],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9220159,0.002657927,0.0005096043,0.06766985,0.001044147,0.004143307,0.000637156,0.0002492731,0.001072855],"genre_scores_gemma":[0.9911805,0.0001759231,0.0007053799,0.006407977,0.0003221086,0.0006379506,0.0004441343,0.00003542601,0.00009060128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07841825,"threshold_uncertainty_score":0.9951538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1930059551612192,"score_gpt":0.4707836307542433,"score_spread":0.2777776755930241,"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."}}