{"id":"W4415008689","doi":"10.1051/0004-6361/202554909","title":"KiDS-Legacy: Redshift distributions and their calibration","year":2025,"lang":"en","type":"article","venue":"Astronomy and Astrophysics","topic":"Astronomy and Astrophysical Research","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Particle Physics","funders":"Science and Technology Facilities Council; Max-Planck-Gesellschaft; Knut och Alice Wallenbergs Stiftelse; Ministerio de Ciencia e Innovación; Bundesministerium für Bildung und Forschung; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Commission; Austrian Science Fund; National Science Foundation; Imperial College London; Deutsche Forschungsgemeinschaft; UK Space Agency; UK Research and Innovation; University of Portsmouth; Alexander von Humboldt-Stiftung","keywords":"Redshift; Calibration; Photometric redshift; Bin; Redshift survey; Source counts; Range (aeronautics); Distribution (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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008463939,0.0003351603,0.0003393675,0.00008321879,0.0005370798,0.0003026855,0.0001851371,0.00004950925,0.00008267885],"category_scores_gemma":[0.000005672012,0.000298417,0.000120612,0.0002830967,0.0003361133,0.0006287144,0.0002802892,0.0003982213,0.00001173429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000246955,"about_ca_system_score_gemma":0.0001402248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008850858,"about_ca_topic_score_gemma":0.000001196773,"domain_scores_codex":[0.9985093,0.00007189144,0.0002879955,0.000496776,0.0001066288,0.0005274289],"domain_scores_gemma":[0.9992062,0.000118701,0.00008982437,0.0003087744,0.00006240547,0.0002140809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003139734,0.0001382982,0.01066022,0.000007395338,0.0001203709,3.224734e-7,0.00006983687,0.00002493806,0.001330906,0.1584245,0.0002247701,0.828967],"study_design_scores_gemma":[0.01064912,0.001515305,0.1606207,0.0007487016,0.000606837,0.000004598314,0.01872376,0.01519993,0.1781405,0.1567857,0.4530054,0.003999454],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.206726,0.0000568951,0.7900659,0.0005453732,0.00006816312,0.0002290589,0.0001668342,0.00004130662,0.002100455],"genre_scores_gemma":[0.9889548,0.000002944104,0.01001728,0.00002765744,0.000336807,0.00006289221,0.000224935,0.00001690677,0.0003557588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8249676,"threshold_uncertainty_score":0.9999468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006708072009373366,"score_gpt":0.2414975415570157,"score_spread":0.2347894695476424,"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."}}