{"id":"W7000228838","doi":"","title":"Exploring Sentence Variations with Bilingual Corpora","year":2005,"lang":"en","type":"article","venue":"NPARC","topic":"Authorship Attribution and Profiling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Sentence; Grammar; Context (archaeology); Machine translation; Parallel corpora; Translation (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":[0.001638323,0.0006858194,0.0009056217,0.004171809,0.001324559,0.001794514,0.0007425188,0.0007531457,0.003655698],"category_scores_gemma":[0.008533455,0.0005447465,0.0006780722,0.004569449,0.0005730065,0.003563547,0.001372054,0.0006461026,0.00114399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005701404,"about_ca_system_score_gemma":0.0008741645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002160544,"about_ca_topic_score_gemma":0.004332754,"domain_scores_codex":[0.998245,0.0006836308,0.0001778515,0.000507962,0.0003096129,0.00007588614],"domain_scores_gemma":[0.9960613,0.002574894,0.0002913126,0.0003978056,0.0005484405,0.0001262614],"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":[0.001115184,0.0005472847,0.0279976,0.001427342,0.000257751,0.003122804,0.009622145,0.0157717,0.1510024,0.0156406,0.01558367,0.7579115],"study_design_scores_gemma":[0.0003488539,0.001078676,0.05787871,0.0003632086,0.0006126098,0.007540351,0.01109787,0.5328686,0.1581724,0.06674903,0.1628786,0.0004110111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5428457,0.002102723,0.4261858,0.0008694409,0.0001267987,0.0004158378,0.004021843,0.008161186,0.01527073],"genre_scores_gemma":[0.6747187,0.0005351597,0.3142374,0.0001817156,0.0000762697,0.0002522024,0.006823341,0.0007308908,0.002444448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004171809,"threshold_uncertainty_score":0.01222956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1264075600395029,"score_gpt":0.2753025695211809,"score_spread":0.148895009481678,"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."}}