{"id":"W3101528348","doi":"10.1115/1.4048787","title":"Toward a Big Data-Based Approach: A Review on Degradation Models for Prognosis of Critical Infrastructure","year":2020,"lang":"en","type":"review","venue":"Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Reliability (semiconductor); Big data; Computer science; Degradation (telecommunications); Data science; Reliability engineering; Risk analysis (engineering); Physics of failure; The Internet; Engineering; Power (physics); Data mining; Telecommunications; World Wide Web; Business","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":["metaresearch","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001603743,0.0004477323,0.002055183,0.0003581158,0.00002561028,0.00004839004,0.0004103871,0.0003084413,0.000001137183],"category_scores_gemma":[0.01394471,0.000374963,0.000274136,0.0004356134,0.00008000496,0.0002208617,0.00004558466,0.0004328614,1.412125e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001491737,"about_ca_system_score_gemma":0.0004812101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001062989,"about_ca_topic_score_gemma":6.878643e-8,"domain_scores_codex":[0.9966384,0.0001604193,0.00193089,0.0003104944,0.000753538,0.0002062838],"domain_scores_gemma":[0.9935507,0.002990181,0.001094228,0.0003659358,0.001858548,0.0001403806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001339436,0.00007180683,0.000002799656,0.2020643,0.0002809031,0.00000168068,0.00004365508,0.6313143,9.784476e-7,0.001256511,0.0003979948,0.1645517],"study_design_scores_gemma":[0.0004405331,0.0004749556,0.000005218369,0.07375316,0.003870945,0.00004856114,0.00002515114,0.9060993,0.000006789268,0.0002660956,0.01468603,0.0003233132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000004737359,0.6327109,0.3638816,0.00001825336,0.000744519,0.002039751,0.0005714825,0.00001547129,0.00001325432],"genre_scores_gemma":[0.004739119,0.9511873,0.04300525,0.000005393125,0.000347179,0.0002383417,0.00039494,0.00008228824,2.542969e-7],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.3208764,"threshold_uncertainty_score":0.9998702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1643738043010991,"score_gpt":0.3374112604044402,"score_spread":0.1730374561033412,"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."}}