{"id":"W2069440547","doi":"10.1109/mdso.2004.1270710","title":"Guest editors' introduction [Data-intensive computing]","year":2004,"lang":"en","type":"article","venue":"IEEE Distributed Systems Online","topic":"Big Data Technologies and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science","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":[0.001171757,0.000215152,0.000428807,0.0001532865,0.0002787099,0.0003397379,0.002410937,0.0002193708,0.00001505992],"category_scores_gemma":[0.003888811,0.0001630805,0.00007079203,0.001224814,0.0002014309,0.0003814711,0.0004448194,0.0003385656,0.0006844641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001390705,"about_ca_system_score_gemma":0.00008844852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004013454,"about_ca_topic_score_gemma":0.00007635288,"domain_scores_codex":[0.9966319,0.00007428609,0.0009827177,0.0009971744,0.0009488828,0.0003650892],"domain_scores_gemma":[0.9945778,0.0002609095,0.0004879519,0.002976523,0.001573544,0.0001232977],"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":[0.000008917933,0.0001730867,0.0001650856,0.000006678009,0.00002624682,0.000009288385,0.00002178984,0.003989321,0.0008296537,0.001774072,0.9908777,0.002118163],"study_design_scores_gemma":[0.0004798776,0.00006081091,0.0007293142,0.0000324478,0.00002435348,0.00008758222,0.003055369,0.006971734,0.0005456106,0.00136603,0.9863901,0.0002567775],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1358588,0.0004770136,0.6716802,0.05786684,0.06905731,0.001137966,0.06262784,0.001247824,0.00004619846],"genre_scores_gemma":[0.9483584,0.00001662801,0.0009941179,0.0001191816,0.03709457,0.00001338337,0.01329951,0.00001720604,0.00008700219],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8124996,"threshold_uncertainty_score":0.8797629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1892674631237383,"score_gpt":0.3888998280057676,"score_spread":0.1996323648820292,"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."}}