{"id":"W4386148332","doi":"10.1093/mnras/stad2477","title":"<scp>astrophot</scp>: fitting everything everywhere all at once in astronomical images","year":2023,"lang":"en","type":"article","venue":"Monthly Notices of the Royal Astronomical Society","topic":"Galaxies: Formation, Evolution, Phenomena","field":"Physics and Astronomy","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Mila - Quebec Artificial Intelligence Institute; Université de Montréal; Centre for Research in Astrophysics of Québec","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec; Canadian Institute for Theoretical Astrophysics","keywords":"Python (programming language); Computer science; Graphics processing unit; Markov chain Monte Carlo; Physics; CUDA; Bayesian optimization; Computational science; Covariance; Dither; Sky; Computer graphics (images); Algorithm; Artificial intelligence; Bayesian probability; Computer vision; Astronomy; Parallel computing; Programming language","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":[0.0009851481,0.001034014,0.0007831133,0.0007731591,0.0008293624,0.001672187,0.002262588,0.001002745,0.07394025],"category_scores_gemma":[0.003302598,0.0008900274,0.00129483,0.001417211,0.0006165639,0.00197126,0.002242629,0.002103095,0.03399271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008044668,"about_ca_system_score_gemma":0.001910391,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01113602,"about_ca_topic_score_gemma":0.0179903,"domain_scores_codex":[0.9994324,0.00004858883,0.00001926573,0.0001062287,0.0003238828,0.00006959039],"domain_scores_gemma":[0.9992418,0.0001596482,0.00006585452,0.0002137837,0.0002149197,0.0001039916],"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":[0.0001237475,0.00007059412,0.003015682,0.0004382246,0.0001510307,0.0002652738,0.0002255498,0.06023744,0.008868641,0.01432733,0.7899746,0.1223019],"study_design_scores_gemma":[0.0001552455,0.00004599889,0.004907763,0.0001111196,0.00003373018,0.0003847428,0.00007611085,0.553009,0.0203569,0.02620922,0.3945751,0.0001351034],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01060652,0.0003753871,0.5502755,0.001680395,0.0006069727,0.0001881996,0.04232318,0.3440034,0.0499405],"genre_scores_gemma":[0.1110895,0.0004927009,0.6004837,0.001907719,0.0002766952,0.0005792105,0.06618053,0.1884624,0.03052753],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.07394025,"threshold_uncertainty_score":0.2473547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00818600159219767,"score_gpt":0.2053713928007752,"score_spread":0.1971853912085776,"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."}}