{"id":"W2552090159","doi":"10.1093/mnras/stw3033","title":"2dFLenS and KiDS: determining source redshift distributions with cross-correlations","year":2016,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"INAF-Osservatorio Astronomico di Padova; Natural Sciences and Engineering Research Council of Canada; European Commission; Chinese Academy of Agricultural Sciences; Science and Technology Facilities Council; University of Toronto; Australian Astronomical Optics-Macquarie; European Southern Observatory; Deutsche Forschungsgemeinschaft; Government of Ontario; Swinburne University of Technology; Compute Canada; Western Canada Research Grid; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Alexander von Humboldt-Stiftung","keywords":"Physics; Redshift; Astrophysics; Source counts; Red shift; Astronomy; Statistical physics; Galaxy","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004275106,0.0005355811,0.0005153549,0.002589117,0.0004046875,0.001133961,0.00187984,0.0005945658,0.001884897],"category_scores_gemma":[0.02235093,0.0005432526,0.0007610397,0.001927941,0.0006776723,0.001690688,0.001484287,0.0007887347,0.0008130717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006754182,"about_ca_system_score_gemma":0.001048797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01409181,"about_ca_topic_score_gemma":0.0129367,"domain_scores_codex":[0.9988015,0.000441962,0.00005926546,0.0002767062,0.000322736,0.00009776022],"domain_scores_gemma":[0.9943281,0.002764313,0.0008487107,0.00127142,0.0006195773,0.0001678971],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001615789,0.0001230122,0.2739693,0.0001453745,0.0004475931,0.0002902054,0.0004242965,0.3773405,0.004989249,0.07229727,0.008390329,0.2614214],"study_design_scores_gemma":[0.0000189464,0.00002326478,0.0275839,0.00001891756,0.00001867381,0.0002056374,0.00004171936,0.9413968,0.00332403,0.02262764,0.004698467,0.00004207425],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06314169,0.0001399907,0.9330004,0.00009519466,0.00001386786,0.0000252239,0.0007569424,0.001512366,0.001314295],"genre_scores_gemma":[0.5923524,0.0001649739,0.4012319,0.0001061836,0.00004443534,0.0001144787,0.003585733,0.0007467233,0.001653241],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01409181,"threshold_uncertainty_score":0.02801961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004332876304725211,"score_gpt":0.1865950138265788,"score_spread":0.1822621375218536,"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."}}