{"id":"W2155243512","doi":"10.1103/physrevd.87.044008","title":"Interpolation in waveform space: Enhancing the accuracy of gravitational waveform families using numerical relativity","year":2013,"lang":"en","type":"article","venue":"Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D, Particles, fields, gravitation, and cosmology","topic":"Pulsars and Gravitational Waves Research","field":"Physics and Astronomy","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; Perimeter Institute; University of Toronto","funders":"","keywords":"Waveform; Singular value decomposition; Interpolation (computer graphics); Algorithm; Gravitational wave; Basis (linear algebra); Theory of relativity; Computer science; Numerical relativity; Projection (relational algebra); Mathematics; Physics; Artificial intelligence; Theoretical physics; Motion (physics); Geometry; Telecommunications; Quantum mechanics","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":[0.002748822,0.00041991,0.0003639068,0.0009192948,0.000314716,0.0008135127,0.0009912697,0.0004972492,0.00100414],"category_scores_gemma":[0.008480518,0.0003023977,0.0004906649,0.0007788501,0.0004937574,0.001279369,0.0008670969,0.0008910702,0.0004952748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004981704,"about_ca_system_score_gemma":0.0006756558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002376962,"about_ca_topic_score_gemma":0.001988576,"domain_scores_codex":[0.9994616,0.0002284489,0.00003419181,0.00006136048,0.0001896022,0.00002484641],"domain_scores_gemma":[0.9979435,0.0009154141,0.0002193973,0.0005751707,0.0003032309,0.00004334829],"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":[0.0002420723,0.00009277502,0.005624132,0.0001323697,0.00005909277,0.0001436993,0.0003901298,0.6006573,0.03369078,0.07909244,0.0011092,0.2787659],"study_design_scores_gemma":[0.000005434315,0.00002256604,0.0004553465,0.000006665225,0.000003427102,0.00002891889,0.0000137864,0.9881253,0.003722213,0.006587212,0.001020502,0.000008546418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04964558,0.0001024141,0.9478203,0.00008032607,0.00003240654,0.00003526706,0.00004659356,0.000510246,0.001726882],"genre_scores_gemma":[0.3513397,0.0002630753,0.646637,0.0000372684,0.00003012773,0.00006122211,0.0002252142,0.0002534024,0.001152941],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002748822,"threshold_uncertainty_score":0.01453733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02256610638206633,"score_gpt":0.3596367529658639,"score_spread":0.3370706465837975,"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."}}