{"id":"W1993085939","doi":"10.1007/s10723-007-9071-y","title":"Enterprise Grids: Challenges Ahead","year":2007,"lang":"en","type":"article","venue":"Journal of Grid Computing","topic":"Distributed and Parallel Computing Systems","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Leverage (statistics); Grid; Grid computing; Maturity (psychological); Data science; Architecture; Enterprise architecture; Data grid; Field (mathematics); Distributed computing; Artificial intelligence; Geology","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":[0.01321911,0.0008536458,0.001388616,0.001082134,0.003151017,0.01265332,0.003419591,0.01904196,0.02233203],"category_scores_gemma":[0.01112595,0.0005184465,0.0008585189,0.002402348,0.008338589,0.02855892,0.005450567,0.01149937,0.008892818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002633854,"about_ca_system_score_gemma":0.01388401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00464354,"about_ca_topic_score_gemma":0.01052247,"domain_scores_codex":[0.9966516,0.0009900352,0.0001831749,0.0004008771,0.001101738,0.0006726271],"domain_scores_gemma":[0.9799586,0.006599572,0.0007196275,0.0007936127,0.005042054,0.006886566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001816449,0.0002151295,0.00125009,0.0008232009,0.00004204184,0.0002446049,0.0005564815,0.001037045,0.0003542522,0.219268,0.6254439,0.1505836],"study_design_scores_gemma":[0.00007884525,0.00008977813,0.001541224,0.0009969627,0.00002918735,0.0003047437,0.006241645,0.001870729,0.0001613127,0.3186127,0.6700068,0.00006605194],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.002034281,0.0897988,0.003967686,0.8846767,0.008132297,0.00001125802,0.0001652267,0.0002032827,0.0110104],"genre_scores_gemma":[0.234836,0.3388059,0.03581164,0.2951276,0.05262916,0.0001865875,0.001461164,0.0003788946,0.04076307],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02233203,"threshold_uncertainty_score":0.0747081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02331173233730225,"score_gpt":0.2694279520317553,"score_spread":0.2461162196944531,"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."}}