{"id":"W2114260797","doi":"10.1017/s1472669606000090","title":"Sources of Legal Information in Hungary: Part 1","year":2006,"lang":"en","type":"article","venue":"Legal Information Management","topic":"Hungarian Social, Economic and Educational Studies","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Library of Parliament","funders":"","keywords":"Legislation; State (computer science); Political science; Law; Legal opinion; The Internet; Legal research; Computer science; Black letter law; World Wide Web; Comparative law; Algorithm; Private law","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001390986,0.00027349,0.0005552444,0.01363166,0.001902042,0.007716255,0.001098484,0.0009668381,0.02026846],"category_scores_gemma":[0.00369459,0.0005039743,0.000217106,0.03782877,0.001194099,0.003842964,0.002745048,0.0006186093,0.004092996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004348798,"about_ca_system_score_gemma":0.005076086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01293,"about_ca_topic_score_gemma":0.00894032,"domain_scores_codex":[0.9978125,0.0002832274,0.0002628782,0.0001525827,0.001173033,0.0003157172],"domain_scores_gemma":[0.9980103,0.0005043434,0.0005927691,0.0001705461,0.0005498825,0.0001722293],"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":[0.0001527527,0.0001614192,0.03256643,0.003172947,0.00009345976,0.004459033,0.01459972,0.001151594,0.001404601,0.1213229,0.2053389,0.6155761],"study_design_scores_gemma":[0.00002528532,0.00002916397,0.1060114,0.001686134,0.00005465804,0.002040758,0.006114826,0.0005593307,0.001955401,0.009528115,0.8719385,0.00005638291],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2082355,0.1151044,0.008844635,0.01964384,0.001912412,0.0007755071,0.04441053,0.00142495,0.5996483],"genre_scores_gemma":[0.7929579,0.07047507,0.01062032,0.00191409,0.001054413,0.000286904,0.02955089,0.0005350865,0.09260529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02026846,"threshold_uncertainty_score":0.06780481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0115343060752517,"score_gpt":0.2512412495068735,"score_spread":0.2397069434316218,"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."}}