{"id":"W3105347508","doi":"10.48550/arxiv.1812.05995","title":"Core Cosmology Library: Precision Cosmological Predictions for LSST","year":2018,"lang":"en","type":"article","venue":"Edinburgh Research Explorer (University of Edinburgh)","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto; University of British Columbia","funders":"Institut National de Physique Nucléaire et de Physique des Particules; Office of Science; Centre National de la Recherche Scientifique; Science and Technology Facilities Council; European Commission; Royal Astronomical Society; U.S. Department of Energy; National Science Foundation","keywords":"Physics; Cosmology; Large Synoptic Survey Telescope; Weak gravitational lensing; Redshift; Photometric redshift; Galaxy; Astrophysics; Halo; Python (programming language); COSMIC cancer database; Computer science","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":[0.002636328,0.001615971,0.001170196,0.001746811,0.0008606772,0.002026922,0.004773498,0.001487624,0.03582539],"category_scores_gemma":[0.01335338,0.001224059,0.001589914,0.001883854,0.0005022862,0.002914708,0.002493743,0.002432663,0.02244863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001401941,"about_ca_system_score_gemma":0.002586192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01319852,"about_ca_topic_score_gemma":0.009676708,"domain_scores_codex":[0.9990649,0.0002311097,0.00004417437,0.0001218408,0.0004286186,0.0001094102],"domain_scores_gemma":[0.9969472,0.0009128586,0.0002289966,0.0009297355,0.0007955009,0.0001856451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002987031,0.000180442,0.01504578,0.0009260536,0.0004127275,0.0003380543,0.0005095202,0.3281379,0.004079111,0.1495571,0.3899085,0.110606],"study_design_scores_gemma":[0.0001591492,0.00004075534,0.002645974,0.0001527267,0.00005037995,0.0001344487,0.00003862283,0.7935525,0.006329508,0.06195884,0.1348136,0.0001235601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0177178,0.0006951716,0.6771581,0.0006786902,0.0003120736,0.0002900721,0.05925069,0.188026,0.05587154],"genre_scores_gemma":[0.2035998,0.001002582,0.5777749,0.0008628443,0.0003702661,0.001522616,0.104095,0.09695285,0.01381916],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03582539,"threshold_uncertainty_score":0.1198479,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1001879897510491,"score_gpt":0.307178009922901,"score_spread":0.2069900201718519,"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."}}