{"id":"W4411411471","doi":"10.1145/3744970.3727292","title":"Learning-Augmented Competitive Algorithms for Spatiotemporal Online Allocation with Deadline Constraints","year":2025,"lang":"en","type":"article","venue":"ACM SIGMETRICS Performance Evaluation Review","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Universitas Brawijaya","keywords":"Computer science; Online algorithm; Workload; Scheduling (production processes); Competitive analysis; Robustness (evolution); Metric (unit); Mathematical optimization; Algorithm; Upper and lower bounds","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002777666,0.001920004,0.003007883,0.001147654,0.0009840773,0.002235382,0.003809142,0.002534474,0.004212484],"category_scores_gemma":[0.01157189,0.0007239742,0.0008668276,0.002591181,0.001479052,0.002969502,0.002064544,0.002672444,0.0008442341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002157428,"about_ca_system_score_gemma":0.003565271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009854293,"about_ca_topic_score_gemma":0.007732479,"domain_scores_codex":[0.9981295,0.000698924,0.0001051161,0.0004189133,0.0003692711,0.000278334],"domain_scores_gemma":[0.992313,0.005733394,0.0005340672,0.0004233938,0.0006471334,0.0003489415],"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":[0.0001646174,0.0002339366,0.0006343384,0.0001778566,0.00006733751,0.00004889176,0.00006479232,0.8981055,0.0004213996,0.0273981,0.00419946,0.06848379],"study_design_scores_gemma":[0.00002226831,0.00003691369,0.00004361704,0.000006053787,0.00000500115,0.0000113008,0.000009606417,0.9880089,0.0001070458,0.01114917,0.0005958162,0.000004441663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01479065,0.001026446,0.9775639,0.000654895,0.000142707,0.0001445694,0.0001658265,0.0005415907,0.004969391],"genre_scores_gemma":[0.5263304,0.001006491,0.4641733,0.0007558277,0.0004148065,0.0006099186,0.0007937094,0.000289129,0.0056266],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009854293,"threshold_uncertainty_score":0.01959383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07449504690266098,"score_gpt":0.3745211049290577,"score_spread":0.3000260580263967,"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."}}