{"id":"W4213446582","doi":"10.21203/rs.3.rs-1389516/v1","title":"Extracting high-order cosmological information in galaxy surveys with power spectra","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Ministry of Colleges and Universities; National Natural Science Foundation of China; Youth Innovation Promotion Association of the Chinese Academy of Sciences; Government of Canada; Youth Innovation Promotion Association; Japan Society for the Promotion of Science; Ministry of Education, Culture, Sports, Science and Technology; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada; National Astronomical Observatories, Chinese Academy of Sciences; University of Portsmouth","keywords":"Order (exchange); Galaxy; Physics; Power (physics); Astrophysics; Economics; Thermodynamics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001315963,0.0007954551,0.0005847089,0.002142892,0.0003346447,0.001401691,0.0007223757,0.0007357702,0.001607626],"category_scores_gemma":[0.01131813,0.0007593929,0.0006590701,0.002097061,0.0004194709,0.002191673,0.001068543,0.0006575765,0.00110577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002358966,"about_ca_system_score_gemma":0.0004565637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002306968,"about_ca_topic_score_gemma":0.003233982,"domain_scores_codex":[0.9994674,0.000213564,0.00002624304,0.00009159407,0.000137869,0.00006330592],"domain_scores_gemma":[0.9960334,0.002813991,0.0002937298,0.0005362827,0.0001708836,0.0001516834],"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":[0.0007103484,0.0003521258,0.1318059,0.000433803,0.0006732155,0.0007147635,0.0004416041,0.2242511,0.0643831,0.02640214,0.005468423,0.5443635],"study_design_scores_gemma":[0.00004112152,0.00007429363,0.07100418,0.00003324404,0.00008727954,0.0004383408,0.0001173469,0.8668148,0.01067458,0.04779132,0.002872478,0.0000509714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3796731,0.0007737318,0.6127309,0.0003267893,0.00005418578,0.00004017616,0.001839798,0.00202308,0.002538242],"genre_scores_gemma":[0.8223547,0.0008018485,0.1696764,0.00008563857,0.0001970932,0.00004677634,0.004047933,0.0002684422,0.00252129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002306968,"threshold_uncertainty_score":0.006959558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02827635060197434,"score_gpt":0.3145301481555204,"score_spread":0.286253797553546,"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."}}