{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002406071,0.0003204691,0.00050437,0.0005306954,0.0001152152,0.00005063532,0.0002757015,0.000379529,0.0001232402],"category_scores_gemma":[0.0003255601,0.0003876376,0.0003246434,0.0005195424,0.00006527651,0.00010353,0.0006757063,0.0007431614,0.000441021],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007128793,"about_ca_system_score_gemma":0.0001707424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001816483,"about_ca_topic_score_gemma":0.0001042217,"domain_scores_codex":[0.998124,0.00009247917,0.0002351954,0.001052793,0.0001379159,0.0003575942],"domain_scores_gemma":[0.9980695,0.00037809,0.0002058791,0.001016974,0.0001299978,0.0001995917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003329887,0.0003870556,0.56594,0.001445548,0.0006824886,0.003415794,0.0004730939,0.4050476,0.0001359168,0.00448443,0.01609823,0.001556874],"study_design_scores_gemma":[0.003069881,0.0001806767,0.5123158,0.00265372,0.001950217,0.00002220716,0.0001709796,0.4315879,0.0002939447,0.03571414,0.01055873,0.001481783],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9866147,0.0000296524,0.007546166,0.003233122,0.000971347,0.0005656747,0.00002315011,0.0007594143,0.0002567663],"genre_scores_gemma":[0.9925624,0.0001413691,0.0002920951,0.002313436,0.0002192508,0.000002025806,0.0002166922,0.00007517557,0.004177594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05362416,"threshold_uncertainty_score":0.9998575,"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."}}