{"meta":{"query_hash":"a5e57c3c2e16","filters":{"venue":"Annual Conference of the PHM Society"},"cohort_total":58,"direct_labels_cover":0,"predictions_cover":58,"exported":58,"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/a5e57c3c2e16","api":"https://metacan.xera.ac/api/v1/cohort?venue=Annual+Conference+of+the+PHM+Society"},"results":[{"id":"W2126450366","doi":"10.36001/phmconf.2010.v2i1.1896","title":"An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries","year":2010,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":252,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; Carleton University","funders":"","keywords":"Prognostics; Artificial neural network; Computer science; Recurrent neural network; State of health; Service life; Lithium (medication); Reliability engineering; Artificial intelligence; Machine learning; Engineering; Data mining; Battery (electricity)","score_opus":0.04372108605268748,"score_gpt":0.281104038336771,"score_spread":0.23738295228408351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126450366","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":[0.97825277,0.000041703763,0.01959888,0.00033901076,0.0007427614,0.00036887068,0.00034066653,0.00020376591,0.00011154834],"genre_scores_gemma":[0.9928268,0.000047376758,0.0068472545,0.000026650527,0.0001520619,0.000044016884,0.00001539577,0.000021365835,0.00001904588],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904525,0.000030560113,0.00025693525,0.00017356976,0.00020206813,0.00029162163],"domain_scores_gemma":[0.9989566,0.00013367482,0.000103266735,0.00042069302,0.0003366116,0.000049110087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025874973,0.00013685093,0.00020472701,0.0000182419,0.00010377846,0.000018035265,0.0004813296,0.00015901383,0.000014931019],"category_scores_gemma":[0.00017784667,0.00010944486,0.00013438989,0.00016968309,0.00030664075,0.00033894496,0.00013494707,0.00049176154,4.944048e-7],"study_design_candidate":"simulation_or_modeling","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":[0.0006766213,0.00037991576,0.046257604,0.0012657818,0.0008294433,0.000001205479,0.047963895,0.14617723,0.5882027,0.0103758015,0.051635455,0.10623432],"study_design_scores_gemma":[0.0011536565,0.001849051,0.056184508,0.00037324248,0.000086423664,0.0000075462895,0.021273397,0.7763547,0.12861095,0.008041452,0.0054238494,0.0006412788],"about_ca_topic_score_codex":0.0000072715384,"about_ca_topic_score_gemma":0.000012068843,"teacher_disagreement_score":0.63017744,"about_ca_system_score_codex":0.000024009865,"about_ca_system_score_gemma":0.000042512245,"threshold_uncertainty_score":0.4463032},"labels":[],"label_agreement":null},{"id":"W2183660402","doi":"10.36001/phmconf.2011.v3i1.1993","title":"Physics Based Prognostic Health Management for Thermal Barrier Coating System","year":2011,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"High-Temperature Coating Behaviors","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Thermal barrier coating; Coating; Physics; Materials science; Nanotechnology","score_opus":0.035689688451975655,"score_gpt":0.2404611128652779,"score_spread":0.20477142441330226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2183660402","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":[0.95401025,0.00005563457,0.041401178,0.00012199707,0.0007663317,0.0017832132,0.00032140958,0.00048501755,0.0010549692],"genre_scores_gemma":[0.99030006,0.0000021140418,0.009352671,0.00006772721,0.00005397489,0.00011068156,0.000010375392,0.00003583311,0.00006655312],"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990497,0.000044751607,0.00025666316,0.00016318624,0.00018999234,0.00029566622],"domain_scores_gemma":[0.9992763,0.000053692453,0.000105770494,0.0003302311,0.00017208749,0.00006192495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027198315,0.00017020301,0.00021342667,0.000009161628,0.00016264513,0.000023998442,0.0003456991,0.00006900738,0.000009753262],"category_scores_gemma":[0.000016485417,0.00013192047,0.00020258484,0.00012293232,0.000068101675,0.00009033333,0.00005858579,0.0001627666,0.0000027021583],"study_design_candidate":"bench_or_experimental","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":[0.00012487434,0.