{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":171,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":171,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"query_hash":"a082d3cc7bd4","filters":{"topic":"Engineering Diagnostics and Reliability"}},"results":[{"id":"W981603413","doi":"10.1007/s00521-015-1990-0","title":"Dynamic neural networks for gas turbine engine degradation prediction, health monitoring and prognosis","year":2015,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Artificial neural network; Computer science; Autoregressive model; Turbine; Gas turbines; Nonlinear autoregressive exogenous model; Artificial intelligence; Machine learning; Engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01307860652696934,"gpt":0.2519531064712056,"spread":0.2388744999442362,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001428034,0.0001166026,0.0001246405,0.00003887474,0.0001411948,0.00004595319,0.00004736756,0.00004526719,1.005657e-7],"category_scores_gemma":[0.00002516206,0.0001153852,0.00001931026,0.0001266174,0.00002218559,0.0000454087,0.00002257118,0.000132308,1.764685e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003858513,"about_ca_system_score_gemma":0.000006087559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009381207,"about_ca_topic_score_gemma":0.000001766654,"domain_scores_codex":[0.9993688,0.000008672559,0.0001948041,0.0001792109,0.00005927228,0.000189185],"domain_scores_gemma":[0.9995435,0.0001223815,0.00002924277,0.0001039418,0.00005720281,0.0001437252],"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":[0.000001581482,0.00001610962,0.007032504,0.0001047789,0.000009564975,7.653447e-8,0.00006084556,0.909022,0.00007102708,0.00009189156,0.0001517919,0.08343787],"study_design_scores_gemma":[0.0002025094,0.00005524749,0.01175707,0.00002182672,0.00001133394,0.000006768787,0.00002039941,0.9870125,0.00004807917,0.00008499982,0.0006878243,0.00009144103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6281113,0.002712242,0.3672411,0.0004388567,0.0003328309,0.0006462733,0.00002104034,0.000485934,0.00001036764],"genre_scores_gemma":[0.9931958,0.0001944764,0.006156256,0.00001309904,0.0002407082,0.0001144824,0.00005810948,0.00002243459,0.000004630664],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3650845,"threshold_uncertainty_score":0.4705273,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2083923231","doi":"10.1108/14714171211244541","title":"Significance ranking of parameters impacting construction labour productivity","year":2012,"lang":"en","type":"article","venue":"Construction Innovation","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Ranking (information retrieval); Productivity; Labour economics; Economics; Econometrics; Environmental science; Computer science; Information retrieval; Economic growth","retraction":null,"screen_n_in":null,"score":{"opus":0.01142623255572148,"gpt":0.2194336554899373,"spread":0.2080074229342159,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005032656,0.000126911,0.0001619323,0.0002223919,0.00006790731,0.00001598841,0.00004737283,0.00009232594,0.00001960972],"category_scores_gemma":[0.0004565272,0.0001367143,0.00003031595,0.0009752771,0.0001241708,0.000384305,0.000009915712,0.0002246584,0.000003489699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000962676,"about_ca_system_score_gemma":0.00001902926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001207109,"about_ca_topic_score_gemma":4.447264e-7,"domain_scores_codex":[0.9991035,0.00002720721,0.0003890932,0.0001231697,0.0001401176,0.0002168412],"domain_scores_gemma":[0.9993343,0.0001012013,0.0001327056,0.0001805989,0.0002185591,0.00003261763],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002258955,0.00008352646,0.2924555,0.0007790814,0.0001351822,3.122527e-7,0.0006234677,0.09357255,0.3290525,0.09442311,0.000112248,0.18874],"study_design_scores_gemma":[0.0007062072,0.0000551079,0.1458341,0.000205352,0.00005906915,0.0001007012,0.0006592488,0.01068472,0.8349832,0.003963585,0.002067468,0.0006811924],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9119788,0.00008918834,0.0843119,0.00002292746,0.002773389,0.0001746798,0.00001040447,0.0002209019,0.0004177961],"genre_scores_gemma":[0.9733004,0.00002110172,0.02641724,0.000004353195,0.0002062109,0.00001624705,0.00001125992,0.00001900512,0.00000418363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5059307,"threshold_uncertainty_score":0.5575047,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2235090753","doi":"10.1038/529156e","title":"Monitor safety of aged fuel pipelines","year":2016,"lang":"en","type":"letter","venue":"Nature","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Pipeline transport; Business; Forensic engineering; Environmental science; Engineering; Environmental engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.003207691768811582,"gpt":0.200815173905512,"spread":0.1976074821367004,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00009322816,0.000284089,0.0003689521,0.00009476874,0.000014054,0.000009379953,0.0002839215,0.005455506,0.0000625071],"category_scores_gemma":[0.0001609801,0.0002060114,0.0001591113,0.00009607283,0.00003560158,0.00002683763,0.0000312163,0.00621932,0.00002669076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005826419,"about_ca_system_score_gemma":0.00001345466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001992845,"about_ca_topic_score_gemma":0.000001063383,"domain_scores_codex":[0.9989876,0.0000138235,0.0002764169,0.0002006956,0.0002508563,0.000270664],"domain_scores_gemma":[0.9990479,0.0003134117,0.0000431646,0.0004670307,0.00009287812,0.00003564385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002335126,0.000003536604,0.00003946856,0.001147172,0.00006215518,0.00003249382,0.0000122498,0.000603985,0.0003471704,0.00001025817,0.9964628,0.001276354],"study_design_scores_gemma":[0.0001555781,0.000009373266,0.0009328685,0.000322517,0.00003408047,0.000002372494,5.488762e-7,0.000146537,0.0008365198,0.0001733146,0.9971159,0.0002703635],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.003518074,0.0945994,0.004945721,0.7947088,0.05759243,0.001993556,0.005928757,0.003372883,0.03334037],"genre_scores_gemma":[0.4361207,0.02588098,0.009714462,0.3704208,0.1362689,0.0001774703,0.002207458,0.001730882,0.01747829],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.4326027,"threshold_uncertainty_score":0.9960734,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2534770783","doi":"10.3303/cet1648040","title":"Developing a quantitative risk-based methodology for maintenance scheduling using Bayesian Network","year":2016,"lang":"en","type":"article","venue":"eCite Digital Repository (University of Tasmania)","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Bayesian network; Computer science; Bayesian probability; Scheduling (production processes); Artificial intelligence; Engineering; Operations management","retraction":null,"screen_n_in":null,"score":{"opus":0.03331541529141491,"gpt":0.2318608815976587,"spread":0.1985454663062438,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002022186,0.000153828,0.0002701504,0.00007068063,0.0001577246,0.00002498852,0.0001942254,0.0001082697,0.000002226219],"category_scores_gemma":[0.0003258831,0.0001532225,0.0001477675,0.0001556842,0.0001198278,0.0002577642,0.00004428486,0.00008879114,0.000002550186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001833207,"about_ca_system_score_gemma":0.00005660419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001949498,"about_ca_topic_score_gemma":0.00001288464,"domain_scores_codex":[0.9991537,0.00003496298,0.0001787285,0.0002356753,0.00009879586,0.000298119],"domain_scores_gemma":[0.9983658,0.001096991,0.000114203,0.0002056842,0.0001396824,0.00007768216],"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":[0.0001595823,0.0000400567,0.02009873,0.0003652611,0.0003057444,0.00004758355,0.0002444857,0.9559448,0.009946634,0.007503208,0.0002533816,0.005090527],"study_design_scores_gemma":[0.002122554,0.0003178598,0.01807678,0.001347602,0.000191716,0.00002335785,0.0006407432,0.9569209,0.007994805,0.005646403,0.005635832,0.001081439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3114471,0.00009528395,0.6875901,0.00002598508,0.0003411784,0.0001240331,0.00004482732,0.0001008164,0.0002306426],"genre_scores_gemma":[0.6562614,0.00002010034,0.3435958,0.000003617186,0.00004342565,5.475914e-7,0.000003169943,0.0000208685,0.0000510432],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3448143,"threshold_uncertainty_score":0.6248233,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1986413971","doi":"10.1134/s0040601510010039","title":"Diagnostics of steam turbine disks using the metal magnetic memory method","year":2010,"lang":"en","type":"article","venue":"Thermal Engineering","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Cytodiagnostics (Canada)","funders":"","keywords":"Steam turbine; Magnetic memory; Nuclear engineering; Engineering; Turbine; Mechanical engineering; Materials science; Composite material","retraction":null,"screen_n_in":null,"score":{"opus":0.005918966899016806,"gpt":0.215497025068098,"spread":0.2095780581690812,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004562401,0.0002859115,0.0003210288,0.00009408315,0.0000432702,0.00002107515,0.0003483889,0.0001430431,0.00008717756],"category_scores_gemma":[0.0004056204,0.000219505,0.0001323477,0.0002456257,0.00006072643,0.00007686298,0.00007765045,0.000666486,0.000006242177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002891044,"about_ca_system_score_gemma":0.00001658892,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002340413,"about_ca_topic_score_gemma":0.000005674469,"domain_scores_codex":[0.998805,0.0000184595,0.0003662868,0.0001731218,0.000217058,0.0004200362],"domain_scores_gemma":[0.99845,0.0008030224,0.00003584661,0.0005399312,0.00005364952,0.0001175803],"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":[0.000001773339,0.00001746005,0.00004898165,0.0001063449,0.00003914164,0.000004089406,0.0001158055,0.6871367,0.3098597,0.0003229849,0.00001031692,0.002336703],"study_design_scores_gemma":[0.0002319967,0.00003906535,0.003457292,0.00004809365,0.0001159278,0.00002387365,0.00002894458,0.8845487,0.1102703,0.0000296196,0.0008700158,0.0003361773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9605887,0.00175817,0.03467659,0.0000175848,0.001916996,0.0002246977,0.00002377299,0.0003394031,0.0004541012],"genre_scores_gemma":[0.9822412,0.0001033852,0.01720746,0.000006040819,0.0003084673,0.00001825853,0.000003752034,0.00009631041,0.00001515892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1995894,"threshold_uncertainty_score":0.8951155,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1510952876","doi":"10.1088/0957-0233/26/6/065604","title":"A framework with nonlinear system model and nonparametric noise for gas turbine degradation state estimation","year":2015,"lang":"en","type":"article","venue":"Measurement Science and Technology","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Life Prediction Technologies (Canada); Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Nonparametric statistics; Nonlinear system; Degradation (telecommunications); Gas turbines; Noise (video); Estimation; State (computer science); Turbine; Computer science; Environmental science; Control theory (sociology); Mathematics; Econometrics; Algorithm; Physics; Artificial intelligence; Engineering; Thermodynamics; Telecommunications; Mechanical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02258184954430753,"gpt":0.2276975479817538,"spread":0.2051156984374462,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009308715,0.00009433334,0.0001131557,0.000312482,0.00007116189,0.00003900124,0.00008846939,0.00006612328,6.219673e-8],"category_scores_gemma":[0.0008171733,0.00007392689,0.000005184887,0.0007704704,0.0001619269,0.000105884,0.00002086379,0.0000917064,8.300992e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001907531,"about_ca_system_score_gemma":0.00008030126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003647695,"about_ca_topic_score_gemma":0.00000515651,"domain_scores_codex":[0.9991205,0.000002236378,0.0001160592,0.0001894853,0.0003833223,0.0001883656],"domain_scores_gemma":[0.999185,0.00002366798,0.00002210489,0.000154039,0.0005387181,0.00007644693],"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":[0.0000180279,0.00004720677,0.002070292,0.0003863531,0.00001994182,0.000001716983,0.0001243123,0.9605275,0.005751178,0.005883589,0.0001013741,0.02506845],"study_design_scores_gemma":[0.0002639796,0.0001225242,0.0001379865,0.00006920159,0.00001391447,0.000005977081,0.00004167449,0.9925259,0.004886858,0.001763493,0.00006920059,0.00009928804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4761347,0.0003299629,0.5227087,0.0001895091,0.00007402318,0.0002804173,0.000005067865,0.0002367367,0.00004092624],"genre_scores_gemma":[0.9062042,0.00002810184,0.09369844,0.000004109294,0.000004880009,0.000051012,0.000001131985,0.000007020036,0.000001159108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4300694,"threshold_uncertainty_score":0.3014651,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1427644118","doi":"10.1115/gt2015-44101","title":"Health Monitoring and Degradation Prognostics in Gas Turbine Engines Using Dynamic Neural Networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Prognostics; Nonlinear autoregressive exogenous model; Akaike information criterion; Artificial neural network; Computer science; Recurrent neural