{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04363044,0.000928307,0.0005032283,0.001531411,0.001607839,0.001298111,0.00115217,0.001373294,0.002556743],"category_scores_gemma":[0.08157086,0.0008753906,0.0008895319,0.00101288,0.001449668,0.002932253,0.001864825,0.001037286,0.001035906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001201584,"about_ca_system_score_gemma":0.004676297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003249183,"about_ca_topic_score_gemma":0.003600426,"domain_scores_codex":[0.969133,0.02208688,0.002725246,0.001789885,0.003288095,0.0009769477],"domain_scores_gemma":[0.9097985,0.05989442,0.006971549,0.005654818,0.0154612,0.002219427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01747917,0.09540027,0.3590035,0.005197876,0.0005410222,0.007106033,0.07257353,0.004750017,0.03282376,0.002166969,0.005309099,0.3976487],"study_design_scores_gemma":[0.01552965,0.2989047,0.4761547,0.001360362,0.001697522,0.01056422,0.057388,0.05805495,0.0478355,0.003025769,0.02871807,0.0007665809],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9817019,0.00009522441,0.008029531,0.0003582936,0.00002817266,0.008011376,0.0001715102,0.0000660217,0.001537996],"genre_scores_gemma":[0.9332205,0.0003283357,0.05494864,0.0004261763,0.00006540526,0.009661447,0.000545419,0.00004641166,0.0007576608],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04363044,"threshold_uncertainty_score":0.2307426,"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."}}