{"id":"W2558876588","doi":"10.4043/27386-ms","title":"Accelerating Numerical Ice Engineering Tools Using GPGPU","year":2016,"lang":"en","type":"article","venue":"Arctic Technology Conference","topic":"Scientific Research and Discoveries","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Centre For Cold Ocean Resources Engineering","funders":"","keywords":"General-purpose computing on graphics processing units; Computer science; CUDA; Graphics processing unit; Speedup; Parallel computing; Monte Carlo method; Computational science; Graphics","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":[0.0003712001,0.0006416084,0.0004580198,0.000535063,0.0004079743,0.0009272997,0.001007815,0.0005489832,0.004764719],"category_scores_gemma":[0.001620563,0.000258832,0.0004249593,0.000874711,0.0003507713,0.0005986118,0.00072708,0.0008178923,0.001630342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004407905,"about_ca_system_score_gemma":0.001019391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007658043,"about_ca_topic_score_gemma":0.004176652,"domain_scores_codex":[0.9996896,0.0000708144,0.00001475902,0.00004123081,0.0001414643,0.0000421073],"domain_scores_gemma":[0.9993032,0.0002557259,0.00004752516,0.0001077415,0.0002467715,0.00003898301],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003111833,0.0001662481,0.004755219,0.0004614099,0.0001226512,0.0003275817,0.0002421386,0.8051344,0.02276404,0.01435686,0.01906781,0.1322905],"study_design_scores_gemma":[0.00003769373,0.00004263717,0.0005314877,0.00001768659,0.00001121656,0.0000389143,0.00002001689,0.9801676,0.007704304,0.002067541,0.009349203,0.00001160085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1678183,0.001327279,0.7614464,0.0005771407,0.000379639,0.0003055962,0.0009757318,0.02298765,0.04418239],"genre_scores_gemma":[0.5702709,0.0006223465,0.4189036,0.00016527,0.00004047869,0.0003962365,0.001251985,0.001406447,0.006942727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007658043,"threshold_uncertainty_score":0.01593953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.052078690188208,"score_gpt":0.2854266959708051,"score_spread":0.2333480057825971,"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."}}