{"id":"W3099805203","doi":"10.36001/phmconf.2020.v12i1.1261","title":"Life prediction for aircraft structure based on Bayesian inference: towards a digital twin ecosystem","year":2020,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Department of National Defence; National Research Council Canada; Government of Canada; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Inference; Data mining; Software; Bayesian probability; Bayesian inference; Machine learning; Artificial intelligence","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":[0.00009734421,0.0002330363,0.0002690929,0.00001841964,0.00008183366,0.00007614101,0.0004530908,0.0001668366,0.00002513478],"category_scores_gemma":[0.0006754512,0.0001863192,0.0002391112,0.0001995434,0.00007610354,0.0002526095,0.00007635992,0.0002968518,0.000001372983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007689774,"about_ca_system_score_gemma":0.0002317274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000709692,"about_ca_topic_score_gemma":0.000002031244,"domain_scores_codex":[0.9989428,0.00002790018,0.0002823134,0.0002435785,0.0002692813,0.0002341631],"domain_scores_gemma":[0.9990467,0.000131321,0.00009721517,0.0002954174,0.0002797881,0.000149562],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001235618,0.0008738837,0.1492125,0.01813656,0.003531224,0.00001244866,0.1459601,0.08724159,0.1621582,0.07762739,0.3058133,0.04819718],"study_design_scores_gemma":[0.00157554,0.001131686,0.01155937,0.0007300992,0.0001362166,0.000004567676,0.002552437,0.909172,0.02605695,0.04477461,0.001364286,0.0009422141],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5598586,0.00002343928,0.4215873,0.003059761,0.0005036328,0.001600005,0.008137301,0.001983176,0.003246862],"genre_scores_gemma":[0.9756159,0.000002045004,0.02383383,0.0003062648,0.0001308186,0.00003546328,0.00003488884,0.00003727686,0.000003569451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8219304,"threshold_uncertainty_score":0.7597875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02153486751451185,"score_gpt":0.2388464766988626,"score_spread":0.2173116091843507,"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."}}