{"id":"W109627659","doi":"","title":"Improved Efficiency of Spring Embedders: Taking Advantage of GPU Programming","year":2007,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Computation; Parallel computing; General-purpose computing on graphics processing units; Visualization; Context (archaeology); Computational science; CUDA; Software; Graph; Computer engineering; Computer architecture; Computer graphics (images); Graphics; Theoretical computer science; Programming language; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005155111,0.0009461318,0.0007096941,0.0005638599,0.0007514997,0.001230091,0.001941331,0.000961032,0.01523316],"category_scores_gemma":[0.00306822,0.0004699807,0.0004213469,0.0009116026,0.0004162254,0.002392114,0.001175957,0.0009896873,0.002463744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004218181,"about_ca_system_score_gemma":0.0006431267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002053257,"about_ca_topic_score_gemma":0.004668924,"domain_scores_codex":[0.999658,0.0000711433,0.00001883809,0.0000626953,0.0001192276,0.00007017003],"domain_scores_gemma":[0.9979795,0.0009246468,0.00007370388,0.0005641092,0.0003028408,0.0001550973],"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":[0.002838018,0.0006701326,0.004211275,0.0005630541,0.000125555,0.0005726039,0.001118437,0.09124707,0.2593699,0.02678267,0.03042645,0.5820748],"study_design_scores_gemma":[0.0002861926,0.0002672533,0.00117969,0.00003769851,0.00005680781,0.0002122768,0.0002093698,0.8246483,0.1427982,0.01193392,0.0183161,0.00005423245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3303587,0.0006739355,0.6168439,0.001047368,0.0006377719,0.00008603715,0.0002522037,0.02504278,0.02505716],"genre_scores_gemma":[0.6498041,0.000251595,0.3255686,0.0001688778,0.00009134771,0.00007027905,0.0003071528,0.004470571,0.0192676],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01523316,"threshold_uncertainty_score":0.05096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01908123060834975,"score_gpt":0.2836258117395772,"score_spread":0.2645445811312274,"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."}}