{"id":"W4287373583","doi":"10.48550/arxiv.2101.08188","title":"Uncovering and Displaying the Coherent Groups of Rank Data by\\n Exploratory Riffle Shuffling","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sensory Analysis and Statistical Methods","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Shuffling; Riffle; Rank (graph theory); Set (abstract data type); Contingency table; Combinatorics; Mathematics; Seriation (archaeology); Computer science; Statistics; Artificial intelligence; Geography","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":[0.0005115563,0.0001794464,0.0003440947,0.0000112884,0.0002080775,0.00008908439,0.0006733307,0.0001311091,0.0001591899],"category_scores_gemma":[0.0001671728,0.00007692011,0.0001016174,0.0002693461,0.0001534637,0.0001485513,0.001438918,0.0003241332,0.000001369683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002255121,"about_ca_system_score_gemma":0.00001262276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000393927,"about_ca_topic_score_gemma":0.000378773,"domain_scores_codex":[0.9984542,0.0003790261,0.0002022893,0.000698292,0.00008665143,0.0001795459],"domain_scores_gemma":[0.9985126,0.0008188619,0.0001875972,0.0003184782,0.00007435313,0.00008809081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001153999,0.002654632,0.2330426,0.001743261,0.004642432,0.001273428,0.003549567,0.09930176,0.4007181,0.05364065,0.004415751,0.1938638],"study_design_scores_gemma":[0.001938905,0.0006532531,0.1238267,0.001607139,0.003816171,0.00001964489,0.04406708,0.7497547,0.008181039,0.05270523,0.009339279,0.0040909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909274,0.0005377106,0.007784455,0.00006742516,0.0001156114,0.0001116716,0.000247718,0.00001886562,0.0001891631],"genre_scores_gemma":[0.9987127,0.0006311171,0.0002409728,0.00004647768,0.00006159351,4.476415e-7,0.0002243439,0.000001385165,0.00008098675],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.650453,"threshold_uncertainty_score":0.3136711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.17727865154453,"score_gpt":0.226498334224315,"score_spread":0.04921968267978499,"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."}}