network; Metric (unit); Degradation (telecommunications); Gas turbines; Condition monitoring; Turbine; Network architecture; Artificial intelligence; Data mining; Machine learning; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01669598623023009,"gpt":0.2492360988483747,"spread":0.2325401126181447,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001856007,0.0001309045,0.0001609331,0.00007990612,0.00001995399,0.00003081891,0.00004555295,0.0000616075,5.267795e-7],"category_scores_gemma":[0.00009637686,0.0001234901,0.00001365717,0.0001773084,0.00001394353,0.00008712617,0.00002279585,0.000167647,4.008016e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001363102,"about_ca_system_score_gemma":0.00001381834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008778708,"about_ca_topic_score_gemma":0.0000348104,"domain_scores_codex":[0.9993154,0.0000121423,0.0002099698,0.0001242876,0.00008686717,0.0002513348],"domain_scores_gemma":[0.9996363,0.00007725909,0.00001553488,0.0001061406,0.00003107298,0.0001337211],"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":[0.000001151977,0.00001040238,0.03747613,0.00005599201,0.000003964614,0.00000253913,0.00005528944,0.9570078,0.00004738464,0.00002445362,0.00001259339,0.005302317],"study_design_scores_gemma":[0.0001918402,0.00002710884,0.02268114,0.0000561993,0.000003874394,0.000007419184,0.00002530977,0.9767547,0.00005557558,0.00004614926,0.00002808321,0.0001225593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9404995,0.002905626,0.05526961,0.00008090896,0.0008239745,0.0001759896,0.000001456966,0.0002174961,0.00002548019],"genre_scores_gemma":[0.9886455,0.0003592325,0.01085099,0.000005086541,0.00009323467,0.000005747732,0.000008093247,0.00002692787,0.000005215915],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04814601,"threshold_uncertainty_score":0.503578,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2249658000","doi":"","title":"A Statistical Analysis and Model of the Residual Value of Different Types of Heavy Construction Equipment","year":2003,"lang":"en","type":"dissertation","venue":"VTechWorks (Virginia Tech)","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Residual; Statistics; Value (mathematics); Econometrics; Mathematics; Engineering; Computer science; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.003973096745061567,"gpt":0.2099554387318356,"spread":0.2059823419867741,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001457003,0.0002385579,0.0006147787,0.0002429874,0.00002334216,0.000007035669,0.0001523031,0.0003474541,0.00001917763],"category_scores_gemma":[0.0001311046,0.0001830719,0.0001413891,0.0003960727,0.0001227849,0.00001648962,0.00002763945,0.0003368907,1.877028e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004375789,"about_ca_system_score_gemma":0.00003599849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000294758,"about_ca_topic_score_gemma":0.00004060612,"domain_scores_codex":[0.9987528,0.00002852289,0.0005702322,0.0002013979,0.0002916352,0.0001553921],"domain_scores_gemma":[0.9991097,0.0001906534,0.0001711147,0.000394517,0.00009075912,0.00004320035],"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":[0.00007396329,0.0001877014,0.007024628,0.002677144,0.001720495,8.696092e-7,0.0003709206,0.9411753,0.01653109,0.02613512,0.0001845166,0.003918176],"study_design_scores_gemma":[0.0007567297,0.000217931,0.1076896,0.001557381,0.005109862,0.000003157825,0.0002722145,0.5703827,0.3026001,0.01045692,0.00004563783,0.0009078371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9575092,0.0006821202,0.04017685,0.000007293427,0.0003670252,0.0002866905,0.0003063088,0.00005196743,0.0006125658],"genre_scores_gemma":[0.9914088,0.0005748869,0.007789093,0.00000119376,0.00001040633,0.00001673826,0.00009839436,0.00003498971,0.00006550448],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3707927,"threshold_uncertainty_score":0.7465457,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4320003664","doi":"10.18280/jesa.550614","title":"Real Time Assessment of Novel Predictive Maintenance System based on Artificial Intelligence for Rotating Machines","year":2022,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Centre National pour la Recherche Scientifique et Technique","keywords":"Downtime; Predictive maintenance; Reliability (semiconductor); Process (computing); Artificial neural network; Reliability engineering; Computer science; Acceleration; Test bench; Engineering; Real-time computing; Artificial intelligence; Embedded system; Power (physics)","retraction":null,"screen_n_in":null,"score":{"opus":0.0146367401805743,"gpt":0.2563380830911107,"spread":0.2417013429105364,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00101333,0.0002186331,0.0003740771,0.0001816466,0.0002884761,0.00006264202,0.0002834354,0.00004321605,0.00003476112],"category_scores_gemma":[0.0002489455,0.0001967455,0.0001670754,0.0002519451,0.00004744188,0.00007090127,0.00005263111,0.0003782459,0.000002340862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005512584,"about_ca_system_score_gemma":0.00008747846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001070027,"about_ca_topic_score_gemma":8.99003e-7,"domain_scores_codex":[0.9981865,0.00009521189,0.0007634777,0.0001914904,0.0004464845,0.0003168572],"domain_scores_gemma":[0.9986007,0.0006712666,0.000238615,0.0002212164,0.0001666607,0.0001016081],"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":[0.00002237782,0.0001183803,0.0002562332,0.0006433499,0.00006360377,0.00001564799,0.0001264013,0.9706538,0.002694209,0.002595989,0.0001610158,0.02264896],"study_design_scores_gemma":[0.000192089,0.00051171,0.02190124,0.0004675644,0.0000394754,0.00007196391,0.00008469557,0.9759781,0.0002451527,0.0002904481,0.00003604707,0.0001815023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1839619,0.00006606391,0.8113474,0.00004089021,0.00125561,0.0006237156,0.0004028015,0.0004626835,0.001839005],"genre_scores_gemma":[0.9267397,0.0000117488,0.07288886,0.000006327584,0.0001484121,0.00009852472,0.0000106709,0.00006771134,0.00002803325],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7427778,"threshold_uncertainty_score":0.8023049,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4395666702","doi":"10.18280/mmep.110414","title":"Comparative Analysis of SVM and ANN for Machine Condition Monitoring and Fault Diagnosis in Gearboxes","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Support vector machine; Fault (geology); Computer science; Machine learning; Artificial intelligence; Engineering; Seismology; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.02170628069246851,"gpt":0.2550249273597862,"spread":0.2333186466673176,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002157187,0.000165803,0.0004061812,0.0002762618,0.00001952557,0.00005449677,0.00003241999,0.0000814798,0.000002178271],"category_scores_gemma":[0.00003122554,0.0001504524,0.00004507709,0.0002624021,0.00003180356,0.00007112365,0.00001780103,0.000144443,2.161299e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001790617,"about_ca_system_score_gemma":0.000002269494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001165282,"about_ca_topic_score_gemma":0.000001449879,"domain_scores_codex":[0.9992633,0.000004516516,0.0002910878,0.0001913494,0.00007450945,0.0001752072],"domain_scores_gemma":[0.999163,0.0006548936,0.00001159466,0.00008219907,0.00001888178,0.00006939728],"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":[0.000001289324,0.0000170302,0.0004937244,0.002918633,0.0002129753,9.66312e-7,0.00101027,0.9918891,0.0003885231,0.002828191,0.000002417849,0.0002369471],"study_design_scores_gemma":[0.0001071719,0.00002943422,0.0005569089,0.0006412392,0.0002090738,0.000001633162,0.0000227576,0.9947543,0.0006342419,0.00282838,0.00005748429,0.0001573789],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6401258,0.007256938,0.3521512,0.00001855276,0.0000648959,0.000203207,0.00003592729,0.0001152506,0.0000282534],"genre_scores_gemma":[0.9839847,0.001549367,0.01428709,5.125357e-7,0.00001939052,0.0001234874,0.00001164,0.00002064494,0.00000314127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.343859,"threshold_uncertainty_score":0.6135272,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1524857579","doi":"10.5006/c2004-04424","title":"Testing Methods and Standards for Oil Field Corrosion Inhibitors","year":2004,"lang":"en","type":"article","venue":"","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Corrosion; Oil field; Materials science; Field (mathematics); Metallurgy; Computer science; Petroleum engineering; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01014066580721642,"gpt":0.2870731332394356,"spread":0.2769324674322192,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003672578,0.0000809638,0.0000929422,0.00002556486,0.00003666323,0.00002003515,0.00003042156,0.00006389728,0.000004727567],"category_scores_gemma":[0.001265499,0.00006891267,0.00002231394,0.00006608556,0.0000105416,0.0000314404,0.00001548459,0.00007305173,4.450235e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006056565,"about_ca_system_score_gemma":0.00001819057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001705609,"about_ca_topic_score_gemma":0.000005304663,"domain_scores_codex":[0.99958,0.000003276473,0.0001131176,0.00009560465,0.00007926239,0.0001287894],"domain_scores_gemma":[0.9992684,0.0005082525,0.000006393868,0.00008493476,0.00007922152,0.0000528497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001078334,0.00004215852,0.001100349,0.001189134,0.00001987857,0.000003332289,0.0002634951,0.4074206,0.1245567,0.003153338,0.003566071,0.4586741],"study_design_scores_gemma":[0.001583878,0.0004502503,0.001378931,0.0004359435,0.00004717533,0.000012629,0.00006441877,0.1382665,0.8049492,0.006976302,0.04512736,0.0007074152],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1063112,0.0005659621,0.8870312,0.0001266193,0.0007752979,0.0000890589,0.00001475422,0.0004143005,0.004671612],"genre_scores_gemma":[0.6320517,0.00006227612,0.3677116,0.00003195583,0.00007865056,0.00001665143,0.000001150628,0.00001925531,0.00002672401],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6803925,"threshold_uncertainty_score":0.2810177,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2761971436","doi":"10.2495/safe-v7-n2-103-112","title":"Statistical analysis of failure consequences for oil and gas pipelines","year":2017,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pipeline transport; Statistical analysis; Forensic engineering; Environmental science; Petroleum engineering; Engineering; Statistics; Environmental engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.005946409513554729,"gpt":0.2442099865619895,"spread":0.2382635770484348,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002573315,0.00009512292,0.000275433,0.0001689102,0.00003675774,0.00005795298,0.0001755965,0.00005523619,0.00000677955],"category_scores_gemma":[0.0005480915,0.0000861076,0.00007737344,0.00003432369,0.00008448496,0.0001313078,0.00003004124,0.0001229141,6.228125e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001955273,"about_ca_system_score_gemma":0.00001112161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001235766,"about_ca_topic_score_gemma":0.0000221021,"domain_scores_codex":[0.9993327,0.000003842277,0.0003507326,0.00007108425,0.0001503944,0.0000912481],"domain_scores_gemma":[0.999195,0.000333288,0.0001013443,0.00008419045,0.0002143122,0.00007185547],"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":[0.0002057919,0.00008305466,0.01272153,0.0008691633,0.005721607,0.00008065835,0.001233732,0.8890601,0.007247356,0.03580708,0.0001829346,0.04678703],"study_design_scores_gemma":[0.001099754,0.00009517428,0.04596614,0.0003188651,0.0006594812,0.00008295759,0.0001040522,0.9388856,0.001970753,0.00194769,0.008560649,0.0003089177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8628311,0.001637057,0.1324801,0.001230538,0.00118436,0.00004115226,0.0004449851,0.00002963168,0.0001210691],"genre_scores_gemma":[0.9911139,0.003189266,0.00555142,0.000004471151,0.0001212966,0.000001051772,0.000007308101,0.000008210917,0.000003089319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1282828,"threshold_uncertainty_score":0.3511366,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2110189591","doi":"10.1061/(asce)hy.1943-7900.0000316","title":"Analysis of the Stability of Floating Ice Blocks","year":2011,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Geology; Stability (learning theory); Hydrology (agriculture); Geotechnical engineering; Environmental science; Geomorphology; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.0105598522961994,"gpt":0.1857004211269734,"spread":0.1751405688307739,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005862352,0.0001558035,0.0005119838,0.0002772882,0.00001578839,0.000004849355,0.0003524846,0.00008242991,0.00005260204],"category_scores_gemma":[0.00043778,0.0001154541,0.0004208351,0.0008966309,0.00003873957,0.00008448071,0.00004562788,0.0003314729,2.617986e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005452954,"about_ca_system_score_gemma":0.00002193014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002526044,"about_ca_topic_score_gemma":0.000005219591,"domain_scores_codex":[0.9985829,0.00001694299,0.0008451752,0.00008346926,0.0002837854,0.0001877293],"domain_scores_gemma":[0.9989243,0.0002295522,0.0002250299,0.0003620502,0.0001805428,0.00007854292],"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":[0.000004301798,0.00005111727,0.02089109,0.0002082707,0.0008115977,0.00000266636,0.0009165501,0.9526035,0.0241629,0.00005882761,0.00001490213,0.0002742346],"study_design_scores_gemma":[0.0002980221,0.00009015935,0.2189439,0.0002454768,0.001217033,0.00001370866,0.0001093165,0.5446626,0.2338417,0.00003601399,0.000294444,0.0002475797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9797052,0.0006296207,0.01829848,0.000008994178,0.0005808389,0.00006649463,0.00000814906,0.00003196979,0.0006702508],"genre_scores_gemma":[0.996201,0.00006652528,0.003653696,0.000003148193,0.00005033299,8.410715e-7,3.531372e-7,0.00002202461,0.000002021376],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4079409,"threshold_uncertainty_score":0.4708081,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2981437164","doi":"10.1108/ijqrm-01-2019-0035","title":"Reliability