{"id":"W2949865904","doi":"10.48550/arxiv.0911.0086","title":"Sorting under Partial Information (without the Ellipsoid Algorithm)","year":2009,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fédération Wallonie-Bruxelles; Fonds De La Recherche Scientifique - FNRS; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Mathematics; Logarithm; Combinatorics; Time complexity; Binary logarithm; Log-log plot; Sorting algorithm; Algorithm; Linear extension; Pairwise comparison; Upper and lower bounds; Sorting; Approximation algorithm; Ellipsoid method; Entropy (arrow of time); Discrete mathematics; Regular polygon; Convex optimization; Convex combination; Partially ordered set","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004109491,0.0002971098,0.0002501378,0.0001469492,0.0004162465,0.0004308412,0.002286671,0.0002453593,0.00001648694],"category_scores_gemma":[0.00002026482,0.0002436386,0.0001703375,0.0003811076,0.00007882396,0.001548157,0.002366983,0.0006978689,0.0001686315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001248774,"about_ca_system_score_gemma":0.0001934286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001518566,"about_ca_topic_score_gemma":0.000007495176,"domain_scores_codex":[0.9983777,0.0001509813,0.0003060812,0.0006016168,0.0001809805,0.0003825767],"domain_scores_gemma":[0.9977745,0.00006980856,0.0004400669,0.001431797,0.0001517173,0.0001320611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002517155,0.0001129443,0.0004197172,0.00004615007,0.00009972747,0.00006947469,0.0007307621,0.6111655,0.00001090379,0.2361611,0.002420321,0.1487383],"study_design_scores_gemma":[0.0002680314,0.00002806865,0.0006947715,0.00006823466,0.0000295831,0.000005480282,0.0000778359,0.9426442,0.00007003118,0.05104374,0.004748947,0.000321115],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007852627,0.00004581624,0.9889531,0.0002949786,0.0007243246,0.0002789495,0.0000151961,0.0002800318,0.001554942],"genre_scores_gemma":[0.9750482,0.0002038505,0.02312131,0.0007668031,0.0003246236,0.000001727608,0.00009351806,0.00001338864,0.0004265267],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9671956,"threshold_uncertainty_score":0.9935294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05492157628295052,"score_gpt":0.196491950106113,"score_spread":0.1415703738231625,"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."}}