{"id":"W4402909386","doi":"10.1093/mnras/stae2243","title":"Improved weak lensing photometric redshift calibration via StratLearn and hierarchical modelling","year":2024,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Optical Systems and Laser Technology","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Deutsches Zentrum für Luft- und Raumfahrt; Horizon 2020 Framework Programme; Deutsche Forschungsgemeinschaft; Engineering and Physical Sciences Research Council; Alliance de recherche numérique du Canada; Science and Technology Facilities Council; Natural Sciences and Engineering Research Council of Canada; European Commission; Bundesministerium für Wirtschaft und Klimaschutz","keywords":"Physics; Photometric redshift; Weak gravitational lensing; Redshift; Gravitational lens; Astrophysics; Calibration; Strong gravitational lensing; Red shift; Astronomy; Galaxy","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":[0.003531198,0.0006367781,0.0007050353,0.001145671,0.0005864009,0.001241476,0.002209932,0.0006856747,0.002183568],"category_scores_gemma":[0.01126975,0.0005804015,0.0007983571,0.0009423743,0.0008679458,0.00158523,0.001883628,0.001223801,0.0006381028],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002179692,"about_ca_system_score_gemma":0.001968944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04008352,"about_ca_topic_score_gemma":0.05634252,"domain_scores_codex":[0.9989403,0.000538485,0.00003811416,0.0001872138,0.0002117374,0.00008418073],"domain_scores_gemma":[0.9962841,0.001888684,0.0005002037,0.0006660175,0.0004891877,0.0001717714],"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":[0.00007837791,0.00003518627,0.006850458,0.00002869813,0.00007294409,0.00004237593,0.000118324,0.9066456,0.001848098,0.03409637,0.001177666,0.04900596],"study_design_scores_gemma":[0.000003355837,0.00000345709,0.0003561208,0.000002883756,0.000003356592,0.000004173914,0.000003708909,0.9928081,0.0002419086,0.006372379,0.0001950029,0.000005507108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06077081,0.0001063624,0.9361717,0.0002300371,0.00001753139,0.00002807079,0.0002316026,0.0008059251,0.001637955],"genre_scores_gemma":[0.7527568,0.0001222451,0.2434689,0.0002078563,0.0000433216,0.00007100384,0.0008279579,0.0003386292,0.002163287],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04008352,"threshold_uncertainty_score":0.07970041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006977771673407674,"score_gpt":0.1868894751870924,"score_spread":0.1799117035136847,"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."}}