analysis of underground rock bolters using the renewal process, the non-homogeneous Poisson process and the Bayesian approach","year":2019,"lang":"en","type":"article","venue":"International Journal of Quality & Reliability Management","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Chicoutimi; Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Weibull distribution; Process (computing); Context (archaeology); Computer science; Renewal theory; Engineering; Statistics; Mathematics; Geology","retraction":null,"screen_n_in":null,"score":{"opus":0.007967711367547621,"gpt":0.2760224128864524,"spread":0.2680547015189047,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004935894,0.0002677641,0.0005896023,0.0001829018,0.0001076759,0.0001322892,0.001151686,0.00009209711,0.00001665225],"category_scores_gemma":[0.000408355,0.0001403416,0.0004316751,0.0005989854,0.0002819292,0.0001790196,0.0001685353,0.0004498517,8.007187e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002593152,"about_ca_system_score_gemma":0.00005049284,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001838309,"about_ca_topic_score_gemma":0.00001980234,"domain_scores_codex":[0.9967031,0.0002728085,0.001287708,0.0003192082,0.001163987,0.0002531406],"domain_scores_gemma":[0.9970207,0.0009502288,0.0005014842,0.0007636868,0.0006892764,0.00007459998],"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":[0.0001948478,0.0001722541,0.01210201,0.0004844608,0.002040566,0.000002249145,0.001406313,0.9814825,0.00003818621,0.001602771,0.00002651742,0.0004473106],"study_design_scores_gemma":[0.001561605,0.0000741666,0.06609689,0.0001409781,0.001624771,0.00002209836,0.003941981,0.9144226,0.0002252085,0.01122401,0.0003383494,0.000327343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9297194,0.0003049209,0.06613474,0.001602638,0.0007298469,0.0007298407,0.00002113004,0.00002581811,0.0007316655],"genre_scores_gemma":[0.9985084,0.0002842676,0.000915286,0.0001147743,0.0001032541,0.0000186537,0.000006707221,0.00002241172,0.00002624821],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06878899,"threshold_uncertainty_score":0.5722963,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4205517757","doi":"10.3390/ma15020560","title":"Study on the P-S-N Curve of Sucker Rod Based on Three-Parameter Weibull Distribution","year":2022,"lang":"en","type":"article","venue":"Materials","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Sucker rod; Sucker; Weibull distribution; Rod; Concentric; Structural engineering; Shape parameter; Fatigue limit; Materials science; Reliability (semiconductor); Mathematics; Composite material; Engineering; Statistics; Geometry; Physics; Anatomy","retraction":null,"screen_n_in":null,"score":{"opus":0.01303713806651685,"gpt":0.2117323756069752,"spread":0.1986952375404583,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005470754,0.0001182237,0.0001708374,0.00002640544,0.00006451742,0.00002245009,0.0001438412,0.00002666179,0.000439806],"category_scores_gemma":[0.0001079695,0.00008382443,0.00003812553,0.00009066493,0.00001639602,0.00001105736,0.00004180223,0.0001034905,0.00002028162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007271985,"about_ca_system_score_gemma":0.000005832584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002918929,"about_ca_topic_score_gemma":0.000001725621,"domain_scores_codex":[0.9992267,0.00008298362,0.0002119844,0.0001275395,0.0002095793,0.0001411796],"domain_scores_gemma":[0.9992249,0.000334698,0.00002998998,0.0003730433,0.00001700043,0.00002034079],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006096655,0.0005436951,0.0031225,0.00006206723,0.00005231779,0.000009001826,0.000135039,0.9841936,0.0079149,0.001167147,0.002676791,0.00006195856],"study_design_scores_gemma":[0.002721996,0.004343473,0.3898021,0.0001563728,0.0001923589,0.000003972549,0.0003895538,0.1314996,0.446155,0.001538016,0.02187297,0.001324466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966688,0.000009710609,0.00100507,0.000110828,0.00100649,0.0004279031,0.0005496445,0.00008578157,0.0001358319],"genre_scores_gemma":[0.99966,0.000001114024,0.00002499911,0.00002657883,0.00004458018,0.0001485765,0.00006415195,0.00002164296,0.000008403852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.852694,"threshold_uncertainty_score":0.4815567,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2810639989","doi":"10.1007/978-3-319-74693-7_20","title":"A Rational Basis for Determining Vibration Signature of Shaft/Coupling Misalignment in Rotating Machinery","year":2018,"lang":"en","type":"book-chapter","venue":"Conference proceedings of the Society for Experimental Mechanics","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Quest University Canada","funders":"","keywords":"Vibration; Signature (topology); Basis (linear algebra); Coupling (piping); Drive shaft; Structural engineering; Physics; Engineering; Control theory (sociology); Computer science; Acoustics; Mechanical engineering; Mathematics; Geometry; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.01531639759762011,"gpt":0.2345356179538547,"spread":0.2192192203562346,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002648685,0.0002968646,0.0003825488,0.0000413291,0.00006882571,0.00002899079,0.0002870726,0.000355833,0.00002254534],"category_scores_gemma":[0.0001010776,0.0002696555,0.0005688971,0.00003907596,0.0000470188,0.00009084417,0.00009679988,0.0002201987,2.04276e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001570294,"about_ca_system_score_gemma":0.00004757685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.313091e-7,"about_ca_topic_score_gemma":6.608323e-7,"domain_scores_codex":[0.9987892,6.212593e-7,0.0005155219,0.000265734,0.0002304255,0.0001985289],"domain_scores_gemma":[0.9991912,0.0001449052,0.0002651963,0.0001036901,0.0002584988,0.00003655833],"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":[0.00006570878,0.00009957782,0.0000259498,0.004182603,0.000480668,7.669883e-8,0.009745716,0.01435449,0.8538482,0.1156292,0.001041429,0.0005263694],"study_design_scores_gemma":[0.0003404258,0.0001620149,0.000002275468,0.001226391,0.00006939533,6.207423e-7,0.0007054785,0.7055686,0.2825322,0.008976848,0.0001458257,0.0002699503],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6552436,0.00913253,0.2941303,0.0004113947,0.007842407,0.0202991,0.002937022,0.0007880654,0.009215564],"genre_scores_gemma":[0.958833,0.00007921168,0.03998942,0.00002302358,0.0001676674,0.0002854536,0.0000373419,0.0001063368,0.0004785415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6912141,"threshold_uncertainty_score":0.9999756,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4232800508","doi":"10.1115/1.4051112","title":"An Ensemble of Recurrent Neural Networks for Real Time Performance Modeling of Three-Spool Aero-Derivative Gas Turbine Engine","year":2021,"lang":"en","type":"article","venue":"Journal of Engineering for Gas Turbines and Power","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Siemens (Canada); École de Technologie Supérieure","funders":"","keywords":"Nonlinear autoregressive exogenous model; Artificial neural network; Autoregressive model; Computer science; Generalization; Weighting; MATLAB; Nonlinear system; Control theory (sociology); Control engineering; Artificial intelligence; Engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.008593154951357368,"gpt":0.2140559200125297,"spread":0.2054627650611723,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003244766,0.000265278,0.0005973643,0.0001316199,0.00002990118,0.00002011787,0.0001422957,0.000131169,0.000007460756],"category_scores_gemma":[0.0001610819,0.00023461,0.0002127402,0.000169304,0.00002212067,0.0001834698,0.00002337167,0.0002363045,7.449083e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003453897,"about_ca_system_score_gemma":0.00002477787,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002253604,"about_ca_topic_score_gemma":0.000001217439,"domain_scores_codex":[0.9986463,0.000007259591,0.0007242894,0.0001548282,0.0001584409,0.0003088385],"domain_scores_gemma":[0.9986972,0.0002652967,0.0001438073,0.0002076204,0.0005462419,0.0001398779],"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":[0.0000820065,0.00006269159,0.0001855866,0.0005999956,0.0001262172,0.000002881525,0.0001342394,0.9742195,0.02257284,0.00007712092,0.00007467068,0.001862241],"study_design_scores_gemma":[0.0007511235,0.0006721983,0.0005602087,0.0003341537,0.00008683572,0.00003633248,0.00001532563,0.9882697,0.008802508,0.00004762924,0.0001968175,0.0002271851],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8319156,0.00197872,0.1649736,0.0000298798,0.0008316826,0.000187209,0.00003357706,0.00003622318,0.00001355429],"genre_scores_gemma":[0.9813932,0.001131601,0.01707162,0.000003555061,0.0002972009,0.00001094581,0.00002152419,0.00006435181,0.000005961195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1494777,"threshold_uncertainty_score":0.9567118,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1996204213","doi":"10.1108/13552510110407087","title":"Genetic algorithms for reliability assessment of mining equipment","year":2001,"lang":"en","type":"article","venue":"Journal of Quality in Maintenance Engineering","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Laurentian University","funders":"","keywords":"Reliability (semiconductor); Reliability engineering; Genetic algorithm; Computer science; Algorithm; Engineering; Machine learning","retraction":null,"screen_n_in":null,"score":{"opus":0.02228409987427662,"gpt":0.3076845387049573,"spread":0.2854004388306807,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00203667,0.0002089988,0.0005805821,0.0002069507,0.00001518265,0.00001548411,0.000251595,0.0001059663,0.000009087364],"category_scores_gemma":[0.0009196403,0.0001938753,0.0002089197,0.0002645199,0.00003087053,0.0001137677,0.00002837239,0.0003213458,2.554415e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000317332,"about_ca_system_score_gemma":0.00005547455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009675518,"about_ca_topic_score_gemma":0.00000427631,"domain_scores_codex":[0.9977733,0.00002810433,0.001357006,0.0001484306,0.0003030616,0.0003900524],"domain_scores_gemma":[0.9984802,0.0006960832,0.0001986326,0.0002683539,0.0002451025,0.0001116549],"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":[0.00001245285,0.00008041863,0.01474682,0.0005567544,0.00004250725,0.00001561973,0.0002027584,0.9790463,0.003257576,0.00029785,0.00009840851,0.001642537],"study_design_scores_gemma":[0.001188148,0.0002043857,0.256989,0.000642291,0.00002820066,0.00003984672,0.0002155674,0.7373462,0.0007808423,0.0003165252,0.001914782,0.0003341166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.624083,0.0003458422,0.3744202,0.00008167702,0.0007914138,0.0001665101,0.000008837849,0.00003238382,0.00007015776],"genre_scores_gemma":[0.8613717,0.000438518,0.1379751,0.000009494102,0.0001456585,0.00001810275,9.741256e-7,0.00003261571,0.00000779356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2422422,"threshold_uncertainty_score":0.7906006,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4309879527","doi":"10.1007/s11668-022-01516-4","title":"Fatigue Fracture of Aircraft Engine Compressor Disks","year":2022,"lang":"en","type":"article","venue":"Journal of Failure Analysis and Prevention","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"National Research Council Canada","funders":"","keywords":"Gas compressor; Solid mechanics; Low-cycle fatigue; Fatigue cracking; Materials science; Fatigue testing; Fracture (geology); Turbine; Gas turbines; Structural engineering; Cracking; Engineering; Mechanical engineering; Composite material","retraction":null,"screen_n_in":null,"score":{"opus":0.00635453727482793,"gpt":0.2267627857574891,"spread":0.2204082484826612,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003439285,0.00008008032,0.0002969612,0.0002436031,0.00004009247,0.0000115731,0.00008734779,0.00003616135,0.0001751681],"category_scores_gemma":[0.00002381608,0.00006812946,0.000266028,0.000365127,0.00001101548,0.00006768372,0.00002209857,0.0002509514,1.165144e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002177745,"about_ca_system_score_gemma":0.000007646185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000758047,"about_ca_topic_score_gemma":0.00001063858,"domain_scores_codex":[0.9992502,0.00004018738,0.000371092,0.00005987142,0.0002035946,0.00007503811],"domain_scores_gemma":[0.9995666,0.00007127931,0.0001457726,0.0001035056,0.00006548955,0.0000474049],"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":[0.000005443108,0.00004402873,0.006264353,0.00007791607,0.0008807218,0.000003330074,0.00010711,0.988506,0.001138125,0.00002053782,0.0008372897,0.002115121],"study_design_scores_gemma":[0.002512683,0.001269403,0.3396686,0.0002535153,0.01033937,0.00009032524,0.0006962151,0.50057,0.01130125,0.003706103,0.1286556,0.0009369154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6657561,0.003691885,0.3299144,0.0002896535,0.0001836267,0.00007211136,0.00002309385,0.00001804716,0.00005112112],"genre_scores_gemma":[0.9965436,0.0003035548,0.003049341,0.00000580932,0.00004972775,0.000002237295,0.00001319614,0.000007594872,0.00002489421],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.487936,"threshold_uncertainty_score":0.2778238,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2464730673","doi":"10.2316/journal.206.2016.4.206-4112","title":"A