{"id":"W2784624502","doi":"10.1103/physrevd.97.024031","title":"Eccentric, nonspinning, inspiral, Gaussian-process merger approximant for the detection and characterization of eccentric binary black hole mergers","year":2018,"lang":"en","type":"article","venue":"Physical review. D/Physical review. D.","topic":"Pulsars and Gravitational Waves Research","field":"Physics and Astronomy","cited_by":153,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto","funders":"H2020 Marie Skłodowska-Curie Actions; Science and Technology Facilities Council; National Centre for Supercomputing Applications; Seventh Framework Programme; Canadian Institute for Advanced Research; Horizon 2020 Framework Programme; University of Illinois at Urbana-Champaign; Canada Research Chairs; Natural Sciences and Engineering Research Council of Canada; European Commission; Canadian Institute for Theoretical Astrophysics; National Science Foundation","keywords":"Eccentric; Binary number; Process (computing); Characterization (materials science); Gaussian process; Astrophysics; Physics; Gaussian; Mathematics; Computer science; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004211126,0.0003870781,0.0009127556,0.00007523824,0.0003035654,0.00004630141,0.0004031782,0.00003134617,0.00005700486],"category_scores_gemma":[0.0001594751,0.0002573383,0.000508083,0.001117746,0.0003984687,0.0003217432,0.0001278431,0.0003054796,0.00008826614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000322557,"about_ca_system_score_gemma":0.00007979548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001110411,"about_ca_topic_score_gemma":3.12738e-7,"domain_scores_codex":[0.9974748,0.0001859185,0.0006229897,0.0005955311,0.0005993209,0.0005214559],"domain_scores_gemma":[0.9979703,0.0003415339,0.0005134766,0.0004694316,0.0004967669,0.0002084627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002409705,0.003221278,0.002523931,0.01749561,0.0007527266,0.000002543608,0.0005405676,0.00006105262,0.7050825,0.03554127,0.003604999,0.2309326],"study_design_scores_gemma":[0.004474163,0.003961501,0.06139597,0.02407,0.005076875,0.00000363656,0.0002264689,0.3012648,0.1883901,0.1163395,0.2907124,0.004084508],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9768428,0.01399532,0.001437407,0.002465582,0.0001916398,0.003852598,0.0004381918,0.00002750007,0.0007489885],"genre_scores_gemma":[0.9783266,0.01890776,0.00004798095,0.0005998666,0.001162673,0.000442304,0.0003004824,0.00005009476,0.0001622699],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5166924,"threshold_uncertainty_score":0.9999879,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01786315230118522,"score_gpt":0.4263784768081765,"score_spread":0.4085153245069912,"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."}}