{"id":"W2014643492","doi":"10.1007/s11590-009-0144-7","title":"An efficient string sorting algorithm for weighing matrices of small weight","year":2009,"lang":"en","type":"article","venue":"Optimization Letters","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"Wilfrid Laurier University","keywords":"Sorting; String (physics); Computational intelligence; Algorithm; Mathematics; Connection (principal bundle); Range (aeronautics); Combinatorics; Computer science; Artificial intelligence","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":[0.0002338052,0.0001147418,0.0001390588,0.0001384807,0.0001664348,0.0001417971,0.0005250122,0.0000384693,0.000004617693],"category_scores_gemma":[0.00001343317,0.0001063251,0.00004999296,0.0002743997,0.00001289018,0.0004979385,0.000059718,0.00005683701,7.826337e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002444888,"about_ca_system_score_gemma":0.00001724544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007452914,"about_ca_topic_score_gemma":1.143673e-7,"domain_scores_codex":[0.9989474,0.00003161823,0.0003008131,0.0003183604,0.0001847665,0.0002169808],"domain_scores_gemma":[0.9992163,0.00005310514,0.0002422713,0.0003448177,0.00007941986,0.00006410787],"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":[0.000002088753,0.00004683755,0.00001333409,0.000005787189,0.00000366063,0.000001348053,0.0001459146,0.8120863,0.001310747,0.001085165,0.000090569,0.1852082],"study_design_scores_gemma":[0.0002970364,0.00006203746,0.00006952175,0.00003486544,0.000006357813,0.000001730732,0.00001134181,0.9965792,0.00258557,0.0000517645,0.0001694091,0.000131171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003342717,0.00004986071,0.9953879,0.0006351386,0.0002452998,0.0001838485,0.000006563409,0.0001111335,0.0000375369],"genre_scores_gemma":[0.01546803,0.000009877189,0.9838041,0.0005447183,0.0001225149,0.000005515372,0.00003304438,0.000007834597,0.00000436928],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.185077,"threshold_uncertainty_score":0.4335814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009961612166301382,"score_gpt":0.2369741017125232,"score_spread":0.2270124895462218,"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."}}