CONTROL APPROACH FOR HUMAN-MECHATRONIC-HYDRAULICCOUPLED EXOSKELETON IN OVERLOAD-CARRYING CONDITION","year":2016,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Exoskeleton; Mechatronics; Computer science; Control (management); Control engineering; Engineering; Artificial intelligence; Simulation","retraction":null,"screen_n_in":null,"score":{"opus":0.005740779590923214,"gpt":0.2366954247737385,"spread":0.2309546451828153,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002600228,0.00008202015,0.0001403073,0.0001582388,0.00001773677,0.00003601834,0.00009141492,0.00005939997,0.00000410393],"category_scores_gemma":[0.0001017976,0.00006216472,0.00005663176,0.00003264225,0.00001501421,0.0001776347,0.000007634775,0.00007094964,4.47795e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001297687,"about_ca_system_score_gemma":0.00001232548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002651039,"about_ca_topic_score_gemma":0.000001824366,"domain_scores_codex":[0.9993179,0.000008885896,0.0003431083,0.00006772469,0.0001633865,0.00009900021],"domain_scores_gemma":[0.9995068,0.0001519056,0.0001017458,0.00004554548,0.0001587373,0.00003525237],"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":[0.00001566827,0.00005149967,0.001280958,0.00005679522,0.00009086489,0.000003684335,0.0000520774,0.9529555,0.03302794,0.006640535,0.0001336956,0.005690791],"study_design_scores_gemma":[0.003093259,0.00009842974,0.01357026,0.0002363523,0.0000279616,0.0000273487,0.00001626461,0.9766509,0.001513998,0.004319562,0.0002961077,0.0001495594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2370886,0.0001269008,0.7618375,0.0002838704,0.0004469823,0.0001161748,0.00001351233,0.00002714974,0.00005938888],"genre_scores_gemma":[0.9931499,0.0001085166,0.006509011,0.0000153368,0.0001824328,0.000007844694,0.000008492864,0.00001262894,0.00000578849],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7560614,"threshold_uncertainty_score":0.2535004,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1586907968","doi":"10.4271/2007-01-1070","title":"Parametric Analysis of Catalytic Converter Plugging Caused by Manganese-Based Gasoline Additives","year":2007,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Gasoline; Manganese; Parametric statistics; Catalytic converter; Catalysis; Petroleum engineering; Environmental science; Automotive engineering; Process engineering; Waste management; Materials science; Computer science; Chemistry; Metallurgy; Engineering; Organic chemistry; Mathematics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.006340309078778187,"gpt":0.2231421279030577,"spread":0.2168018188242795,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008629673,0.0007437855,0.001339753,0.0009304227,0.0001272595,0.0000501938,0.0007222628,0.000742426,0.0003489304],"category_scores_gemma":[0.001182037,0.0006667678,0.0006967436,0.0032614,0.0007292478,0.0001833997,0.0001259422,0.00104415,0.00003163583],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003440595,"about_ca_system_score_gemma":0.00004593467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004738688,"about_ca_topic_score_gemma":0.01285984,"domain_scores_codex":[0.9960123,0.0000534887,0.001299717,0.0008375138,0.0008020551,0.0009949],"domain_scores_gemma":[0.9957903,0.002169054,0.0001652926,0.001309804,0.000139398,0.0004261361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00009999207,0.0004578797,0.001426035,0.0002292064,0.0005903053,0.00006277315,0.00002374553,0.008827339,0.9829556,0.0007229192,0.002866505,0.001737658],"study_design_scores_gemma":[0.0006347662,0.0004326752,0.9867595,0.0001635794,0.0007606276,0.00000641105,0.00004025848,0.00007340415,0.002189136,0.00008261741,0.008091801,0.0007652311],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830987,0.001335282,0.0002052859,0.0005362707,0.0004136159,0.001035462,0.0007123858,0.003929308,0.008733632],"genre_scores_gemma":[0.9973869,0.0002690542,0.00130977,0.0003034718,0.0000658942,0.00009474054,0.000366563,0.0001381234,0.00006548606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9853334,"threshold_uncertainty_score":0.9995784,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4388566292","doi":"10.18280/ijsse.130511","title":"Assessing Occupational Risk: A Classification of Harmful Factors in the Production Environment and Labor Process","year":2023,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Production (economics); Hazardous waste; Process (computing); Work (physics); Industrial production; Microclimate; Business; Identification (biology); Risk analysis (engineering); Occupational safety and health; Environmental science; Environmental resource management; Environmental economics; Engineering; Computer science; Waste management; Geography","retraction":null,"screen_n_in":null,"score":{"opus":0.01159792812497621,"gpt":0.2538532662810767,"spread":0.2422553381561005,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005238894,0.00008213852,0.0001127313,0.0001771951,0.00002116002,0.0000320914,0.0001042944,0.00004322996,0.000002904423],"category_scores_gemma":[0.000341597,0.00006456425,0.00002711066,0.0001305543,0.0000241604,0.0002390194,0.00001428195,0.0002250932,2.567114e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004233312,"about_ca_system_score_gemma":0.00001121739,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004111573,"about_ca_topic_score_gemma":0.00000180762,"domain_scores_codex":[0.9992576,0.0000155724,0.0003193732,0.00007311958,0.0002557737,0.00007856159],"domain_scores_gemma":[0.9995276,0.0002364744,0.00008343158,0.00005129373,0.00007418213,0.00002700771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00002401361,0.00004405881,0.1258163,0.0001586096,0.00008029892,0.000007381867,0.003882884,0.8647437,0.002852344,0.0007115618,0.00001526524,0.001663532],"study_design_scores_gemma":[0.0001501691,0.00001498936,0.8420795,0.0001150343,0.00001180171,0.00001641729,0.0004836082,0.1558236,0.0006897931,0.0002495873,0.0002974076,0.00006803338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970701,0.0002405016,0.001941237,0.0002388555,0.0003954323,0.00006279645,0.0000195649,0.00001911477,0.00001233776],"genre_scores_gemma":[0.9977579,0.001942848,0.0001804523,0.000002257788,0.00009727276,0.000002314373,0.000008368377,0.000008011619,5.662989e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7162632,"threshold_uncertainty_score":0.2632854,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2791096214","doi":"","title":"Development of Equipment Failure Prognostic Model based on Logical Analysis of Data (LAD)","year":2012,"lang":"en","type":"article","venue":"Library and Archives Canada (Government of Canada)","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Computer science; Risk analysis (engineering); Business","retraction":null,"screen_n_in":null,"score":{"opus":0.008984716547035388,"gpt":0.1628394506623266,"spread":0.1538547341152912,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00002580346,0.0001367949,0.0002843167,0.00004186237,0.00002797767,0.000003427344,0.0002267913,0.00002529755,0.00001503079],"category_scores_gemma":[0.000008257971,0.0001191889,0.00002550231,0.0001317275,0.00003254851,0.0001177894,0.0001096934,0.00007987354,9.049849e-10],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008096682,"about_ca_system_score_gemma":0.0003500233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005929971,"about_ca_topic_score_gemma":0.001720299,"domain_scores_codex":[0.9984411,0.00001250321,0.0003232496,0.0001365347,0.0008720473,0.0002145742],"domain_scores_gemma":[0.9992002,0.0002721108,0.00006198463,0.0003075563,7.042097e-7,0.0001574932],"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":[0.00006643499,0.0001762418,0.0766208,0.0007707144,0.0007168348,0.000004947956,0.0001302697,0.8922346,0.01218391,0.01335916,0.000238874,0.003497181],"study_design_scores_gemma":[0.0001380662,0.0000206455,0.0726645,0.00008014085,0.0001427974,1.086604e-7,0.0001210476,0.8901631,0.03462511,0.00002367178,0.001858714,0.0001621423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9305983,0.0004809119,0.0392022,0.0004099987,0.0002412104,0.0003560929,0.001287458,0.00003619863,0.02738762],"genre_scores_gemma":[0.9757063,0.00001707287,0.02412796,0.00004835452,0.00001043091,0.000006636578,0.00004023209,0.00001167742,0.00003128892],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04510804,"threshold_uncertainty_score":0.4860384,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2053167414","doi":"10.1520/jai101244","title":"Measurement of Corrosion Potentials of the Internal Surface of Operating High-Pressure Oil and Gas Pipelines","year":2008,"lang":"en","type":"article","venue":"Journal of ASTM International","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Natural Resources Canada; Devon Energy (Canada)","funders":"","keywords":"Pipeline transport; Corrosion; Materials science; Internal pressure; Petroleum engineering; Environmental science; Metallurgy; Composite material; Geology; Environmental engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.008089240603051938,"gpt":0.2001168879309348,"spread":0.1920276473278828,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004890697,0.00008132598,0.0002288646,0.00005378091,0.00001597814,0.00000603973,0.0002264134,0.00004001526,0.00002817778],"category_scores_gemma":[0.0004140635,0.00005467821,0.0001025198,0.00004794772,0.00005674794,0.00006904839,0.00005435412,0.0001491242,1.656029e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000242281,"about_ca_system_score_gemma":0.00003033605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002537668,"about_ca_topic_score_gemma":0.000002974966,"domain_scores_codex":[0.9986095,0.00002455231,0.0006735245,0.00005247221,0.000578512,0.00006143993],"domain_scores_gemma":[0.9987165,0.00007164669,0.0003065477,0.0000934278,0.0007816887,0.0000301955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002177181,0.0000736025,0.02290173,0.0001304769,0.0001303931,0.000002513949,0.0001703531,0.5968013,0.3784287,0.00008218085,0.0004419243,0.0008150985],"study_design_scores_gemma":[0.001527592,0.0002117629,0.1922869,0.002077887,0.000126223,0.0002050443,0.0000821945,0.1130234,0.6891495,0.0001480838,0.0009719355,0.0001895062],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915595,0.0009132935,0.005789306,0.0001621631,0.001384699,0.0000197353,0.0000211871,0.000003450892,0.0001466855],"genre_scores_gemma":[0.9970244,0.0005825152,0.002228265,0.000003833803,0.0001109409,2.267607e-7,4.736769e-7,0.000008871003,0.00004043601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4837779,"threshold_uncertainty_score":0.2229713,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2074668057","doi":"10.1115/detc2010-29126","title":"EMD, Ranking Mutual Information and PCA Based Condition Monitoring","year":2010,"lang":"en","type":"article","venue":"","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Syncrude","keywords":"Mutual information; Condition monitoring; Ranking (information retrieval); Monotonic function; Impeller; Data mining; Computer science; Artificial intelligence; Fault (geology); Pattern recognition (psychology); Feature (linguistics); Fault detection and isolation; Information fusion; Feature extraction; Machine learning; Engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.002361691469539282,"gpt":0.1825384536042161,"spread":0.1801767621346768,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009269148,0.00006867562,0.00005661001,0.00005646493,0.00003288144,0.00004596335,0.00003168376,0.0000669815,0.00003512645],"category_scores_gemma":[0.00006373688,0.00006462925,0.0000143389,0.00005101908,0.00001436658,0.0002174255,0.000007538315,0.0001513591,0.00001880195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001045295,"about_ca_system_score_gemma":0.000004508629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007641629,"about_ca_topic_score_gemma":0.000003269181,"domain_scores_codex":[0.9996736,0.000001969123,0.0001126015,0.00004429934,0.00006851237,0.00009903638],"domain_scores_gemma":[0.9997611,0.00007422538,0.000008328472,0.00008743588,0.00002440567,0.00004450596],"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":[0.00001811835,0.00007046195,0.1078212,0.00130811,0.00009162826,0.000007695489,0.001196335,0.5610125,0.2134216,0.01144621,0.002140408,0.1014657],"study_design_scores_gemma":[0.0005728684,0.00002311507,0.08003569,0.00003984252,0.00001344938,0.00000412623,0.00003714325,0.8507373,0.05785104,0.0002166999,0.01018823,0.0002804579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9677256,0.00001858054,0.02876826,0.00002306244,0.00101429,0.00007291682,0.0000042771,0.000319787,0.00205327],"genre_scores_gemma":[0.9950105,0.00001556087,0.004839173,0.000009691961,0.00009244357,0.000008406032,0.00001212482,0.000007590132,0.000004507442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2897248,"threshold_uncertainty_score":0.2635504,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4311078569","doi":"10.18280/ijsse.120509","title":"Relationship