{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006371515,0.00007916685,0.0001259562,0.0002008581,0.0002842848,0.000206185,0.0001513723,0.00004801486,0.0001728631],"category_scores_gemma":[0.00003310576,0.0000872461,0.00004729726,0.0002511023,0.0001147337,0.004563902,0.00004683681,0.00006571929,0.0002387336],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001976694,"about_ca_system_score_gemma":0.00007594812,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01187009,"about_ca_topic_score_gemma":0.001759432,"domain_scores_codex":[0.9988567,0.00004098649,0.0005651351,0.00005325724,0.0002715911,0.0002123063],"domain_scores_gemma":[0.9995229,0.00003010437,0.0002605888,0.00008104747,0.00007458743,0.00003075949],"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":[0.0000115739,0.00003447471,0.006303866,0.00004915461,0.00002297379,2.73956e-7,0.0117384,0.001169831,1.013887e-7,0.9060545,0.06279776,0.01181707],"study_design_scores_gemma":[0.0002291884,0.000007427155,0.03962464,0.00001391762,0.000007129808,1.118421e-7,0.02231584,0.00007526459,0.000005806594,0.001580962,0.936047,0.00009267789],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.08844568,0.0000332685,0.0006330647,0.004694,0.0005149835,0.0004173008,0.00001382207,0.00005151519,0.9051964],"genre_scores_gemma":[0.9949046,0.00007393282,0.0004665873,0.000664577,0.0002026499,0.00005478144,0.00009051697,0.000002465638,0.003539853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.906459,"threshold_uncertainty_score":0.99471,"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."}}