{"id":"W4399991298","doi":"10.48550/arxiv.2406.15351","title":"Impact & Mitigation of Polarized Extragalactic Foregrounds on Bayesian Cosmic Microwave Background Lensing","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Radio Astronomy Observations and Technology","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"SLAC National Accelerator Laboratory; Office of Science; Alliance de recherche numérique du Canada; European Commission; National Energy Research Scientific Computing Center; Lawrence Berkeley National Laboratory; Innovation, Science and Economic Development Canada; University of Toronto; U.S. Department of Energy","keywords":"Cosmic microwave background; COSMIC cancer database; Physics; Astronomy; Microwave; Weak gravitational lensing; Astrophysics; Optics; Galaxy; Redshift; Anisotropy","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":[0.00515489,0.001040028,0.000811226,0.0006695547,0.0006010781,0.001436302,0.001950947,0.001475944,0.00238931],"category_scores_gemma":[0.02048284,0.0006141584,0.0008217456,0.0006395351,0.001536571,0.002265652,0.002363878,0.001258483,0.000555184],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0011499,"about_ca_system_score_gemma":0.001576119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01122775,"about_ca_topic_score_gemma":0.01144876,"domain_scores_codex":[0.9979038,0.001214863,0.00005550607,0.0002357288,0.0004456022,0.000144403],"domain_scores_gemma":[0.9945415,0.003389163,0.0005215021,0.0008217965,0.000462793,0.000263364],"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":[0.0004211554,0.00008759549,0.01374116,0.0001972523,0.0003164997,0.0001425323,0.0001648438,0.7441554,0.009301756,0.1120392,0.002529405,0.1169033],"study_design_scores_gemma":[0.00003260363,0.00003549115,0.001984293,0.00002426601,0.00002989453,0.00007010836,0.00001729805,0.9687844,0.001702232,0.02567214,0.001618968,0.00002830095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05319001,0.000634424,0.9395726,0.0008865445,0.00005509887,0.00003164116,0.0001772687,0.0006732843,0.004779122],"genre_scores_gemma":[0.6268026,0.000650908,0.3678124,0.0004606399,0.0001406841,0.00007369898,0.0003962991,0.0005966045,0.003066138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01122775,"threshold_uncertainty_score":0.02726203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05379206150993389,"score_gpt":0.2222083548156248,"score_spread":0.1684162933056909,"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."}}