{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001197907,0.00007621697,0.00007970463,0.00002695365,0.0001764478,0.0002593194,0.0005104294,0.00003251696,0.000005814227],"category_scores_gemma":[0.000005911243,0.00006175486,0.0000345496,0.0001255344,0.00001019091,0.0007977024,0.00006546226,0.00004716192,0.0000206718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004071074,"about_ca_system_score_gemma":0.0001923034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001614719,"about_ca_topic_score_gemma":0.000003582398,"domain_scores_codex":[0.9993082,0.000006434882,0.0001271427,0.0002529442,0.0001304698,0.0001747972],"domain_scores_gemma":[0.9993999,0.00003710199,0.00003863452,0.0003902598,0.00008031749,0.0000537571],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001772318,0.00002137173,0.000002115738,0.000005213471,0.00000296098,7.023464e-7,0.0001272748,0.003022835,0.004518297,0.8996569,0.0006893472,0.09195119],"study_design_scores_gemma":[0.0002739823,0.00007121903,0.00009605179,0.00001505991,0.000003225899,0.000006654789,0.0002131927,0.7463063,0.001300888,0.2339336,0.01758347,0.0001963683],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004036806,0.0001124831,0.992762,0.0006261177,0.00004152533,0.0002304478,0.000002130703,0.0002145286,0.005607124],"genre_scores_gemma":[0.139118,0.000008118262,0.8600901,0.0004704233,0.00008706278,0.00004793956,0.000006151139,0.00000256578,0.0001696084],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7432835,"threshold_uncertainty_score":0.251829,"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."}}