{"id":"W1980232445","doi":"10.7202/037680ar","title":"Finding Translations. On the Use of Bibliographical Databases in Translation History","year":2009,"lang":"en","type":"article","venue":"Meta Journal des traducteurs","topic":"Translation Studies and Practices","field":"Arts and Humanities","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Translation (biology); Database; Scale (ratio); Index (typography); Period (music); Information retrieval; Natural language processing; World Wide Web; Geography","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01697763,0.0006097017,0.0009775595,0.0272044,0.003924766,0.01366825,0.001638081,0.001813783,0.01818145],"category_scores_gemma":[0.05699026,0.0007808667,0.0009864316,0.05925921,0.007580125,0.02660247,0.006802975,0.001330747,0.004928702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003753256,"about_ca_system_score_gemma":0.003192292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006261915,"about_ca_topic_score_gemma":0.009883111,"domain_scores_codex":[0.988018,0.006808598,0.001508019,0.001618934,0.00182877,0.0002176221],"domain_scores_gemma":[0.9331988,0.04900205,0.003792382,0.00977196,0.003636335,0.0005985611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008574715,0.00003638357,0.008614101,0.001841087,0.00009484286,0.0003103417,0.01367478,0.000661817,0.0008526501,0.4853348,0.00981936,0.4786741],"study_design_scores_gemma":[0.00004942506,0.00007511824,0.01570575,0.002636114,0.0001769446,0.001823734,0.01906721,0.006233701,0.00308305,0.5255359,0.4255043,0.0001086738],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05745015,0.07529008,0.5956786,0.02352168,0.0009066461,0.001155561,0.009061235,0.002450537,0.2344856],"genre_scores_gemma":[0.433296,0.03426026,0.4880199,0.002045399,0.0008479874,0.0007528538,0.005947983,0.0009480231,0.03388156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9727956,"threshold_uncertainty_score":0.0897873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3909388739645495,"score_gpt":0.3202918268762726,"score_spread":0.0706470470882769,"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."}}