{"meta":{"query_hash":"4869e53e5c27","filters":{"venue":"Journal of Prognostics and Health Management"},"cohort_total":4,"direct_labels_cover":0,"predictions_cover":4,"exported":4,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/4869e53e5c27","api":"https://metacan.xera.ac/api/v1/cohort?venue=Journal+of+Prognostics+and+Health+Management"},"results":[{"id":"W3098167416","doi":"10.22215/jphm.v1i1.1349","title":"Health Monitoring of IGBTs with a Rule-Based Sub-safety Recognition Model Using Neural Networks","year":2020,"lang":"en","type":"article","venue":"Journal of Prognostics and Health Management","topic":"Silicon Carbide Semiconductor Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Guangzhou Municipal Science and Technology Project","keywords":"Artificial neural network; Aerospace; Computer science; State (computer science); Power (physics); Electronics; Power grid; Reliability engineering; Engineering; Artificial intelligence; Electrical engineering; Aerospace engineering","score_opus":0.08246651788366458,"score_gpt":0.2843093876675516,"score_spread":0.20184286978388702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098167416","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9299268,0.0037995533,0.06298212,0.0025536427,0.00020324551,0.00042045314,0.000008651759,0.00009157217,0.000013960118],"genre_scores_gemma":[0.9729002,0.0024042998,0.024276465,0.00029122332,0.00009703516,0.0000026544167,0.0000021728868,0.000025720003,2.0542714e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988105,0.000021438156,0.0005914774,0.00010885276,0.00021009218,0.0002576021],"domain_scores_gemma":[0.9992664,0.000031411964,0.0003677868,0.00008489103,0.000089832676,0.00015966798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032516936,0.00013558562,0.00035313494,0.0001383965,0.000057909234,0.000023024242,0.00009984487,0.00004407318,5.842056e-7],"category_scores_gemma":[0.000016636332,0.000120827885,0.0000389353,0.00019631072,0.00003034795,0.0000926104,0.000033231307,0.00028505482,9.1703924e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000060828832,0.000023785944,0.006925577,0.0012401417,0.00007162363,0.000015065404,0.00012700155,0.9259377,0.00017089194,0.000032214877,0.00011791892,0.065277226],"study_design_scores_gemma":[0.0007389022,0.0004643802,0.0036836073,0.00058832916,0.000040564664,0.000011141309,0.00026280398,0.99356973,0.00042373405,0.0000922347,0.00001617461,0.00010839991],"about_ca_topic_score_codex":0.000009065503,"about_ca_topic_score_gemma":0.0000022264398,"teacher_disagreement_score":0.067632,"about_ca_system_score_codex":0.00013415904,"about_ca_system_score_gemma":0.000058544036,"threshold_uncertainty_score":0.49272183},"labels":[],"label_agreement":null},{"id":"W4285395612","doi":"10.22215/jphm.v2i1.3321","title":"Jet Engine Optimal Preventive Maintenance Scheduling Using Golden Section Search and Genetic Algorithm","year":2022,"lang":"en","type":"article","venue":"Journal of Prognostics and Health Management","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Chongqing Municipal Education Commission; Research Manitoba","keywords":"Preventive maintenance; Corrective maintenance; Jet engine; Predictive maintenance; Reliability engineering; Schedule; Aircraft maintenance; Optimal maintenance; Scheduling (production processes); Planned maintenance; Reliability (semiconductor); Engineering; Job shop scheduling; Computer science; Automotive engineering; Operations management; Mechanical engineering; Aeronautics; Power (physics)","score_opus":0.019293107480640403,"score_gpt":0.2665848716997915,"score_spread":0.2472917642191511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285395612","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18156607,0.003133053,0.813829,0.00048108614,0.00048586196,0.00044292348,0.0000067727115,0.000018426814,0.000036764883],"genre_scores_gemma":[0.49886715,0.009266537,0.4915438,0.00008018599,0.0001651538,0.000011800134,0.0000029475389,0.000021841353,0.000040568117],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991006,0.000042812964,0.0003369996,0.00010485932,0.00020273493,0.00021197868],"domain_scores_gemma":[0.9996681,0.0000179267,0.00009271651,0.00005764925,0.0000729675,0.000090605135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006923539,0.00008854201,0.00016215064,0.0001382767,0.0001786955,0.00004047506,0.000057016427,0.00001901902,0.000008051765],"category_scores_gemma":[0.000008782989,0.000085719315,0.000026709333,0.00013673141,0.000020403413,0.00008552599,0.00009191903,0.00024990534,1.1673436e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001060529,0.000033816286,0.00042001242,0.00035992332,0.00005339917,0.000026821137,0.0002910998,0.9207575,0.000010460751,0.00016119899,0.00014458994,0.07773058],"study_design_scores_gemma":[0.00049216027,0.00034273294,0.0052995146,0.00013185968,0.00003658579,0.00013558076,0.0007834596,0.9908249,0.00001004275,0.00016342719,0.0016904755,0.00008926097],"about_ca_topic_score_codex":0.000012243466,"about_ca_topic_score_gemma":0.0000012123868,"teacher_disagreement_score":0.32228523,"about_ca_system_score_codex":0.00020361519,"about_ca_system_score_gemma":0.000029797287,"threshold_uncertainty_score":0.34955323},"labels":[],"label_agreement":null},{"id":"W4285496627","doi":"10.22215/jphm.v2i1.3162","title":"Knowledge