{"id":"W2067326981","doi":"10.5555/545381.545481","title":"Incremental) priority algorithms","year":2002,"lang":"en","type":"article","venue":"Symposium on Discrete Algorithms","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Greedy algorithm; Computer science; Simplicity; Greedy randomized adaptive search procedure; Mathematical optimization; Algorithm; Scheduling (production processes); Limit (mathematics); Approximation algorithm; Theoretical computer science; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004463471,0.0003428609,0.0002909083,0.0002336546,0.0003724384,0.0004524019,0.001378147,0.000132617,0.0004291617],"category_scores_gemma":[0.00002981671,0.0003071682,0.0001588015,0.0007427223,0.000113614,0.0009005999,0.0004383372,0.0003653613,0.001173763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001431449,"about_ca_system_score_gemma":0.0000343029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004773168,"about_ca_topic_score_gemma":0.000003503183,"domain_scores_codex":[0.9968771,0.0001877977,0.0004352275,0.0008535386,0.0009198871,0.0007264537],"domain_scores_gemma":[0.9982026,0.00008300958,0.00012892,0.001076689,0.0001280084,0.0003808221],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006150902,0.003162021,0.00111733,0.0002044387,0.0005158584,0.0005529934,0.01124149,0.01054904,0.01351559,0.2159218,0.06389142,0.6792665],"study_design_scores_gemma":[0.001357668,0.0006127447,0.0003741068,0.00004569947,0.00001096408,0.00003563457,0.00005040589,0.9553661,0.003477945,0.001213042,0.03674391,0.0007117513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00148193,0.0005233068,0.8211851,0.02070309,0.002250874,0.001493169,0.00005944268,0.001663774,0.1506394],"genre_scores_gemma":[0.4071974,0.002730316,0.552513,0.008768098,0.001365534,0.0003985479,0.00009320662,0.0002145313,0.02671934],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9448171,"threshold_uncertainty_score":0.9999381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02435609350982374,"score_gpt":0.2632225026411814,"score_spread":0.2388664091313576,"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."}}