{"id":"W2005161541","doi":"10.1016/j.bdr.2014.08.001","title":"Special Issue on Scalable Computing for Big Data","year":2014,"lang":"en","type":"article","venue":"Big Data Research","topic":"Cloud Computing and Resource Management","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Big data; Computer science; Scalability; Data science; Distributed computing; Data mining; Database","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":["open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.008392628,0.0001656869,0.0002206579,0.00031762,0.0007722924,0.0007932737,0.01205129,0.00007097676,0.00001071587],"category_scores_gemma":[0.001187153,0.0001436047,0.00003091847,0.0008400392,0.0001237017,0.0000716056,0.01918906,0.000396602,0.0006012522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005419643,"about_ca_system_score_gemma":0.0001048389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002258259,"about_ca_topic_score_gemma":0.00007242339,"domain_scores_codex":[0.9957447,0.0003971921,0.000294042,0.001481277,0.001171968,0.0009108004],"domain_scores_gemma":[0.9910287,0.001292883,0.00006818271,0.007262051,0.00016237,0.0001857619],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009860468,0.00007586511,0.00003081196,0.00003332608,0.00001109857,0.000004058209,0.00006368228,0.0001624828,0.000007318794,0.001458671,0.4211022,0.5770407],"study_design_scores_gemma":[0.0003057255,0.0001119117,0.0001709758,0.00004438815,0.000002103718,0.000001632043,0.00002681849,0.4241656,0.00003504746,0.0002087031,0.5748211,0.0001060016],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"editorial","genre_scores_codex":[0.01034275,0.0002141048,0.8365394,0.02189006,0.01762965,0.002231386,0.0005147207,0.000682365,0.1099556],"genre_scores_gemma":[0.3820136,0.00007219491,0.09265386,0.002897288,0.4824162,0.00004899901,0.002952557,0.0002038668,0.03674148],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7438855,"threshold_uncertainty_score":0.993294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3942306666903458,"score_gpt":0.4020958052569457,"score_spread":0.007865138566599927,"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."}}