{"id":"W2706146898","doi":"10.1299/jsmecmd.2014.27.202","title":"Information visualization to analyze large scale scientific data","year":2014,"lang":"en","type":"article","venue":"Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Atomic Energy (Canada)","funders":"","keywords":"Information visualization; Data science; Scale (ratio); Visualization; Computer science; Data visualization; Information retrieval; Data mining; Geography; Cartography","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":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.006864052,0.00171215,0.002026642,0.002670153,0.002386287,0.006366493,0.01182154,0.0006926359,0.0004971293],"category_scores_gemma":[0.001965133,0.001694403,0.0005698519,0.006918605,0.0005214381,0.01281125,0.005821748,0.0009347381,0.005020895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000490631,"about_ca_system_score_gemma":0.000762072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002662932,"about_ca_topic_score_gemma":0.0007534891,"domain_scores_codex":[0.9855289,0.001178497,0.00335371,0.003318736,0.003596063,0.003024121],"domain_scores_gemma":[0.9842843,0.0004711906,0.001495679,0.01016102,0.001479577,0.002108218],"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":[0.0001437206,0.002413795,0.02544766,0.000642008,0.0007021126,0.00004499694,0.01822395,0.003169302,0.001669019,0.394316,0.5153108,0.03791664],"study_design_scores_gemma":[0.002086486,0.0003651977,0.01003758,0.0003003067,0.0002464564,0.0000519522,0.001071773,0.3313128,0.0009731247,0.002236454,0.6489955,0.002322328],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05507096,0.0002315909,0.9148903,0.004212948,0.004369471,0.002455947,0.001829284,0.002949041,0.01399045],"genre_scores_gemma":[0.910915,0.0001552583,0.04178257,0.01823534,0.001684041,0.0002288005,0.0199153,0.0003926493,0.006691076],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8731077,"threshold_uncertainty_score":0.9995625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01988514948266043,"score_gpt":0.295913087365233,"score_spread":0.2760279378825726,"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."}}