{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06230209,0.001457331,0.002473235,0.002416867,0.001370395,0.00579642,0.004120057,0.004091749,0.002462794],"category_scores_gemma":[0.2632744,0.001027826,0.002241589,0.003859539,0.01172866,0.01145409,0.008762063,0.005927837,0.0006262345],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002807891,"about_ca_system_score_gemma":0.002635137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001704039,"about_ca_topic_score_gemma":0.001318263,"domain_scores_codex":[0.9048487,0.05243247,0.007142019,0.01530729,0.01854879,0.001720634],"domain_scores_gemma":[0.7460956,0.1561278,0.02016192,0.06422598,0.01229935,0.001089409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005832237,0.00002868718,0.005338886,0.0003583376,0.0002153404,0.0001463404,0.0008036978,0.0153814,0.0005768032,0.9443303,0.00103358,0.03172823],"study_design_scores_gemma":[0.00002431152,0.00007853544,0.003148807,0.0003741382,0.00008531033,0.0003713201,0.000256508,0.03561779,0.001157849,0.9491054,0.009693973,0.0000860334],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007301938,0.0007887774,0.9861727,0.001396334,0.000239068,0.00009491022,0.0002035069,0.0001472295,0.003655497],"genre_scores_gemma":[0.5983174,0.001814852,0.392018,0.002024901,0.0008362536,0.001021281,0.0006716168,0.0002352867,0.003060423],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06230209,"threshold_uncertainty_score":0.3294889,"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."}}