Between Occupational Risk and Personal Protective Equipment on the Example of Ferroalloy Production","year":2022,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Ferroalloy; Personal protective equipment; Production (economics); Occupational exposure; Forensic engineering; Environmental health; Risk analysis (engineering); Engineering; Business; Medicine; Metallurgy; Materials science; Economics; Internal medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.01791344649508277,"gpt":0.2278955042257952,"spread":0.2099820577307124,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000723965,0.000094262,0.0001278775,0.0001335989,0.00009320938,0.00001506932,0.0001154723,0.0000299816,0.00001423523],"category_scores_gemma":[0.0004279676,0.00007932713,0.00004974489,0.00008509974,0.00002901071,0.00008362562,0.00005363494,0.0005010444,1.934778e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001217345,"about_ca_system_score_gemma":0.00001846165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002169434,"about_ca_topic_score_gemma":0.000001516448,"domain_scores_codex":[0.9991137,0.00003444114,0.0003067792,0.00008845628,0.0003742006,0.00008238488],"domain_scores_gemma":[0.9990976,0.0005991772,0.0001003031,0.00005707824,0.0001045033,0.00004127689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000149465,0.00006041568,0.06348594,0.00008217512,0.000259554,0.000005729455,0.003371396,0.9209055,0.0003698224,0.009965578,0.00007201736,0.001272456],"study_design_scores_gemma":[0.0006462716,0.0002803639,0.8905193,0.0001300402,0.00006177071,0.0001012336,0.0003046345,0.09879402,0.0008250372,0.002819875,0.005274282,0.0002431326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9901074,0.0003228023,0.008338896,0.0003832791,0.0005876927,0.0001294161,0.00008178296,0.00001888377,0.00002988851],"genre_scores_gemma":[0.9994121,0.0001114394,0.0002462432,0.000005913818,0.0001950346,0.000008655458,0.000006743368,0.00001095066,0.000002911371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8270334,"threshold_uncertainty_score":0.3234867,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2343564205","doi":"10.5267/j.esm.2016.1.001","title":"Campbell diagram analysis of open cracked rotor","year":2016,"lang":"en","type":"article","venue":"Engineering Solid Mechanics","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Diagram; Rotor (electric); Materials science; Structural engineering; Engineering drawing; Computer science; Mechanical engineering; Engineering; Database","retraction":null,"screen_n_in":null,"score":{"opus":0.006774997904466819,"gpt":0.2310044703245135,"spread":0.2242294724200466,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003257526,0.0002658567,0.0005799489,0.000358884,0.00002330326,0.00003863539,0.0005769809,0.0001522025,0.00009562835],"category_scores_gemma":[0.0003217958,0.0002106457,0.0002080697,0.0008632371,0.00001120524,0.0001480937,0.0001514195,0.0001384309,0.00001985859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001097566,"about_ca_system_score_gemma":0.00001794268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001472542,"about_ca_topic_score_gemma":0.00001012063,"domain_scores_codex":[0.9986872,0.00001082763,0.0004514134,0.000258706,0.0001973466,0.0003945061],"domain_scores_gemma":[0.9988184,0.0002597006,0.00004906454,0.0006307739,0.00007841014,0.000163634],"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":[0.000004445221,0.00005286751,0.0001547169,0.000129394,0.001094695,0.000006337357,0.00008171421,0.9234284,0.0634004,0.008709488,0.0002912096,0.002646376],"study_design_scores_gemma":[0.0003937538,0.0000558552,0.001306517,0.0001148131,0.0004505636,0.000001393869,0.000005492491,0.944383,0.04442107,0.0002599693,0.008137477,0.0004700672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1430419,0.0004086688,0.8534518,0.00004904031,0.001360242,0.000578339,0.0001537437,0.0006814628,0.0002747288],"genre_scores_gemma":[0.9944663,0.0003682972,0.004828874,0.000007925108,0.00006561021,0.00008261783,0.00001284504,0.0000779598,0.00008959055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8514243,"threshold_uncertainty_score":0.8589883,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4394624992","doi":"10.1109/reepe60449.2024.10479711","title":"Analysis of Regulatory Requirements for Providing Personal Protective Equipment to Electric Power Industry Employees in Russia, the USA and Canada","year":2024,"lang":"en","type":"article","venue":"","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Electric power industry; Business; Personal protective equipment; Electric power; Power (physics); Telecommunications; Engineering; Electrical engineering; Electricity; Medicine","retraction":null,"screen_n_in":null,"score":{"opus":0.009826430695535472,"gpt":0.2342539138367491,"spread":0.2244274831412136,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002185706,0.00009464761,0.0001433727,0.0001842775,0.00002263713,0.00002103501,0.00006157409,0.00006429765,0.0000151717],"category_scores_gemma":[0.00006195475,0.00006684531,0.00003699845,0.0005248626,0.00000938332,0.00003209895,0.00002048827,0.0001586699,1.487922e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002828553,"about_ca_system_score_gemma":0.00009671316,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03436574,"about_ca_topic_score_gemma":0.1080282,"domain_scores_codex":[0.9993531,0.000007680885,0.0001679418,0.0001533071,0.0001434768,0.0001744689],"domain_scores_gemma":[0.9996907,0.0001359111,0.000008403435,0.00009618164,0.00002193406,0.00004690548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00004784714,0.0001336251,0.2227533,0.001663807,0.004396597,0.00002136161,0.004300791,0.7242311,0.02923035,0.003501043,0.003895734,0.005824391],"study_design_scores_gemma":[0.0002025388,0.0001887897,0.5005394,0.0002173453,0.0003254247,9.677966e-7,0.0002863312,0.4813724,0.01470042,0.00009211402,0.001693482,0.0003807466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969958,0.0002635092,0.001861091,0.0001359461,0.0001009451,0.0004928714,0.00001279281,0.00002828006,0.0001087504],"genre_scores_gemma":[0.9996216,0.00000563555,0.0001402542,0.00002050649,0.00001293928,0.0001346029,0.000001182906,0.00001261543,0.00005070679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2777861,"threshold_uncertainty_score":0.9720645,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2527718235","doi":"10.36001/phmconf.2015.v7i1.2724","title":"A New Generic Approach to Convert FMEA in Causal Trees for the Purpose of Hydro-Generator Rotor Failure Mechanisms Identification","year":2015,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"Hydro-Québec; École de Technologie Supérieure","funders":"","keywords":"Identification (biology); Generator (circuit theory); Failure mode and effects analysis; Stator; Computer science; Reliability engineering; Fault tree analysis; Root cause; Rotor (electric); Component (thermodynamics); Root cause analysis; Data mining; Artificial intelligence; Engineering; Mechanical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.02529017832348318,"gpt":0.2235715828618013,"spread":0.1982814045383182,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000358025,0.0001277916,0.0001929721,0.00001697421,0.00003005253,0.00002259098,0.000398949,0.00009680107,0.000002667917],"category_scores_gemma":[0.0001486348,0.0000840971,0.0001309658,0.0002068167,0.00004151644,0.00006811456,0.00006676791,0.0001135791,0.000001304548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005016419,"about_ca_system_score_gemma":0.0001037946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001414927,"about_ca_topic_score_gemma":0.00005002732,"domain_scores_codex":[0.9992217,0.00001964005,0.0002594441,0.0001486597,0.000184938,0.0001656371],"domain_scores_gemma":[0.9992148,0.00008656298,0.00005315544,0.0003529368,0.0002139299,0.00007863619],"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":[0.00003639112,0.000263288,0.0007491025,0.0005672204,0.0003194475,2.445684e-7,0.03638819,0.6842518,0.1975265,0.01743846,0.05869547,0.003763853],"study_design_scores_gemma":[0.001217674,0.0001806354,0.005167721,0.0001050824,0.0001327845,0.000002412499,0.006087372,0.7739659,0.2028907,0.004279701,0.005470566,0.0004994004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3269572,0.0003669022,0.6688479,0.0008947762,0.000803351,0.001722901,0.0002410025,0.00007458711,0.00009133856],"genre_scores_gemma":[0.9897588,0.000029181,0.009862039,0.00002992601,0.00006222484,0.0001249698,0.000008735228,0.00001828284,0.0001058783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6628016,"threshold_uncertainty_score":0.342938,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4226474509","doi":"10.5267/j.msl.2022.2.004","title":"Selecting maintenance strategy in a combined cycle power plant: An AHP model utilizing BOCR technique","year":2022,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Preventive maintenance; Predictive maintenance; Reliability engineering; Proactive maintenance; Computer science; Analytic hierarchy process; Condition-based maintenance; Planned maintenance; Rank (graph theory); Reliability (semiconductor); Total productive maintenance; Plan (archaeology); Operations research; Risk analysis (engineering); Operations management; Power (physics); Production (economics); Engineering; Business; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.006992505336940253,"gpt":0.20603834891094,"spread":0.1990458435739997,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008586471,0.0001420093,0.000117596,0.0003119178,0.0002224289,0.00007523275,0.000513421,0.00001767192,0.00001385791],"category_scores_gemma":[0.00001393134,0.0001556716,0.00002562192,0.0008679301,0.00007968233,0.0002314829,0.0001912212,0.0003040961,0.000001864952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003215573,"about_ca_system_score_gemma":0.000006757862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002381281,"about_ca_topic_score_gemma":0.000006556375,"domain_scores_codex":[0.9985657,0.00001671588,0.0001986737,0.000352869,0.0003412761,0.0005247995],"domain_scores_gemma":[0.9995772,0.00002133353,0.00002254453,0.000306737,0.000006146154,0.00006604411],"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":[0.000002229559,0.00003514445,0.0008146157,0.0000368572,0.000003867488,0.00004105019,0.0001652815,0.9808444,0.01446055,0.003058635,0.000312386,0.0002249665],"study_design_scores_gemma":[0.0001805485,0.00003851468,0.004105909,0.00002522134,0.000003360871,0.000003767224,0.0002516042,0.9938361,0.0007157075,0.0003603397,0.0002536042,0.000225342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9411014,0.00001300043,0.05089157,0.0003752128,0.0002601549,0.000547822,0.000009545477,0.0004199565,0.006381326],"genre_scores_gemma":[0.9970875,0.0000073182,0.002307604,0.0003710382,0.000006600054,0.0001738976,0.000004012398,0.00002168887,0.00002034243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05598609,"threshold_uncertainty_score":0.6348104,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2624905986","doi":"10.5006/c2015-05473","title":"Corrosion of a Vertical Shell and Tube Heat Exchanger","year":2015,"lang":"en","type":"article","venue":"","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Nova Chemicals (Canada)","funders":"","keywords":"Corrosion; Heat exchanger; Materials science; Shell and tube heat exchanger; Tube (container); Metallurgy; Shell (structure); Composite material; Mechanical engineering; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01189519673949572,"gpt":0.2037969736745697,"spread":0.191901776935074,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006658306,0.00005232425,0.00008287597,0.00001916181,0.000004074335,0.000004282788,0.00002544864,0.00004251591,0.0000193252],"category_scores_gemma":[0.00005446771,0.00004146618,0.00001261971,0.00003947247,0.00002038956,0.00002076675,0.00001900032,0.00004502746,0.000008617832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001192809,"about_ca_system_score_gemma":0.000003399733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001347904,"about_ca_topic_score_gemma":0.000002856871,"domain_scores_codex":[0.9997164,0.000002897706,0.00007895759,0.00005605795,0.00006280174,0.00008288163],"domain_scores_gemma":[0.9997512,0.00004526183,9.694421e-7,0.0000888115,0.00002266019,0.00009116252],"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":[0.0000859498,0.0005401497,0.0717172,0.002148529,0.00008749913,0.00003994772,0.003175132,0.4600003,0.3625317,0.01361493,0.07799608,0.008062633],"study_design_scores_gemma":[0.0008410463,0.0002314903,0.01552663,0.00008277057,0.0000298144,0.00001114835,0.00007133777,0.873187,0.09870944,0.0006158199,0.01036118,0.0003323182],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835473,0.0007754996,0.01052923,0.00005008265,0.0003275408,0.00006339615,0.000002189526,0.0001186862,0.004586075],"genre_scores_gemma":[0.9990659,0.00008159885,0.0007711469,0.00001069056,0.00002299166,0.000002265683,0.000001094237,0.000008810244,0.00003554442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4131867,"threshold_uncertainty_score":0.1690942,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2580712357","doi":"10.5006/c2013-02118","title":"Corrosion of a Shell-and-Tube Heat Exchanger","year":2013,"lang":"en","type":"article","venue":"","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Nova