Informed Machine Learning using a Weibull-based Loss Function","year":2022,"lang":"en","type":"article","venue":"Journal of Prognostics and Health Management","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Weibull distribution; Prognostics; Computer science; Machine learning; Reliability (semiconductor); Artificial intelligence; Context (archaeology); Field (mathematics); Function (biology); Artificial neural network; Set (abstract data type); Data mining; Statistics; Mathematics","score_opus":0.15824352325968055,"score_gpt":0.42647349817282654,"score_spread":0.268229974913146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285496627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00587362,0.00033070685,0.9901131,0.0026032412,0.00014919104,0.00039373437,0.00003965899,0.000022798353,0.00047396764],"genre_scores_gemma":[0.9638216,0.00008826786,0.03539466,0.00047132286,0.000044553097,0.000024817879,0.000027165055,0.000010900746,0.000116696254],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891436,0.00006776395,0.0005448931,0.000075002405,0.00025118928,0.00014681528],"domain_scores_gemma":[0.9989954,0.00020033953,0.0005117478,0.00006212134,0.000114343056,0.00011599561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084570417,0.00007536222,0.00015934522,0.00013606765,0.00048891635,0.00003105869,0.00006717726,0.000013098919,0.00012492685],"category_scores_gemma":[0.00015342704,0.00007017659,0.00003750622,0.00020294213,0.000024782023,0.00003568518,0.00006908432,0.00023071929,0.0000020962166],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007958907,0.0006220484,0.0020492836,0.000979025,0.00008099307,0.000013495891,0.00024971084,0.003445164,0.0000031846373,0.9534437,0.006005895,0.03302789],"study_design_scores_gemma":[0.0039028844,0.0017494953,0.025596598,0.00024518257,0.00037349833,0.00010397474,0.0012617694,0.58443725,0.000006818361,0.09151089,0.2904768,0.0003348159],"about_ca_topic_score_codex":0.0000038221206,"about_ca_topic_score_gemma":0.0000024952803,"teacher_disagreement_score":0.95794797,"about_ca_system_score_codex":0.00019591943,"about_ca_system_score_gemma":0.00013758049,"threshold_uncertainty_score":0.3760399},"labels":[],"label_agreement":null},{"id":"W4322208384","doi":"10.22215/jphm.v3i1.4154","title":"An Enhanced Joint Indicator for Starter Failure Diagnostics in Auxiliary Power Unit","year":2023,"lang":"en","type":"article","venue":"Journal of Prognostics and Health Management","topic":"Power System Reliability and Maintenance","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada; Life Prediction Technologies (Canada); Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability engineering; Starter; Reliability (semiconductor); Performance indicator; Power (physics); Engineering; Computer science; Automotive engineering","score_opus":0.02651593665485657,"score_gpt":0.28916413190286755,"score_spread":0.262648195248011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322208384","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8013189,0.003685855,0.17369828,0.012462192,0.003508177,0.004250288,0.00012400975,0.00027593217,0.00067640864],"genre_scores_gemma":[0.99186975,0.0033397127,0.004321957,0.00029856013,0.00007496123,0.00004162582,0.000007167406,0.000022672542,0.000023622106],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9986931,0.000029325636,0.00062405784,0.00012215659,0.00018170183,0.00034962787],"domain_scores_gemma":[0.99936634,0.000088624845,0.00014998254,0.00014061773,0.0000660262,0.00018841805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012571493,0.00012197172,0.00029973232,0.0002971547,0.000051930132,0.000037786103,0.000121238314,0.000053311593,0.0000058332444],"category_scores_gemma":[0.000054936885,0.00010768978,0.00004413087,0.00019536387,0.000021650814,0.000108734384,0.00003209136,0.00018033205,0.0000052755045],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004975957,0.0026752804,0.07412146,0.045580678,0.0014750436,0.0012524151,0.02871436,0.051959917,0.00088353234,0.0742917,0.5061066,0.21244137],"study_design_scores_gemma":[0.0063609956,0.0047394955,0.61829937,0.0037864323,0.00013844403,0.00003826365,0.00859413,0.028334042,0.0004161457,0.011192318,0.31708947,0.0010109186],"about_ca_topic_score_codex":0.0000034939014,"about_ca_topic_score_gemma":0.00004115892,"teacher_disagreement_score":0.5441779,"about_ca_system_score_codex":0.00008238989,"about_ca_system_score_gemma":0.000042869975,"threshold_uncertainty_score":0.4391462},"labels":[],"label_agreement":null}]}