{"id":"W4385328059","doi":"10.48550/arxiv.2307.14197","title":"DESI Mock Challenge: Constructing DESI galaxy catalogues based on FastPM simulations","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Division of Astronomical Sciences; Science and Technology Facilities Council; Department of Atomic Energy, Government of India; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; U.S. Department of Energy; Office of Science; National Science Foundation","keywords":"Physics; Galaxy; Covariance; Cluster analysis; Halo; Covariance matrix; Correlation function (quantum field theory); Astrophysics; Statistical physics; Algorithm; Computer science; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001577428,0.001306235,0.001054621,0.001470598,0.0006823952,0.001453627,0.003313909,0.001662921,0.009101725],"category_scores_gemma":[0.00811801,0.00113222,0.001534016,0.001612535,0.000574738,0.001363084,0.001494992,0.001603254,0.004743414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001704507,"about_ca_system_score_gemma":0.001492914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01639448,"about_ca_topic_score_gemma":0.01945509,"domain_scores_codex":[0.9994107,0.0001540286,0.00003257701,0.0001179662,0.0001939373,0.00009079694],"domain_scores_gemma":[0.997264,0.0008977613,0.0001310779,0.0008852832,0.0005342272,0.0002875135],"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":[0.0004478917,0.000243521,0.01336857,0.000287518,0.0003490374,0.0002566182,0.0003416618,0.8855568,0.002576344,0.01442419,0.05595129,0.0261965],"study_design_scores_gemma":[0.0001549396,0.00003418242,0.001982521,0.00001741049,0.00002025564,0.00002819831,0.00003788151,0.9792016,0.001653487,0.005739709,0.01109429,0.00003560543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4339137,0.0005812118,0.2836853,0.001547198,0.0006689726,0.001067638,0.1282873,0.108554,0.04169451],"genre_scores_gemma":[0.5235936,0.0003353778,0.2432857,0.000617461,0.0001595861,0.001580898,0.2105692,0.01355895,0.00629921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01639448,"threshold_uncertainty_score":0.03259814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.072915947501746,"score_gpt":0.1981909305321902,"score_spread":0.1252749830304442,"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."}}