Chemicals (Canada)","funders":"","keywords":"Shell and tube heat exchanger; Corrosion; Heat exchanger; Materials science; Tube (container); Shell (structure); Metallurgy; Composite material; Mechanical engineering; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.004615188964200362,"gpt":0.1749468823780875,"spread":0.1703316934138872,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003525997,0.00006253208,0.00008997248,0.00002689391,0.000007687458,0.000007262819,0.00003350872,0.00004349393,0.0002915315],"category_scores_gemma":[0.00001784509,0.00004861947,0.00002001823,0.00004567304,0.00001614713,0.00003506021,0.00001789197,0.00004756855,0.00004031636],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007396387,"about_ca_system_score_gemma":0.000001272779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006714061,"about_ca_topic_score_gemma":0.000002703335,"domain_scores_codex":[0.9996993,0.000002283548,0.00009482897,0.00006107324,0.00004874801,0.00009376596],"domain_scores_gemma":[0.9997631,0.00005422335,0.00000273911,0.000110887,0.00002208505,0.00004691129],"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":[0.00000444937,0.0001674209,0.02194947,0.001674504,0.00005651698,0.000003790118,0.0009172334,0.1775231,0.7277641,0.004119693,0.04868142,0.01713833],"study_design_scores_gemma":[0.0004493774,0.0001194747,0.068597,0.0001211629,0.00002557355,0.000006472711,0.00006357651,0.8086051,0.1130117,0.0008732089,0.007678855,0.0004484202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828056,0.0007023563,0.008816311,0.00007809918,0.0003033729,0.0001584159,0.000002333671,0.0001845794,0.006948974],"genre_scores_gemma":[0.9983861,0.0002656555,0.001137157,0.00001539268,0.00002146926,0.00001259361,0.000001278265,0.00001144056,0.0001489017],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.631082,"threshold_uncertainty_score":0.3192066,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2245107911","doi":"10.1109/tdei.2015.005179","title":"Strategies to maximize life of rotating machines windings","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Dielectrics and Electrical Insulation","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Manitoba Hydro","funders":"","keywords":"Electromagnetic coil; Reliability (semiconductor); Reliability engineering; Engineering; Quality (philosophy); Automotive engineering; Computer science; Electrical engineering; Power (physics)","retraction":null,"screen_n_in":null,"score":{"opus":0.01642050597117335,"gpt":0.232377384285557,"spread":0.2159568783143836,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001423308,0.0001379659,0.0001802651,0.0002485715,0.00005070735,0.00003938534,0.00005811081,0.0000962454,0.00000331561],"category_scores_gemma":[0.00006926044,0.0001322019,0.00003958427,0.000753664,0.00001299686,0.00009868611,7.203009e-7,0.0002117269,0.00000346804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005110603,"about_ca_system_score_gemma":0.00003444107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003562763,"about_ca_topic_score_gemma":0.000003993992,"domain_scores_codex":[0.9992026,0.00001435028,0.000254434,0.0001479285,0.0001762021,0.0002044825],"domain_scores_gemma":[0.9994247,0.0001700841,0.00002453442,0.000100272,0.00009164451,0.0001887469],"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":[0.00003317997,0.00007119408,0.0001718406,0.00003818997,0.00003268196,7.52739e-7,0.0002405835,0.9590924,0.005101366,0.0008808704,0.00008107585,0.03425581],"study_design_scores_gemma":[0.0003402747,0.0003862203,0.003647574,0.00001754085,0.00002944437,0.000002403581,0.0000151731,0.9855408,0.008485791,0.001129608,0.0002019937,0.0002031514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4467764,0.0001898554,0.552261,0.00004414079,0.0001642863,0.0001310043,0.000004647476,0.0001109067,0.0003177725],"genre_scores_gemma":[0.9974136,0.0001405081,0.002332619,0.00002767737,0.00003115339,0.00001877792,0.000001801079,0.00002217094,0.00001172757],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5506372,"threshold_uncertainty_score":0.5391037,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4200372379","doi":"10.33423/jabe.v23i7.4867","title":"IPO Underpricing and Prospectus Readability: A Machine Learning Approach","year":2021,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Prospectus; Initial public offering; Business; Readability; Stock exchange; Monetary economics; Accounting; Finance; Economics; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.004972010358045508,"gpt":0.1565757729240755,"spread":0.15160376256603,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002108535,0.0001172872,0.0002811729,0.00005199727,0.00004790628,0.00007754193,0.00004192657,0.00006700822,0.00000417992],"category_scores_gemma":[0.0000416871,0.0001061912,0.00002732654,0.00008945161,0.00003206165,0.00007237878,0.00003921077,0.0002439063,3.663592e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004796496,"about_ca_system_score_gemma":0.00002619127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004150698,"about_ca_topic_score_gemma":0.000004465236,"domain_scores_codex":[0.9994254,0.000003487251,0.0002888256,0.0001178936,0.00003616293,0.0001282227],"domain_scores_gemma":[0.9996446,0.00006858986,0.00006573322,0.00008779535,0.00005949168,0.00007379692],"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":[0.00002175114,0.00005214265,0.004995338,0.0005301567,0.00009677832,0.00001030164,0.0002944832,0.976919,0.001052427,0.002130153,0.00001871371,0.01387877],"study_design_scores_gemma":[0.00188505,0.00007996089,0.06516193,0.0001639149,0.0002156612,0.0008008407,0.0007146133,0.9000527,0.003683624,0.0111363,0.01516834,0.0009370944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877751,0.001751214,0.008146426,0.00009115026,0.0001675313,0.0000533439,0.000002269027,0.00002540883,0.001987565],"genre_scores_gemma":[0.9890601,0.004672846,0.006102331,0.00001575753,0.0001145317,0.000001777823,0.000003037596,0.00002236141,0.000007278133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07686631,"threshold_uncertainty_score":0.4330352,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2518636877","doi":"10.5220/0005921600150023","title":"A Systematic Assessment of Operational Metrics for Modeling Operator Functional State","year":2016,"lang":"en","type":"article","venue":"","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Université Laval; Thales (Canada)","funders":"","keywords":"Computer science; Operator (biology)","retraction":null,"screen_n_in":null,"score":{"opus":0.01482058113305096,"gpt":0.2378958058151845,"spread":0.2230752246821335,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002754754,0.00008288359,0.0001715558,0.00007374519,0.00002057298,0.00001308882,0.00005382844,0.00003145242,0.00003422928],"category_scores_gemma":[0.0001991842,0.00004981947,0.00005383514,0.00008810135,0.000007253668,0.00006479735,0.00001064393,0.00002821379,0.000002907023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007017018,"about_ca_system_score_gemma":0.00003373765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002034116,"about_ca_topic_score_gemma":0.000001270199,"domain_scores_codex":[0.9993404,0.000006903943,0.0003065281,0.00009058852,0.0001481673,0.0001074434],"domain_scores_gemma":[0.9992828,0.0003703205,0.0000132841,0.0001185098,0.000173378,0.000041679],"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":[9.386509e-7,0.00001929391,0.0001803863,0.00271542,0.00006363942,9.90719e-8,0.000006583236,0.9861946,0.004079606,0.006558729,0.0001293122,0.00005144646],"study_design_scores_gemma":[0.0002486484,0.0000232267,0.0001390568,0.0004909782,0.00001587275,5.551799e-7,0.000004380837,0.9973572,0.001398534,0.0002137671,0.00002347559,0.0000843181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05639277,0.0001530695,0.9424917,0.00003484947,0.0003224281,0.0003230711,0.00004852826,0.00007601541,0.00015758],"genre_scores_gemma":[0.9766943,0.00006781791,0.02294726,0.00000855318,0.00003256623,0.0001273722,0.000005205886,0.00001665133,0.0001003064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9203015,"threshold_uncertainty_score":0.2031579,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3120149862","doi":"10.1115/gt2020-15756","title":"An Ensemble of Recurrent Neural Networks for Real Time Performance Modelling of Three-Spool Aero-Derivative Gas Turbine Engine","year":2020,"lang":"en","type":"article","venue":"","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Siemens (Canada); École de Technologie Supérieure","funders":"","keywords":"Nonlinear autoregressive exogenous model; Artificial neural network; Computer science; Autoregressive model; Generalization; MATLAB; Process (computing); Control theory (sociology); Control engineering; Artificial intelligence; Engineering; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.01921054416359242,"gpt":0.2059453294964369,"spread":0.1867347853328445,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001115647,0.0001797012,0.0003258165,0.00003444871,0.00001971552,0.000006460044,0.0001496209,0.00008453467,0.0000200604],"category_scores_gemma":[0.0000280218,0.0001614303,0.00008018532,0.0001622376,0.00002771781,0.00009392663,0.0000218319,0.0001497355,0.000001170577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000194193,"about_ca_system_score_gemma":0.000008026112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001109991,"about_ca_topic_score_gemma":0.000001722998,"domain_scores_codex":[0.999104,0.000006666754,0.0003654001,0.0001770562,0.0001112981,0.00023564],"domain_scores_gemma":[0.9993837,0.0001467559,0.00004530438,0.0002025555,0.0001096246,0.0001120192],"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":[0.00003605965,0.00003401883,0.0002226716,0.0003184768,0.00003138631,2.840335e-7,0.000165942,0.9893535,0.005976603,0.00008052025,0.00009602236,0.003684548],"study_design_scores_gemma":[0.0002796296,0.0004031204,0.0002756486,0.00004518536,0.00002305671,3.589733e-7,0.000007542133,0.9797223,0.01902633,0.00002401047,0.00002819249,0.0001646266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5741464,0.0001038217,0.4251483,0.00002626192,0.0001064258,0.0002179396,0.00001462499,0.0001247355,0.0001114204],"genre_scores_gemma":[0.9844456,0.0002833788,0.01502157,0.000007400088,0.0001393031,0.00001829588,0.00004039063,0.0000401557,0.000003888345],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4102992,"threshold_uncertainty_score":0.6582935,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3153484358","doi":"10.1007/s12541-021-00515-z","title":"Assessment of Geometrical Features of Internal Flaws with Artificial Neural Network","year":2021,"lang":"en","type":"article","venue":"International Journal of Precision Engineering and Manufacturing","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"Korea Electrotechnology Research Institute","keywords":"Artificial neural network; Artificial intelligence; Computer science; Engineering; Engineering drawing; Mechanical engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.006000986983745796,"gpt":0.2377241253667204,"spread":0.2317231383829746,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002539567,0.0001344317,0.0002968368,0.0002291421,0.00001091569,0.00003298503,0.0001858557,0.00006137735,0.00001772746],"category_scores_gemma":[0.0001329261,0.00010876,0.00009949003,0.00009533545,0.00002178614,0.0001028302,0.00006434797,0.0003156464,1.146039e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004670092,"about_ca_system_score_gemma":0.00002332275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004069672,"about_ca_topic_score_gemma":0.000001329107,"domain_scores_codex":[0.9987986,0.00001205692,0.0005174204,0.00009946241,0.0004376911,0.000134754],"domain_scores_gemma":[0.9991034,0.0003488846,0.0001286338,0.0001031818,0.0002359511,0.00008000809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0000229868,0.00003533305,0.001228502,0.00006588593,0.0002043769,0.00006657568,0.00003204195,0.9789197,0.006560512,0.0003138813,0.00005548439,0.01249466],"study_design_scores_gemma":[0.001102641,0.000332679,0.4177384,0.001299701,0.0001060885,0.0008274979,0.000055561,0.2746947,0.3007345,0.000453992,0.002263439,0.0003908363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.909488,0.0006994423,0.08809566,0.00003071372,0.001463566,0.00003456471,0.000008074652,0.00002017874,0.0001597556],"genre_scores_gemma":[0.9826876,0.0002451164,0.01673242,0.000004880612,0.0002995785,9.35547e-7,0.000002101754,0.00001871637,0.00000867444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7042251,"threshold_uncertainty_score":0.4435103,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3119863467","doi":"","title":"Predictive risk-based model for oil and gas pipelines","year":2013,"lang":"en","type":"article","venue":"","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Pipeline transport; Petroleum engineering; Environmental science; Engineering; Environmental engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.005066677356477894,"gpt":0.1821283785054346,"spread":0.1770617011489568,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005678608,0.00009266778,0.00008973931,0.00002574005,0.00002537007,0.00001953182,0.0000395321,0.00005546638,0.00001462191],"category_scores_gemma":[0.0001092679,0.0000745579,0.00002919172,0.00002854233,0.00001815825,0.00004448779,0.00000785339,0.00006321003,0.00000722917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001266225,"about_ca_system_score_gemma":0.000006135291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002891751,"about_ca_topic_score_gemma":0.00001300969,"domain_scores_codex":[0.9996158,0.000002356341,0.0001001959,0.000102133,0.00004287154,0.0001366365],"domain_scores_gemma":[0.9995859,0.0001724594,0.000007704986,0.0001126235,0.00005574095,0.0000655225],"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":[0.000001396535,0.00001055878,0.0004883877,0.00009212427,0.00001003651,5.118389e-8,0.00002630924,0.9901013,0.0001611334,0.00007654719,0.002585855,0.006446352],"study_design_scores_gemma":[0.0002194869,0.00001951955,0.0007763856,0.00001082961,0.0000132307,1.369894e-7,0.000004838714,0.9965149,0.0006757888,0.001338961,0.0003271363,0.00009884724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1938546,0.0002418913,0.8033308,0.0001679819,0.0001606562,0.000126043,0.00005010388,0.0004100751,0.001657779],"genre_scores_gemma":[0.9749365,0.0002828084,0.02419425,0.00002571249,0.00005466198,0.0001472673,0.000006506724,0.00002436031,0.0003278653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.781082,"threshold_uncertainty_score":0.3040383,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3000630179","doi":"10.2316/j.2019.203-0164","title":"DISCRETE PREDICTIVE CONTROL OF A FLYWHEEL ENERGY STORAGE FOR TRANSIENT STABILITY AUGMENTATION","year":2019,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Flywheel; Transient (computer programming); Model predictive control; Flywheel energy storage; Control theory (sociology); Stability (learning theory); Energy storage; Computer science; Control (management); Engineering; Automotive engineering; Physics; Artificial intelligence; Thermodynamics; Power (physics)","retraction":null,"screen_n_in":null,"score":{"opus":0.003089279795941874,"gpt":0.1953905490416407,"spread":0.1923012692456988,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002138954,0.00009065485,0.000212051,0.0000855906,0.000008500183,0.00002053034,0.0001060381,0.00005043882,0.00001325048],"category_scores_gemma":[0.00002591157,0.00007445701,0.00009812384,0.00002768213,0.00002018378,0.0001097914,0.000006192428,0.00005105316,1.377512e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007988304,"about_ca_system_score_gemma":0.00001851768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004920711,"about_ca_topic_score_gemma":0.000003563021,"domain_scores_codex":[0.9991673,0.00002067536,0.0004096914,0.00007711904,0.0002401198,0.00008513941],"domain_scores_gemma":[0.9992892,0.0001683229,0.000110547,0.00006794908,0.0003077957,0.00005623],"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":[0.0002297146,0.00007744031,0.00176766,0.000150926,0.000719894,0.00000471386,0.0007225135,0.9766462,0.01276367,0.005864437,0.000211672,0.0008411002],"study_design_scores_gemma":[0.004847737,0.0009851551,0.01352222,0.0003966526,0.0001132576,0.00005805536,0.000603488,0.9425223,0.006290689,0.0003894598,0.02994787,0.0003230897],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2158913,0.001320156,0.7778733,0.00004082889,0.004144432,0.00006578396,0.000154355,0.00001393924,0.0004958814],"genre_scores_gemma":[0.9995738,0.0001347896,0.00009724835,0.000008843462,0.0001258919,0.000009196086,0.00000748947,0.00001237275,0.00003031895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7836826,"threshold_uncertainty_score":0.3036268,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W263738382","doi":"10.5006/c2008-08027","title":"Insights into Atlas Cell Testing for Selection of Linings for Oil and Gas Production Vessels and Tanks","year":2008,"lang":"en","type":"article","venue":"","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Encana (Canada)","funders":"","keywords":"Atlas (anatomy); Selection (genetic algorithm); Petroleum engineering; Production (economics); Oil production; Environmental science; Forensic engineering; Marine engineering; Engineering; Computer science; Geology; Artificial intelligence","retraction":null,"screen_n_in":null,"score":{"opus":0.009842228506006696,"gpt":0.1959201111831814,"spread":0.1860778826771747,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000614506,0.00008159074,0.000111416,0.00004411725,0.00006376662,0.000006491644,0.00001797887,0.00006063819,4.37055e-7],"category_scores_gemma":[0.0003022794,0.00007394228,0.00001510789,0.00008545601,0.00002652489,0.00005966369,0.000007201003,0.00005061988,1.24335e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001439127,"about_ca_system_score_gemma":0.00000808296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001575168,"about_ca_topic_score_gemma":0.000007000908,"domain_scores_codex":[0.9995944,0.000002247073,0.0001407673,0.0001360183,0.0000365397,0.00008997615],"domain_scores_gemma":[0.9995573,0.0002317719,0.00002025889,0.00005376078,0.000104007,0.00003296637],"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":[0.00003703908,0.0001295911,0.01066845,0.006621854,0.00006430031,6.734588e-7,0.002743188,0.1962338,0.7380726,0.0003571549,0.002056573,0.04301477],"study_design_scores_gemma":[0.0006261324,0.0004352763,0.004745528,0.0001088122,0.00004213288,0.00001280389,0.00004301426,0.514933,0.4737104,0.001244996,0.003803503,0.0002943671],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.989512,0.0003704458,0.009395423,0.00003070176,0.0002260222,0.0001216248,0.000001066582,0.0001089616,0.0002337001],"genre_scores_gemma":[0.9629322,0.0002910864,0.03646447,0.000002450801,0.0001021036,0.00004420599,0.000002847565,0.00001820197,0.0001424195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3186992,"threshold_uncertainty_score":0.3015278,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2078164134","doi":"10.1006/jsvi.2000.3520","title":"DEVELOPMENT OF OPTIMALLY DISORDERED CRITICAL RANDOM EXCITATION","year":2001,"lang":"en","type":"article","venue":"Journal of Sound and Vibration","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematical optimization; Principle of maximum entropy; Gaussian; Mathematics; Gaussian process; Maximization; Spectral density; Excitation; Optimization problem; Pareto principle; Electric power system; Applied mathematics; Statistical physics; Power (physics); Physics; Statistics","retraction":null,"screen_n_in":null,"score":{"opus":0.009824819430227493,"gpt":0.2364114033473335,"spread":0.226586583917106,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001567016,0.00004432604,0.00009600576,0.00004516028,0.00002324232,0.00001776433,0.00002220251,0.0000314414,0.000007864159],"category_scores_gemma":[0.000123002,0.00003680143,0.00002183058,0.00004332019,0.00001525412,0.00012694,0.000003296159,0.00005656086,4.290344e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001178503,"about_ca_system_score_gemma":0.00001153182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.019019e-7,"about_ca_topic_score_gemma":0.000001422808,"domain_scores_codex":[0.9995655,0.00000570244,0.0002647619,0.00002792884,0.00008526204,0.00005088313],"domain_scores_gemma":[0.9997293,0.0001079089,0.00003308666,0.00002568126,0.00007107801,0.00003299693],"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":[0.0005729467,0.0006890323,0.02255236,0.001783645,0.0004189608,0.00003424069,0.01331192,0.650252,0.217756,0.01415416,0.0004499098,0.07802476],"study_design_scores_gemma":[0.008489024,0.0007333843,0.3628474,0.0007429326,0.0002026174,0.0002150626,0.00157886,0.5653825,0.01862351,0.03196236,0.008328506,0.0008938361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6411782,0.0002762464,0.3582558,0.00003848336,0.0001249044,0.00002025672,3.616756e-7,0.000006099006,0.00009963533],"genre_scores_gemma":[0.9694733,0.0001684721,0.03029275,0.000003613993,0.00005337799,5.180184e-7,0.000001299683,0.000004901785,0.000001757234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3402951,"threshold_uncertainty_score":0.1500719,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3118762265","doi":"10.4203/ccp.102.201","title":"Failure Prediction Model of Oil and Gas Pipelines","year":2013,"lang":"en","type":"article","venue":"Civil-comp proceedings","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"","keywords":"Pipeline transport; Petroleum engineering; Fossil fuel; Computer science; Environmental science; Geology; Engineering; Waste management; Environmental engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.004985376279006646,"gpt":0.1663857447468683,"spread":0.1614003684678616,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007519526,0.000140866,0.0001701894,0.00005482168,0.00002610538,0.00003382947,0.00008479331,0.00009927789,0.00001279352],"category_scores_gemma":[0.00006163543,0.0001296494,0.00003370989,0.00008460139,0.00004489614,0.0001825022,0.00003069457,0.0001384284,0.000006249856],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001864217,"about_ca_system_score_gemma":0.00000454498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000142904,"about_ca_topic_score_gemma":0.000002291828,"domain_scores_codex":[0.9993423,8.284972e-7,0.0002179157,0.0001507551,0.0001105235,0.0001777264],"domain_scores_gemma":[0.9996395,0.0000250451,0.00002547422,0.00007647662,0.0001510279,0.00008247017],"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":[0.000009855904,0.0001854236,0.01496775,0.006643505,0.0001568903,8.093947e-7,0.00270535,0.4282856,0.4298655,0.006640974,0.08654626,0.0239921],"study_design_scores_gemma":[0.0001769497,0.00002307587,0.001756761,0.0001015567,0.00001780393,0.00000360249,0.00004319269,0.9891608,0.005455042,0.001213822,0.001899165,0.0001482614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.991464,0.0004287386,0.001426677,0.0001853174,0.0001584614,0.00008490363,0.00001519289,0.0003602372,0.005876486],"genre_scores_gemma":[0.9956405,0.0007026624,0.003357629,0.000009190188,0.00009736353,0.00003941292,0.000003199304,0.00002993047,0.0001201068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5608752,"threshold_uncertainty_score":0.5286947,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W1493770628","doi":"10.5539/mas.v9n8p204","title":"Features of Aboveground Pipeline Compensation Part Stress-Deformed Study at Permafrost","year":2015,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Pipeline transport; Pipeline (software); Permafrost; Environmental science; Piping; Stress (linguistics); Deformation (meteorology); Geotechnical engineering; Geology; Marine engineering; Mining engineering; Computer science; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.01737563872890244,"gpt":0.2323628129422455,"spread":0.2149871742133431,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004732944,0.0001360743,0.0001751995,0.00007870993,0.00007936419,0.00003775961,0.0002843198,0.00004142104,0.000007638682],"category_scores_gemma":[0.00006324653,0.000115301,0.00002116469,0.0002859839,0.0001861467,0.00008700106,0.00009754272,0.0001147673,0.00001334971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001667928,"about_ca_system_score_gemma":0.00004231924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001864588,"about_ca_topic_score_gemma":0.00008109654,"domain_scores_codex":[0.9987739,0.000005315476,0.0002188715,0.000241837,0.0005072517,0.0002528401],"domain_scores_gemma":[0.999315,0.00004893697,0.00003328161,0.0003530467,0.0001042354,0.0001455539],"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":[0.00003146355,0.0004011661,0.0133109,0.0001148462,0.00001865983,0.000003733481,0.006088803,0.8100824,0.1641765,0.0009195024,0.001198918,0.003653048],"study_design_scores_gemma":[0.001138491,0.0001076997,0.1792254,0.00003295659,0.00002848266,0.000003794676,0.0007211548,0.7787237,0.03805432,0.0009424178,0.0005711034,0.0004505162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9712436,0.0001350259,0.01743669,0.0000148406,0.0004395727,0.0003977118,0.00005982363,0.0001582609,0.01011452],"genre_scores_gemma":[0.9994037,0.000008442911,0.0003964692,0.000008340056,0.0000512728,0.00002296404,0.00001669357,0.00001551515,0.00007656059],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1659145,"threshold_uncertainty_score":0.4701839,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410157187","doi":"10.3390/en18092395","title":"Analysis and Diagnosis of the Stator Turn-to-Turn Short-Circuit Faults in Wound-Rotor Synchronous Generators","year":2025,"lang":"en","type":"article","venue":"Energies","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Concordia University","funders":"China Scholarship Council","keywords":"Turn (biochemistry); Stator; Rotor (electric); Short circuit; Wound rotor motor; Electrical engineering; Control theory (sociology); Engineering; Computer science; Physics; Induction motor; Artificial intelligence; Voltage; Nuclear magnetic resonance","retraction":null,"screen_n_in":null,"score":{"opus":0.003676740346470709,"gpt":0.2023880217575495,"spread":0.1987112814110788,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001198762,0.0001520112,0.0002771834,0.0002662221,0.00003442568,0.00002437565,0.0001709098,0.00008383081,0.000009699657],"category_scores_gemma":[0.0001570006,0.0001172618,0.00009080365,0.0009649716,0.00004696286,0.00003737056,0.00007070689,0.000128463,7.357125e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008310989,"about_ca_system_score_gemma":0.00002227996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001997045,"about_ca_topic_score_gemma":0.0008076535,"domain_scores_codex":[0.9992237,0.00002113666,0.0002610373,0.0001840064,0.0001164742,0.0001936568],"domain_scores_gemma":[0.9993517,0.0002185151,0.0000149997,0.0003318588,0.00003250146,0.00005046597],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000001231705,0.0000205603,0.2966275,0.00008747232,0.0001748768,0.000001559206,0.0002290476,0.7003103,0.0007373751,0.0002406319,0.0001821695,0.001387313],"study_design_scores_gemma":[0.000187069,0.00002097265,0.911274,0.0001331856,0.0002436797,3.704465e-7,0.0001069327,0.02418592,0.06016606,0.0001627331,0.003225683,0.0002933764],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968501,0.001454664,0.0007968252,0.00006195501,0.0003878216,0.0001748057,0.00004129556,0.00006664241,0.0001658524],"genre_scores_gemma":[0.9990813,0.0004439766,0.000248397,0.00002691983,0.00002961384,0.0001168474,0.00000318171,0.00001488171,0.00003487013],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6761243,"threshold_uncertainty_score":0.4781797,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4409085222","doi":"10.18280/mmep.120330","title":"Alpha