{"id":"W3020417722","doi":"10.1093/mnras/staa3293","title":"<tt>DAYENU:</tt> a simple filter of smooth foregrounds for intensity mapping power spectra","year":2020,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Radio Astronomy Observations and Technology","field":"Physics and Astronomy","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Science and Technology Facilities Council; Natural Sciences and Engineering Research Council of Canada; National Research Foundation; Gordon and Betty Moore Foundation; National Aeronautics and Space Administration; National Science Foundation","keywords":"Intensity mapping; Spectral density; Physics; Algorithm; Estimator; Weighting; Filter (signal processing); Fourier transform; Discrete Fourier transform (general); Intensity (physics); Bandlimiting; Matrix (chemical analysis); Inverse; Optics; Computer science; Fourier analysis; Fractional Fourier transform; Mathematics; Redshift; Acoustics; Geometry; Galaxy; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0001302512,0.0002164577,0.0004764535,0.00001446119,0.0001381729,0.00002621365,0.0005823025,0.0000865019,0.000164731],"category_scores_gemma":[0.00001615183,0.0001733936,0.0007680114,0.000129511,0.0002532137,0.0001011195,0.0002584811,0.0002520338,0.000003098899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004670681,"about_ca_system_score_gemma":0.00005340243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002631659,"about_ca_topic_score_gemma":0.000004781424,"domain_scores_codex":[0.9986947,0.00002201438,0.0004816226,0.0003244999,0.0001146366,0.0003625903],"domain_scores_gemma":[0.9989747,0.0001070791,0.0003850415,0.0003554063,0.00009024615,0.00008751824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003126879,0.0006326514,0.656005,0.0001358266,0.00139469,1.261168e-7,0.002000266,0.3095627,0.0008143116,0.007961004,0.01310307,0.008077685],"study_design_scores_gemma":[0.002088505,0.0004348737,0.3644615,0.00004627773,0.0002024256,1.125459e-8,0.00337018,0.6001145,0.007821134,0.001228844,0.0197628,0.0004689597],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9799296,0.0000203087,0.0167021,0.002070598,0.0001022474,0.0004171166,0.0002720712,0.00002882623,0.0004571042],"genre_scores_gemma":[0.9786897,1.59218e-8,0.02087666,0.00009100985,0.0001991784,0.00003082237,0.00003803677,0.00002312766,0.00005150088],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2915435,"threshold_uncertainty_score":0.7070788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01611728740767599,"score_gpt":0.2085743976411821,"score_spread":0.1924571102335061,"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."}}