{"id":"W7070928581","doi":"","title":"A Posteriori Error Analysis of a Non-Standard Quantity of Interest","year":2019,"lang":"en","type":"article","venue":"UNM’s Digital Repository (University of New Mexico)","topic":"QR Code Applications and Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"A priori and a posteriori; Bounded function; Error analysis; Nonlinear system; Errors-in-variables models; Ode; Approximation error; Ordinary differential equation; Error detection and correction; Maximum a posteriori estimation","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":[0.00004879904,0.00009565124,0.0003692331,0.0003470444,0.00004190291,0.00003684931,0.001017847,0.00007753981,0.000009188887],"category_scores_gemma":[0.00001291631,0.000105691,0.0002382329,0.001068017,0.0001439428,0.0006729941,0.0004961928,0.00006792215,0.000006703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004273022,"about_ca_system_score_gemma":0.0001117827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003603889,"about_ca_topic_score_gemma":0.00009242121,"domain_scores_codex":[0.9992154,0.00001007375,0.000196487,0.0002747187,0.0001881913,0.0001151146],"domain_scores_gemma":[0.9986008,0.00004643923,0.0003153271,0.0008059347,0.000176717,0.00005481094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004755663,0.0009658841,0.6596575,0.000321115,0.00334619,0.00007359789,0.006154982,0.0007101814,0.1315789,0.1283255,0.001453978,0.06693661],"study_design_scores_gemma":[0.002773547,0.002911455,0.8423773,0.0003931936,0.001002394,0.00004050095,0.008542041,0.04082208,0.08959039,0.005010886,0.00545424,0.001081966],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9005849,0.00002969067,0.09391523,0.0001080046,0.00005356794,0.0001107341,0.00002457951,0.00005719961,0.005116129],"genre_scores_gemma":[0.9934493,0.0000049206,0.005605176,0.000004295406,0.000002509957,9.737546e-8,0.000003124107,0.000003210789,0.0009273798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1827198,"threshold_uncertainty_score":0.4309953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02477541217251374,"score_gpt":0.2313132995608394,"score_spread":0.2065378873883257,"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."}}