{"id":"W4320024084","doi":"10.1109/bigdata55660.2022.10020739","title":"The Dimensional Analysis of Data Flow Programs That Include Multidimensional and User-Defined Functions","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Big Data (Big Data)","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"University of Victoria","keywords":"Computer science; Dimension (graph theory); Curse of dimensionality; Multidimensional analysis; Data mining; Flow (mathematics); Rank (graph theory); Theoretical computer science; Data flow diagram; Raw data; Artificial intelligence; Database; Programming language; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":["sts","open_science","insufficient_payload"],"consensus_categories":["open_science"],"category_scores_codex":[0.009355021,0.0002719839,0.0004221731,0.0008680695,0.0013382,0.0009508677,0.01477543,0.00004387726,0.001048849],"category_scores_gemma":[0.002336142,0.0001967127,0.00008127001,0.002049841,0.000400673,0.001131832,0.03453492,0.0004263976,0.00008796015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006848728,"about_ca_system_score_gemma":0.0003167267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005612059,"about_ca_topic_score_gemma":0.002894643,"domain_scores_codex":[0.9901613,0.0005362791,0.0009935346,0.002593312,0.005350038,0.000365512],"domain_scores_gemma":[0.9856824,0.002322393,0.000685914,0.01069602,0.000448034,0.0001652038],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000267434,0.0005229602,0.004274483,0.00000314002,0.00116424,0.00002059857,0.00007242993,0.002280628,0.0002234827,0.004515918,0.4314239,0.5552308],"study_design_scores_gemma":[0.0003465817,0.00005014424,0.005646142,0.00001092626,0.0001881141,0.000007175557,0.0006474024,0.5892569,0.000005081983,0.0003610839,0.4033126,0.000167926],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.192576,0.0006116975,0.1140181,0.03004499,0.05211749,0.001997547,0.604129,0.0003037322,0.004201384],"genre_scores_gemma":[0.802406,0.00009249244,0.003430693,0.0004849265,0.0003747916,0.00004104285,0.1888428,0.00002025518,0.00430704],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.60983,"threshold_uncertainty_score":0.9999619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6122670306410279,"score_gpt":0.4310872843677545,"score_spread":0.1811797462732734,"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."}}