{"id":"W2333398907","doi":"10.1142/9789812811684_0038","title":"INTERPRETING KEY COMPARISON DATA FOR THE MRA DATABASE","year":2001,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Key (lock); Database; Computer security","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":[0.008864083,0.0008758503,0.001182419,0.008618819,0.001228342,0.005044232,0.002416158,0.001505339,0.05198302],"category_scores_gemma":[0.0916842,0.0004682585,0.0009048281,0.007762655,0.0007081773,0.006332188,0.001941299,0.001344878,0.03428312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001531928,"about_ca_system_score_gemma":0.00310897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005676781,"about_ca_topic_score_gemma":0.005677074,"domain_scores_codex":[0.9899551,0.002010736,0.002202078,0.001349902,0.003854512,0.000627716],"domain_scores_gemma":[0.9283283,0.02407229,0.004249734,0.02142819,0.02070164,0.001219833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.008286072,0.0005140236,0.02663214,0.002185705,0.0003772905,0.0009425141,0.000542571,0.002985712,0.019449,0.01470812,0.6899959,0.2333809],"study_design_scores_gemma":[0.001626386,0.00153079,0.06149351,0.0008571204,0.0006390156,0.002966897,0.002604041,0.02496643,0.1001283,0.0338329,0.7689128,0.0004417838],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.1387141,0.004593426,0.04629264,0.00887449,0.00492513,0.001550257,0.6963193,0.02134895,0.07738164],"genre_scores_gemma":[0.3251827,0.001719061,0.06363627,0.001262643,0.0008316605,0.0008854238,0.5800114,0.005332423,0.0211384],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05198302,"threshold_uncertainty_score":0.1739005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1076461258314657,"score_gpt":0.3583728281429113,"score_spread":0.2507267023114456,"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."}}