{"id":"W2128986460","doi":"10.1109/icgrid.2006.311020","title":"Metascheduling Multiple Resource Types using the MMKP","year":2006,"lang":"en","type":"article","venue":"","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Particle Physics; University of Victoria","funders":"National Science Council; University of Victoria","keywords":"Computer science; Grid; Knapsack problem; Resource allocation; Grid computing; Task (project management); Quality of service; Resource (disambiguation); Resource management (computing); Function (biology); Service (business); Distributed computing; Shared resource; Operations research; Computer network; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002073429,0.0005485446,0.0009276037,0.0005665174,0.0005754429,0.001336227,0.001428712,0.000616163,0.001379642],"category_scores_gemma":[0.002721321,0.0004641934,0.0006866332,0.0008512569,0.0005497817,0.001568707,0.00138465,0.0008676589,0.0002352672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00109139,"about_ca_system_score_gemma":0.001313337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002232863,"about_ca_topic_score_gemma":0.003097171,"domain_scores_codex":[0.9989768,0.0003700864,0.0000712061,0.0002063642,0.0002295297,0.0001460664],"domain_scores_gemma":[0.9990501,0.000379558,0.0001263607,0.000246517,0.0001089974,0.00008851463],"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":[0.0001066366,0.00008635261,0.0009139104,0.00007303763,0.00005459701,0.00006754613,0.00009017834,0.9013993,0.004917393,0.02943119,0.0006757451,0.06218414],"study_design_scores_gemma":[0.00001630147,0.0000536037,0.0001892718,0.000006280521,0.00001236001,0.00003429201,0.00002966144,0.9802491,0.002261845,0.0152379,0.001897398,0.00001202222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04969739,0.0002364199,0.9454105,0.0001549165,0.00003758639,0.0001033929,0.00005211083,0.0003587519,0.003948875],"genre_scores_gemma":[0.6021939,0.000191599,0.3940628,0.00007052333,0.00002755956,0.0002412179,0.0001143085,0.0001081072,0.002989908],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002232863,"threshold_uncertainty_score":0.01096541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02297719655948348,"score_gpt":0.2368782711377892,"score_spread":0.2139010745783057,"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."}}