{"id":"W4399527102","doi":"10.1109/tcpmt.2024.3410298","title":"TC-GVF: Tensor Core GPU-Based Vector Fitting via Accelerated Tall-Skinny QR Solvers","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Components Packaging and Manufacturing Technology","topic":"Tensor decomposition and applications","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computational science; Tensor (intrinsic definition); Parallel computing; Computer science; Core (optical fiber); Computer graphics (images); CUDA; Mathematics; Geometry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006492643,0.001029804,0.0007647066,0.0005788446,0.0004392647,0.0009678648,0.001389263,0.001046247,0.007843044],"category_scores_gemma":[0.002676686,0.0003655703,0.0006574813,0.0008281742,0.0005506507,0.001218474,0.001404333,0.001417627,0.002866146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005036374,"about_ca_system_score_gemma":0.001802998,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005283408,"about_ca_topic_score_gemma":0.008433999,"domain_scores_codex":[0.9996124,0.00009040395,0.00002070629,0.00005225118,0.000179281,0.0000449287],"domain_scores_gemma":[0.9993398,0.000198316,0.00006708129,0.0001220454,0.0002138958,0.00005897352],"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":[0.000220181,0.000123049,0.001683865,0.0004106543,0.0001204119,0.0004281865,0.0003847753,0.4960322,0.03156399,0.1025033,0.03035683,0.3361725],"study_design_scores_gemma":[0.00001569483,0.00001956004,0.00005144655,0.00001067694,0.000004272419,0.00006102651,0.00001797274,0.9866757,0.002720579,0.004941832,0.005470984,0.00001026485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00260411,0.0001431033,0.9935628,0.0000871828,0.00005257046,0.00003590116,0.00007459096,0.001259024,0.002180667],"genre_scores_gemma":[0.06706433,0.0002717575,0.927062,0.0001254634,0.00004733632,0.0001286587,0.0004664535,0.000857453,0.003976471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007843044,"threshold_uncertainty_score":0.02623761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05252913284032976,"score_gpt":0.3024098454241697,"score_spread":0.24988071258384,"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."}}