{"id":"W1777520031","doi":"10.48550/arxiv.1508.03211","title":"Computing accurate Horner form approximations to special functions in finite precision arithmetic","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Numerical Methods and Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Univariate; Implementation; Heuristic; Arithmetic; Computer science; Inverse trigonometric functions; Machine epsilon; Arbitrary-precision arithmetic; Algorithm; Elementary function; Round-off error; Algebra over a field; Mathematics; Artificial intelligence; Programming language; Pure mathematics; Machine learning; Multivariate statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002120013,0.0005574429,0.0009142743,0.0006867422,0.0004044946,0.001261699,0.0009756302,0.0008180938,0.002387486],"category_scores_gemma":[0.01090652,0.0003974684,0.000439105,0.0008324593,0.001553234,0.002176705,0.001177969,0.001312391,0.0007234225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006888004,"about_ca_system_score_gemma":0.000710034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00101497,"about_ca_topic_score_gemma":0.002206829,"domain_scores_codex":[0.9988583,0.0003736088,0.00006377213,0.0001353244,0.000437185,0.0001316513],"domain_scores_gemma":[0.9971907,0.001833992,0.0001733423,0.0005362732,0.0002109448,0.00005472236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005404622,0.0000996935,0.002319772,0.0002618383,0.00005478901,0.000237962,0.0005520221,0.4163884,0.01510127,0.4321864,0.002471914,0.1297855],"study_design_scores_gemma":[0.00002767399,0.00004837997,0.0001303034,0.00002392749,0.000009120902,0.00003539223,0.00004359585,0.803973,0.006864819,0.1876986,0.001133882,0.00001128945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08934402,0.00009774514,0.9053599,0.0001155515,0.00002673631,0.00002335383,0.00003430503,0.001017881,0.003980426],"genre_scores_gemma":[0.4933057,0.000158566,0.5032895,0.0001202899,0.00003553292,0.0000740062,0.0001384082,0.0002985035,0.002579441],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002387486,"threshold_uncertainty_score":0.01121187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1247453068416767,"score_gpt":0.2453228981624314,"score_spread":0.1205775913207547,"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."}}