{"id":"W1899193873","doi":"10.1109/tvcg.2015.2467331","title":"JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Austrian Science Fund","keywords":"Computer science; Tree traversal; Data structure; Overhead (engineering); Leverage (statistics); Sparse matrix; Parallel computing; Data set; Set (abstract data type); External Data Representation; Volume (thermodynamics); Theoretical computer science; Algorithm; Artificial intelligence","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"],"consensus_categories":[],"category_scores_codex":[0.0005253503,0.0003742908,0.0003933646,0.0009871445,0.0002273278,0.0005897576,0.001555095,0.000222189,0.00001882072],"category_scores_gemma":[0.000005406861,0.0003877403,0.00007678821,0.002028426,0.0001199892,0.0009902054,0.00007737752,0.000327107,0.00001500515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004328387,"about_ca_system_score_gemma":0.0001236825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004880225,"about_ca_topic_score_gemma":0.00007906421,"domain_scores_codex":[0.9971452,0.0002781754,0.0005799787,0.0009747699,0.0006557318,0.0003661513],"domain_scores_gemma":[0.9978362,0.00008330244,0.0001702411,0.001311632,0.000284419,0.0003141478],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005180612,0.0008658907,0.001470348,0.00005922297,0.00008812889,0.00003187448,0.002037857,0.0007533062,0.00002257124,0.974539,0.008305403,0.01177459],"study_design_scores_gemma":[0.001027684,0.0003044729,0.0006695036,0.00006732598,0.0000198192,0.00003394111,0.00001509546,0.9886874,0.000368135,0.003172826,0.005194694,0.0004391172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01109164,0.00006583086,0.986878,0.0001010148,0.0007335035,0.0003888767,0.00006503767,0.000644368,0.00003171945],"genre_scores_gemma":[0.9903021,0.0002166901,0.006874699,0.002171575,0.0001233435,0.00001983565,0.0001366005,0.00004837371,0.0001068073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9879341,"threshold_uncertainty_score":0.9998574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0548191303935291,"score_gpt":0.3112992609359386,"score_spread":0.2564801305424095,"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."}}