{"id":"W3215903943","doi":"10.3847/2515-5172/ac3dfe","title":"Updates to LUCI: A New Fitting Paradigm Using Mixture Density Networks","year":2021,"lang":"en","type":"article","venue":"Research Notes of the AAS","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre for Research in Astrophysics of Québec","funders":"","keywords":"Computer science; Algorithm; Inference; Point (geometry); Computation; Convolutional neural network; Code (set theory); Bayesian inference; Pipeline (software); Simple (philosophy); Gaussian; Bayesian probability; Machine learning; Artificial intelligence; 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":[],"consensus_categories":[],"category_scores_codex":[0.0007512182,0.0001078373,0.0001788435,0.00007541787,0.0003224445,0.0003204618,0.001624689,0.00007388241,0.0000208562],"category_scores_gemma":[0.001183975,0.00007808847,0.00008630678,0.001576982,0.00007525252,0.0002330635,0.001784031,0.0004136399,0.0000166937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004641205,"about_ca_system_score_gemma":0.0005931309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003726065,"about_ca_topic_score_gemma":0.00009754915,"domain_scores_codex":[0.9980604,0.0002221838,0.0002004706,0.0003870518,0.0005911559,0.0005387089],"domain_scores_gemma":[0.9979556,0.0004935262,0.00006804639,0.0009697436,0.0003131153,0.0001999801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001498016,0.0006349533,0.08417121,0.0007865804,0.0003628789,0.0005432601,0.007617344,0.05234952,0.1065348,0.3771808,0.03409611,0.3355727],"study_design_scores_gemma":[0.0005916752,0.0001966191,0.03766137,0.001409307,0.00002521365,0.0002094686,0.0001418627,0.4141881,0.3254522,0.2151188,0.00429391,0.0007113539],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1389518,0.0009486045,0.8376501,0.02171005,0.0002116903,0.0002053305,0.000001793586,0.00004119227,0.0002794187],"genre_scores_gemma":[0.9357208,0.00002812461,0.06357442,0.0003212247,0.000153232,0.000001828313,5.746456e-7,0.000008257573,0.0001915709],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.796769,"threshold_uncertainty_score":0.3184355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.100764085158658,"score_gpt":0.3684162451952743,"score_spread":0.2676521600366164,"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."}}