{"id":"W4414974036","doi":"10.48550/arxiv.2510.05215","title":"QML-FAST -- A Fast Code for low-$\\ell$ Tomographic Maximum Likelihood Power Spectrum Estimation","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Office of Science; Ministry of Colleges and Universities; National Science Foundation; Government of Canada; Wisconsin Alumni Research Foundation; High Energy Physics; U.S. Department of Energy; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada","keywords":"Estimator; Minimum-variance unbiased estimator; Covariance; Quadratic equation; Spectral density; Minimax estimator; Covariance matrix; Set (abstract data type); Code (set theory); Efficient estimator","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":[0.001316529,0.001375042,0.0008378237,0.001080721,0.0006024342,0.001666151,0.003641624,0.00144078,0.04155521],"category_scores_gemma":[0.007190512,0.001078844,0.001020396,0.001129843,0.0007104462,0.001576476,0.002403882,0.002028156,0.01871537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008377479,"about_ca_system_score_gemma":0.00209072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005613434,"about_ca_topic_score_gemma":0.01113985,"domain_scores_codex":[0.9995418,0.00008876467,0.0000351874,0.0000736462,0.0002116574,0.00004909344],"domain_scores_gemma":[0.9985968,0.000626779,0.0001089307,0.0002378826,0.0003402999,0.00008925431],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005237368,0.0001902636,0.005792008,0.0009623151,0.0003240206,0.0007440586,0.0004456964,0.2480421,0.01462163,0.06106772,0.2100484,0.4572379],"study_design_scores_gemma":[0.0001374769,0.00002266289,0.0005553355,0.00005321897,0.00001372178,0.0002336736,0.00003831278,0.9432845,0.006689713,0.01906465,0.02985846,0.00004827368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001490675,0.00009284724,0.9649369,0.0001270185,0.00004786258,0.00006972159,0.001264688,0.02998332,0.001987121],"genre_scores_gemma":[0.03319348,0.0001292845,0.9456168,0.0002297979,0.00004883435,0.0003928608,0.004341821,0.01241672,0.003630416],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04155521,"threshold_uncertainty_score":0.1390161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02508419023757295,"score_gpt":0.3182487091055576,"score_spread":0.2931645188679846,"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."}}