{"id":"W4403884711","doi":"10.48550/arxiv.2410.03632","title":"BLAST: Beyond Limber Angular power Spectra Toolkit. A fast and efficient algorithm for 3x2 pt analysis","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Ministry of Colleges and Universities; Innovation, Science and Economic Development Canada; Institut Périmètre de physique théorique; Alliance de recherche numérique du Canada; Government of Canada; U.S. Department of Energy","keywords":"Algorithm; Spectral line; Power analysis; Computer science; Physics; Quantum mechanics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002413069,0.0003877626,0.000390037,0.0003135381,0.0001098786,0.0001084749,0.0003764422,0.0004552985,0.00005682907],"category_scores_gemma":[0.00004347737,0.0004140005,0.0004805042,0.0003998121,0.0001460117,0.000003785991,0.001312674,0.0004589387,0.00002425673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006067322,"about_ca_system_score_gemma":0.000104583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002818042,"about_ca_topic_score_gemma":0.00002902284,"domain_scores_codex":[0.9983622,0.00005256418,0.0002453542,0.0008943875,0.0000876007,0.0003578725],"domain_scores_gemma":[0.9987623,0.0000307404,0.0001826442,0.0007358033,0.0001327279,0.0001557773],"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":[0.0002869893,0.0004217569,0.004018298,0.0009605334,0.009369344,0.0002669838,0.001187374,0.9591641,0.002769781,0.01166038,0.003812983,0.006081431],"study_design_scores_gemma":[0.0005999116,0.0002684259,0.0007303515,0.00004724957,0.001991942,0.00001263958,0.000259734,0.9861125,0.001257774,0.001372385,0.006618688,0.0007283336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6309177,0.0004226085,0.3632159,0.00006311171,0.0003231014,0.0004779933,0.0003570651,0.00006040798,0.004162122],"genre_scores_gemma":[0.9847507,0.0001327852,0.00918682,0.0001102427,0.0001603503,0.000003389252,0.0005469833,0.00004787858,0.005060837],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3540291,"threshold_uncertainty_score":0.9998312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213282087426916,"score_gpt":0.1886571273214563,"score_spread":0.1765243064471871,"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."}}