{"id":"W4382632287","doi":"10.1371/journal.pone.0286680","title":"Generalized measurement error: Intrinsic and incidental measurement error","year":2023,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Observational error; Inference; Sample (material); Computer science; Errors-in-variables models; Statistics; Variable (mathematics); Algorithm; Random error; Mathematics; Measurement uncertainty; Error detection and correction; Artificial intelligence; Physics","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":[0.001149936,0.0001818874,0.0002516009,0.0001507778,0.0001609529,0.0001649298,0.0004992032,0.00006385709,0.0000149027],"category_scores_gemma":[0.0001229873,0.0001750174,0.00004446643,0.0004287974,0.00004304346,0.0002471485,0.0003633001,0.0001655637,0.000251601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001607921,"about_ca_system_score_gemma":0.0001176207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007466471,"about_ca_topic_score_gemma":0.00007847504,"domain_scores_codex":[0.9971294,0.0001014538,0.0002833813,0.0004788455,0.001625936,0.0003809535],"domain_scores_gemma":[0.9989144,0.00001583037,0.00007379299,0.0004765601,0.0003350862,0.0001843702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001542781,0.004554366,0.01125388,0.0008465938,0.001806665,0.0002598885,0.007390921,0.0003991267,0.7541881,0.0634501,0.01292135,0.1427747],"study_design_scores_gemma":[0.006168927,0.001202348,0.07415576,0.002132738,0.0004761446,0.00004771382,0.0003038208,0.6656749,0.2054841,0.0403358,0.001030965,0.002986782],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9312394,0.0008249875,0.062006,0.003691722,0.0002463505,0.0004356804,0.000004184829,0.0009073101,0.000644383],"genre_scores_gemma":[0.9862986,0.00008701649,0.01297361,0.0003733562,0.0000848933,0.00005598519,0.000002483453,0.00001514021,0.0001089295],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6652758,"threshold_uncertainty_score":0.7137002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2112177621666526,"score_gpt":0.2718132194389913,"score_spread":0.0605954572723387,"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."}}