{"id":"W2161615476","doi":"10.1109/hpcc.2009.100","title":"Load Scheduling Strategies for Parallel DNA Sequencing Applications","year":2009,"lang":"en","type":"article","venue":"","topic":"Algorithms and Data Compression","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Scheduling (production processes); Computation; Parallel computing; Load distribution; Distributed computing; Load balancing (electrical power); Algorithm; Mathematical optimization; Mathematics","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":[0.0005538036,0.000552849,0.0003855454,0.0006273707,0.0007179381,0.0005721764,0.0006893431,0.0003859295,0.001698308],"category_scores_gemma":[0.00169058,0.0002315018,0.0001831768,0.0006226265,0.0003817696,0.001091592,0.0004745936,0.0002965714,0.000367483],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007830914,"about_ca_system_score_gemma":0.0005540787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001631177,"about_ca_topic_score_gemma":0.001682125,"domain_scores_codex":[0.9997309,0.00008276369,0.000014677,0.00003979112,0.00009304439,0.00003879145],"domain_scores_gemma":[0.9994672,0.0002824526,0.00007140137,0.00004404523,0.00009493007,0.00003998747],"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":[0.0003987202,0.000233455,0.0008071093,0.0001587195,0.00002906753,0.000203052,0.0002540501,0.7117212,0.03525551,0.03076749,0.001955206,0.2182164],"study_design_scores_gemma":[0.0000218386,0.00007377315,0.000142059,0.000005236338,0.00000715911,0.00002994835,0.00004292166,0.9847098,0.004200883,0.008603682,0.002155161,0.000007505903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1405816,0.0007944531,0.8509403,0.0002745836,0.00009939119,0.0001388995,0.00004043564,0.0005064058,0.006623881],"genre_scores_gemma":[0.8221758,0.0005990741,0.1726088,0.00008986337,0.0001027242,0.0001282908,0.00008023261,0.0001239918,0.004091321],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001698308,"threshold_uncertainty_score":0.005681694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03164412740282505,"score_gpt":0.2877861372188106,"score_spread":0.2561420098159856,"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."}}