Power Type II-G Family: Adding a Power Parameter of Distributions","year":2025,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Power (physics); Type (biology); Mathematics; Physics; Biology; Thermodynamics","retraction":null,"screen_n_in":null,"score":{"opus":0.01103164307076366,"gpt":0.206642591238307,"spread":0.1956109481675434,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001913905,0.0002201791,0.0003335102,0.0001171488,0.00005560482,0.00002963309,0.0001102489,0.0001411467,0.00001663889],"category_scores_gemma":[0.0001437855,0.0002015596,0.00007328536,0.0002791495,0.00004057466,0.0000531345,0.00006136521,0.0002471102,0.000004771202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003389935,"about_ca_system_score_gemma":0.00001006521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000234512,"about_ca_topic_score_gemma":6.070611e-8,"domain_scores_codex":[0.9989934,0.000004980242,0.0003920948,0.0001884205,0.0001155173,0.0003055856],"domain_scores_gemma":[0.9992824,0.0002901697,0.00001861951,0.0002591469,0.00006010738,0.0000896047],"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":[0.000001788902,0.00005121915,0.00001589828,0.0007537273,0.00007332992,8.118178e-7,0.0001905979,0.9647521,0.001791331,0.03214615,0.000118352,0.000104683],"study_design_scores_gemma":[0.0001424711,0.00004699356,0.00006899943,0.00064502,0.00003952234,0.000002302406,0.00001140588,0.9888327,0.0006963584,0.007208196,0.002085971,0.0002201162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2300293,0.001272865,0.7661839,0.00002882866,0.0002793264,0.0001628724,0.00001680938,0.0003158364,0.001710291],"genre_scores_gemma":[0.9719981,0.000165714,0.02770278,0.00000580833,0.000009498865,0.00002650404,0.000006196555,0.00003293769,0.00005243619],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7419688,"threshold_uncertainty_score":0.8219362,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W3088252452","doi":"10.15407/pmach2020.03.037","title":"Analysis of the Static Strength of the Emergency-Cooldown Heat Exchanger with the Use of the Design Tightness Value of Flange-Joint Pins","year":2020,"lang":"en","type":"article","venue":"Problemy mašinostroeniâ/Problemy mašinostroeniâ","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Response Biomedical (Canada)","funders":"","keywords":"Flange; Heat exchanger; Joint (building); Value (mathematics); Structural engineering; Engineering; Mechanical engineering; Computer science","retraction":null,"screen_n_in":null,"score":{"opus":0.02835696925396775,"gpt":0.1934592203310346,"spread":0.1651022510770669,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005898025,0.000696782,0.001248235,0.0001883035,0.000187651,0.00003527785,0.001692462,0.0001892359,0.0001233313],"category_scores_gemma":[0.0003616035,0.000334835,0.0008977978,0.002950751,0.000632553,0.0001758929,0.0005311692,0.0007476319,0.000001810146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009472154,"about_ca_system_score_gemma":0.0001509573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002398581,"about_ca_topic_score_gemma":0.0002186571,"domain_scores_codex":[0.9953449,0.0004897275,0.001653583,0.0005793337,0.001149168,0.0007832393],"domain_scores_gemma":[0.9962177,0.0005638923,0.0007205775,0.001878404,0.0004426117,0.0001768257],"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":[0.00008616066,0.0001961818,0.04238759,0.001329121,0.001727399,0.000001098986,0.001577028,0.9210046,0.02899101,0.0008297475,0.001743441,0.0001266201],"study_design_scores_gemma":[0.002106785,0.000905179,0.2315066,0.001236593,0.007408684,0.0000106304,0.0006508266,0.4119985,0.3404922,0.000298104,0.002335708,0.00105012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9742323,0.0007543077,0.01512487,0.004437722,0.0006086311,0.003527896,0.0009732914,0.0001415746,0.0001994646],"genre_scores_gemma":[0.996645,0.0002844336,0.002590699,0.00008455359,0.00005621035,0.0001378741,0.00001745979,0.0001087643,0.0000749819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5090061,"threshold_uncertainty_score":0.9999104,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2232198394","doi":"10.5539/mas.v10n2p172","title":"New Flow Stabilizers as a Method to Improve the Reliability and Efficiency of Power Equipment","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"Ministry of Education and Science of the Russian Federation","keywords":"Reliability (semiconductor); Flow (mathematics); Flow resistance; Pipeline (software); Turbine; Computer science; Environmental science; Power (physics); Steam turbine; Reliability engineering; Automotive engineering; Mechanical engineering; Mechanics; Engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.00447956266160309,"gpt":0.226552257394555,"spread":0.2220726947329519,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001304709,0.0001415577,0.0001613805,0.00005950962,0.00007788979,0.00003048598,0.0004034005,0.00004245343,0.0000226468],"category_scores_gemma":[0.0002614938,0.00007788953,0.00003063924,0.0003342369,0.0002434032,0.00005875519,0.0001712336,0.00008525626,0.00001208023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001064886,"about_ca_system_score_gemma":0.00009181903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002018001,"about_ca_topic_score_gemma":0.000001861211,"domain_scores_codex":[0.9986553,0.000009456776,0.0002207087,0.0003951782,0.0003748882,0.0003444813],"domain_scores_gemma":[0.9988205,0.0003227229,0.00002159418,0.0005690315,0.0000540023,0.0002120959],"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":[0.000008466889,0.00001856503,0.00003783477,0.00002509111,0.000003503192,1.897942e-7,0.0009006856,0.06685977,0.8677555,0.00169964,0.00005200974,0.06263868],"study_design_scores_gemma":[0.0006214231,0.000204926,0.003682733,0.00005586621,0.00001904919,0.00000242504,0.00009015399,0.4988308,0.4749846,0.01924448,0.001787151,0.0004764661],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2283023,0.00004979537,0.7689751,0.0002754017,0.0001865114,0.0003693061,0.000006605245,0.00007474772,0.001760254],"genre_scores_gemma":[0.9524402,0.000009968077,0.04742512,0.00003387507,0.000013041,0.00002932418,6.692165e-8,0.00001264871,0.00003577914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7241379,"threshold_uncertainty_score":0.3176243,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2059668416","doi":"10.1007/s11015-014-9949-4","title":"The Repairs Project as a Tool for Improving the Productivity of Equipment","year":2014,"lang":"en","type":"article","venue":"Metallurgist","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"EVRAZ (Canada)","funders":"","keywords":"Productivity; Realization (probability); Manufacturing engineering; Engineering; Computer science; Systems engineering; Operations management; Mathematics","retraction":null,"screen_n_in":null,"score":{"opus":0.007525898563021024,"gpt":0.2205953578942758,"spread":0.2130694593312548,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001412916,0.0001139634,0.0001431681,0.0000183117,0.000121728,0.00003551403,0.0001989023,0.0000344565,0.000002716566],"category_scores_gemma":[0.00126289,0.00006111063,0.000113216,0.00009481253,0.00009088781,0.00003331365,0.00004395976,0.0001042937,0.000003171958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003211975,"about_ca_system_score_gemma":0.00002125655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005069046,"about_ca_topic_score_gemma":0.0000177298,"domain_scores_codex":[0.9992614,0.00002922266,0.0002175556,0.0001501212,0.0001357073,0.000206024],"domain_scores_gemma":[0.998808,0.0005592444,0.00004058375,0.0005188367,0.00005537298,0.0000179919],"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":[0.0001834773,0.0005636988,0.001036393,0.005716681,0.001271525,0.000004009726,0.002319744,0.525818,0.08325056,0.1369681,0.01102098,0.2318468],"study_design_scores_gemma":[0.0004534776,0.0002841406,0.002834183,0.00004310916,0.0001525515,0.000006750426,0.00008092874,0.5014132,0.03374701,0.001816819,0.4587656,0.000402239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8685439,0.001313549,0.118961,0.0009824246,0.002547801,0.002995033,0.00002055022,0.0005300207,0.004105678],"genre_scores_gemma":[0.9979392,0.00002552072,0.001225584,0.000007657505,0.0001397325,0.0003439246,0.000001880427,0.00002406267,0.0002924261],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4477446,"threshold_uncertainty_score":0.2492019,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2072761185","doi":"10.2478/v10012-007-0067-0","title":"A discrete model of the plate heat exchanger","year":2008,"lang":"en","type":"article","venue":"Polish Maritime Research","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true},"ca_institutions":"","funders":"","keywords":"Heat exchanger; Correctness; Mechanical engineering; Plate heat exchanger; Mechanics; Computer science; Engineering; Physics; Algorithm","retraction":null,"screen_n_in":null,"score":{"opus":0.05319625563822027,"gpt":0.2913274160318245,"spread":0.2381311603936043,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003820786,0.00009652352,0.0001429485,0.00009409736,0.0001115917,0.00001440194,0.0003079155,0.00008900404,0.00004994597],"category_scores_gemma":[0.0002189658,0.0000717698,0.00007734667,0.0003035901,0.0002245333,0.00005585303,0.0001715019,0.0004474988,0.00002168574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006452549,"about_ca_system_score_gemma":0.00004218277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002491276,"about_ca_topic_score_gemma":0.00001871067,"domain_scores_codex":[0.9988003,0.00003915344,0.000159833,0.0001310027,0.0004327543,0.0004369141],"domain_scores_gemma":[0.9991344,0.0002209388,0.000004989355,0.0004264703,0.0001139428,0.00009921395],"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":[0.00001444971,0.0001012532,0.005576464,0.0006499947,0.00007753267,0.00001629561,0.001033649,0.9379653,0.0153535,0.003485895,0.03526008,0.0004655459],"study_design_scores_gemma":[0.0002389131,0.00002634411,0.01506708,0.00009131547,0.000006058711,0.00001092564,0.0000129985,0.9710097,0.008897867,0.001775481,0.002681353,0.0001819757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9382402,0.001489455,0.001589292,0.0008037201,0.0003697641,0.0005739984,0.0001821486,0.0002349451,0.0565165],"genre_scores_gemma":[0.9980039,0.0006672303,0.0003174696,0.0000100508,0.00008673435,0.00003092434,0.00000324798,0.00003299743,0.0008474495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05976372,"threshold_uncertainty_score":0.2926688,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W2990857799","doi":"10.2495/dne-v14-n4-249-263","title":"From nature and basic scientific results to modern engineering applications","year":2019,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Engineering Diagnostics and Reliability","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false},"ca_institutions":"","funders":"","keywords":"Engineering; Management science; Computer science; Systems engineering","retraction":null,"screen_n_in":null,"score":{"opus":0.003711950556049949,"gpt":0.2109316954215396,"spread":0.2072197448654896,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002560749,0.0001268694,0.0001527713,0.0002295195,0.00002412745,0.0001445428,0.0002386557,0.0002300359,0.000003696955],"category_scores_gemma":[0.0001126915,0.0001161186,0.00004274285,0.0001164846,0.00001476328,0.0001443273,0.00004228324,0.0006905425,0.000004794073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006932343,"about_ca_system_score_gemma":0.0000204598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001751167,"about_ca_topic_score_gemma":0.000003033691,"domain_scores_codex":[0.9991907,0.000006588066,0.0002742891,0.000165746,0.0002404476,0.0001222388],"domain_scores_gemma":[0.999233,0.0002583524,0.00005425973,0.0001360022,0.0002057995,0.0001125951],"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":[0.00005259839,0.00003731807,0.0006648985,0.00004319469,0.0002280937,0.00001645175,0.0003644768,0.9709899,0.01819325,0.001188157,0.0009156233,0.007306026],"study_design_scores_gemma":[0.0005982107,0.0000609297,0.006636003,0.0001970096,0.00003544695,0.0000589524,0.00002584526,0.970865,0.00118401,0.003369489,0.01669724,0.00027186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8173018,0.003270208,0.1750538,0.0004879427,0.003317264,0.000226819,0.000163442,0.00005190912,0.0001267512],"genre_scores_gemma":[0.9847913,0.0002724836,0.01449004,0.00005246953,0.0003125882,0.000003468998,0.00002186174,0.00002086266,0.00003497034],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1674894,"threshold_uncertainty_score":0.4735181,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}