{"id":"W1512734143","doi":"10.1108/rmj-01-2014-0009","title":"Meeting Big Data challenges with visual analytics","year":2014,"lang":"en","type":"article","venue":"Records Management Journal","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Data science; Big data; Context (archaeology); Visual analytics; Data management; Computer science; Exploratory research; Analytics; Visualization; Knowledge management; Data mining; Geography","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":[0.06674601,0.001211675,0.001105292,0.005911747,0.005151078,0.03165861,0.005567852,0.003339231,0.00761657],"category_scores_gemma":[0.1900069,0.00126168,0.001433336,0.006053403,0.01007286,0.02943532,0.01658922,0.006539203,0.002280498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00366189,"about_ca_system_score_gemma":0.007564039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003211922,"about_ca_topic_score_gemma":0.003126472,"domain_scores_codex":[0.9294177,0.04870571,0.003392159,0.003304173,0.01366751,0.00151287],"domain_scores_gemma":[0.7044734,0.2260966,0.01113323,0.02957743,0.02432461,0.004394788],"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":[0.0005240484,0.0004133159,0.01915125,0.005655444,0.0003419528,0.001010945,0.1517311,0.007231927,0.008051774,0.2855057,0.05420216,0.4661804],"study_design_scores_gemma":[0.0001144661,0.0002669272,0.004922203,0.004780199,0.0001101648,0.0015416,0.1184679,0.02366232,0.006364114,0.4857019,0.3537733,0.0002948949],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09573957,0.007264896,0.6800051,0.1440791,0.001902799,0.001246973,0.0008764235,0.004503212,0.06438194],"genre_scores_gemma":[0.528245,0.003681437,0.4510534,0.007381825,0.00152961,0.00117222,0.0007125352,0.001275307,0.004948626],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06674601,"threshold_uncertainty_score":0.3529909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07964229327395393,"score_gpt":0.3131988829620281,"score_spread":0.2335565896880741,"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."}}