{"id":"W2982518953","doi":"10.48550/arxiv.1910.13650","title":"Bundle methods for dual atomic pursuit","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Bundle; Duality (order theory); Dual (grammatical number); Set (abstract data type); Sequence (biology); Gauge (firearms); Duality gap; Mathematics; Optimization problem; Algorithm; Mathematical optimization; Computer science; Combinatorics; Chemistry; Materials science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002012597,0.0003229124,0.0004066178,0.0001997908,0.00005633567,0.0000592278,0.0005237713,0.0004007592,0.00003286158],"category_scores_gemma":[0.00002061803,0.0004026002,0.0002753396,0.0001431307,0.00004932593,0.00008714088,0.0004903002,0.0004412946,0.00005450542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001932466,"about_ca_system_score_gemma":0.00005308049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003806796,"about_ca_topic_score_gemma":0.00000682073,"domain_scores_codex":[0.9988634,0.00006575657,0.0001551958,0.0005478911,0.00003827399,0.0003294663],"domain_scores_gemma":[0.9987733,0.000164003,0.00008009405,0.0008047707,0.00009366892,0.00008410506],"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":[0.00007559642,0.00006840764,0.0002933555,0.0002881241,0.0005175535,0.000047913,0.0001171512,0.948149,0.003937442,0.03031257,0.01247064,0.003722228],"study_design_scores_gemma":[0.0003931011,0.00005516467,0.00009848498,0.0001410701,0.0001975889,0.000004966473,0.00003594292,0.9082112,0.01491854,0.05770601,0.01759266,0.0006453008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.173258,0.0002412317,0.8190732,0.00001197617,0.00110402,0.0005274527,0.00003301828,0.001152339,0.004598773],"genre_scores_gemma":[0.9754349,0.0002222509,0.02257769,0.00003497573,0.0001363874,0.000002795708,0.00003971911,0.00007852224,0.001472758],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.802177,"threshold_uncertainty_score":0.9998426,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09574137947842264,"score_gpt":0.2375950047311853,"score_spread":0.1418536252527627,"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."}}