{"id":"W4399636642","doi":"10.32614/cran.package.r5r","title":"r5r: Rapid Realistic Routing with 'R5'","year":2020,"lang":"en","type":"dataset","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Routing (electronic design automation); Computer network","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":[0.0001359854,0.0002977631,0.0003339694,0.00007327479,0.0001524358,0.0003784219,0.002079798,0.0001387832,0.0001449587],"category_scores_gemma":[0.00004862111,0.0001998397,0.00005018355,0.0003119529,0.00003467828,0.0003299743,0.001315,0.0004027491,0.0006142338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002819077,"about_ca_system_score_gemma":0.0001543954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005867995,"about_ca_topic_score_gemma":0.00003055322,"domain_scores_codex":[0.9981498,0.00005922088,0.0002582953,0.0007521327,0.0004723515,0.0003082647],"domain_scores_gemma":[0.9980556,0.00009799146,0.0001864681,0.001416364,0.00005497717,0.0001885966],"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":[0.000004596538,0.00001747571,3.276566e-7,0.00003225205,0.00001530709,0.00009459368,0.00001151465,0.000006816159,6.227073e-7,0.0009375076,0.9930632,0.005815802],"study_design_scores_gemma":[0.0001800648,0.0001305775,0.000006079586,0.0001036329,0.00001877573,0.00003491567,0.000004847121,0.01272309,0.000006378182,0.00008611233,0.9863845,0.0003210694],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[4.397327e-8,0.00004076535,0.3017221,0.0002483037,0.0002562953,0.0001039422,0.6967419,0.0001524303,0.0007342671],"genre_scores_gemma":[0.000004831469,0.00008247053,0.04497423,0.001061454,0.0003151678,0.000009902988,0.9534116,0.00001092982,0.0001293442],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2567478,"threshold_uncertainty_score":0.8149227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01800830907805613,"score_gpt":0.2364991988832204,"score_spread":0.2184908898051643,"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."}}