{"id":"W4362706828","doi":"10.48550/arxiv.2304.03094","title":"PopulAtion Parameter Averaging (PAPA)","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Samsung; Alliance de recherche numérique du Canada; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Generality; Generalization; Inference; Population; Computation; Artificial neural network; Computer science; Artificial intelligence; Machine learning; Mathematics; Statistics; Algorithm; Demography; Psychology","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":[0.003363287,0.002587268,0.002196549,0.001623915,0.0008730107,0.001409705,0.002812043,0.001635036,0.002859265],"category_scores_gemma":[0.009531066,0.0009038153,0.001489018,0.001381682,0.0008979993,0.003664614,0.00193666,0.00252928,0.001592899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006989863,"about_ca_system_score_gemma":0.001307409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003124174,"about_ca_topic_score_gemma":0.005264989,"domain_scores_codex":[0.998309,0.0005334893,0.000104038,0.0006137916,0.0003284727,0.0001111681],"domain_scores_gemma":[0.9959514,0.001797135,0.0002892807,0.001113908,0.0007295974,0.0001185967],"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":[0.0001705669,0.0001352885,0.00487148,0.0001682242,0.0008926673,0.0001535343,0.0001824333,0.5220963,0.006823746,0.01320166,0.01086584,0.4404383],"study_design_scores_gemma":[0.000018472,0.0001088832,0.0006153713,0.00001951482,0.0001244117,0.000138253,0.00002037679,0.9739247,0.003567575,0.01836869,0.003061607,0.00003216901],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01362695,0.0008081045,0.980475,0.0002023885,0.0001007115,0.0000707064,0.000147324,0.002715079,0.001853721],"genre_scores_gemma":[0.4797855,0.001003451,0.5094762,0.0006670209,0.0004984394,0.0005554946,0.001421535,0.0009781383,0.00561418],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003363287,"threshold_uncertainty_score":0.01778692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.164489168627728,"score_gpt":0.2526322831985267,"score_spread":0.08814311457079865,"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."}}