{"id":"W4253348229","doi":"10.1007/978-0-387-39940-9_3009","title":"Machine-Readable Dictionary (MRD)","year":2009,"lang":"en","type":"book-chapter","venue":"Encyclopedia of Database Systems","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Natural language processing; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004535774,0.0005136376,0.0006998381,0.000409055,0.0001052453,0.00009063344,0.001871932,0.0003525734,0.00006849517],"category_scores_gemma":[0.00005223625,0.0004567763,0.0001662163,0.0001080027,0.00007480674,0.0008276629,0.0005720127,0.0006365122,0.0001316271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001055396,"about_ca_system_score_gemma":0.0002441519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002898309,"about_ca_topic_score_gemma":0.00001134824,"domain_scores_codex":[0.9971728,0.00005048399,0.0007963159,0.000805178,0.0008406641,0.0003346115],"domain_scores_gemma":[0.9969335,0.0001273363,0.0007135562,0.001859929,0.000211039,0.0001546721],"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":[0.00001119612,0.00005278515,0.00001137324,0.0006001416,0.00007200577,0.0002989647,0.00009613985,0.000005410873,0.0001007932,0.8763456,0.07153025,0.05087532],"study_design_scores_gemma":[0.0001721557,0.0001217758,0.000002235976,0.001467858,0.00005163707,0.0001796366,0.000003508999,0.001166575,0.0001548485,0.02325915,0.9727161,0.0007045438],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[6.18033e-7,0.06442874,0.1008361,0.0001156926,0.001361747,0.000581773,0.0007657795,0.0008999886,0.8310096],"genre_scores_gemma":[0.0004033656,0.004903445,0.1922137,0.0001375886,0.0009462293,0.00004026981,0.001136241,0.0001004483,0.8001187],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9011858,"threshold_uncertainty_score":0.9997884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01097752820018187,"score_gpt":0.2376979927240999,"score_spread":0.226720464523918,"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."}}