{"id":"W2966597656","doi":"10.1137/19m127879x","title":"A Privacy-Preserving Method to Optimize Distributed Resource Allocation","year":2020,"lang":"en","type":"article","venue":"SIAM Journal on Optimization","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; HEC Montréal","funders":"","keywords":"Mathematical optimization; Operator (biology); Computer science; Resource allocation; Convergence (economics); Dimension (graph theory); Computation; Mathematics; Algorithm","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":[0.00187692,0.001040595,0.0009545693,0.0005078812,0.0006684666,0.001166917,0.001398168,0.001494338,0.002795474],"category_scores_gemma":[0.003846985,0.0005876374,0.0008797492,0.0008304505,0.001588093,0.001252692,0.001986693,0.002370919,0.0004922689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001315459,"about_ca_system_score_gemma":0.001918674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002323398,"about_ca_topic_score_gemma":0.001968209,"domain_scores_codex":[0.999001,0.0004003853,0.0000301191,0.0002013861,0.0002653297,0.0001017633],"domain_scores_gemma":[0.9990207,0.0006072046,0.0000798466,0.0001315089,0.0001014311,0.00005933457],"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":[0.00007798749,0.00004297225,0.0001691928,0.00006457275,0.00002972232,0.00005907236,0.00008021752,0.9077606,0.002232094,0.06361841,0.00137117,0.02449405],"study_design_scores_gemma":[0.00001478896,0.00002408396,0.00002452897,0.000005213929,0.000004001434,0.00001327888,0.000008795355,0.9726854,0.0005261735,0.02572032,0.0009692022,0.000004233489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002414652,0.00005144895,0.9960828,0.0001330872,0.00001724371,0.00003022636,0.00002497303,0.00007666404,0.001168945],"genre_scores_gemma":[0.3168765,0.0002165654,0.6763552,0.0002178893,0.00006527087,0.0004338403,0.0001511642,0.0001505027,0.005532997],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002795474,"threshold_uncertainty_score":0.0099262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02199265047430246,"score_gpt":0.2669085174709549,"score_spread":0.2449158669966524,"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."}}