0010256914,0.044239603,0.02563492,0.0020120451,0.000012095168,0.5082323,0.027366593,0.26198152,0.07880909,0.027144583,0.023416722],"study_design_scores_gemma":[0.0065641226,0.0012406104,0.100791164,0.007189224,0.001192371,0.000011478857,0.10195678,0.25493416,0.51925975,0.00058414607,0.002783467,0.0034927682],"about_ca_topic_score_codex":0.000024343268,"about_ca_topic_score_gemma":0.0000029310804,"teacher_disagreement_score":0.40627548,"about_ca_system_score_codex":0.00007904525,"about_ca_system_score_gemma":0.00007383193,"threshold_uncertainty_score":0.53795606},"labels":[],"label_agreement":null},{"id":"W2184240449","doi":"10.36001/phmconf.2010.v2i1.1867","title":"Application of Oil Debris Monitoring For Wind Turbine Gearbox Prognostics and Health Management","year":2010,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Gastops (Canada)","funders":"","keywords":"Prognostics; Turbine; Condition monitoring; Debris; Bearing (navigation); Lubrication; Engineering; Marine engineering; Environmental science; Computer science; Reliability engineering; Mechanical engineering; Geology","score_opus":0.014123410872481736,"score_gpt":0.24710816101952665,"score_spread":0.23298475014704492,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2184240449","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":[0.9875548,0.000082726125,0.011370724,0.00042574006,0.00013072761,0.0001713986,0.000043186,0.000027966618,0.00019273786],"genre_scores_gemma":[0.99308527,0.00015594481,0.006618511,0.00000798422,0.000031373696,0.000009280788,0.0000042829397,0.000008348836,0.00007903133],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958116,0.000003554791,0.00013572806,0.00008370916,0.00009110165,0.00010474251],"domain_scores_gemma":[0.9996275,0.000017640725,0.000057028967,0.0001632696,0.00010762952,0.000026968444],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016312653,0.000063716085,0.00011777616,0.00001134201,0.0000464439,0.000011405398,0.00012802835,0.000038870367,7.940848e-7],"category_scores_gemma":[0.0000073057663,0.000051830637,0.00007774826,0.00008077447,0.000042310254,0.000025405376,0.00005291434,0.00009577342,1.964237e-7],"study_design_candidate":"simulation_or_modeling","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":[0.000021006706,0.0003460098,0.15617082,0.006333146,0.0017740646,2.7291372e-7,0.025751226,0.016780974,0.078360714,0.016471159,0.0013053011,0.6966853],"study_design_scores_gemma":[0.0009450802,0.00012573271,0.26394472,0.00022372897,0.00034418324,0.0000022386187,0.0049475264,0.70873237,0.015035003,0.0026874135,0.0025717097,0.0004402903],"about_ca_topic_score_codex":0.000098277116,"about_ca_topic_score_gemma":0.000018201617,"teacher_disagreement_score":0.696245,"about_ca_system_score_codex":0.0000099067975,"about_ca_system_score_gemma":0.00001171171,"threshold_uncertainty_score":0.2113592},"labels":[],"label_agreement":null},{"id":"W2189223380","doi":"10.36001/phmconf.2011.v3i1.1992","title":"Physics based Prognostics of Solder Joints in Avionics","year":2011,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Electronic Packaging and Soldering 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":"Cistel Technology (Canada); Life Prediction Technologies (Canada)","funders":"","keywords":"Prognostics; Avionics; Soldering; Physics; Physics of failure; Reliability engineering; Aeronautics; Forensic engineering; Mechanical engineering; Aerospace engineering; Engineering; Reliability (semiconductor); Composite material; Materials science","score_opus":0.04278353228646314,"score_gpt":0.21468690445962205,"score_spread":0.1719033721731589,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189223380","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":[0.9900309,0.00013469408,0.008134989,0.00009103974,0.00010693199,0.00013890196,0.00002425627,0.00018827844,0.0011500496],"genre_scores_gemma":[0.9967046,0.000063801315,0.0031723978,0.000012975056,0.000005880712,0.000006436172,0.0000011407437,0.000011470252,0.00002132943],"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994277,0.000013507405,0.00016661994,0.00008455329,0.00011230819,0.00019533551],"domain_scores_gemma":[0.99952614,0.000032736603,0.00005331828,0.00027900116,0.0000955158,0.000013285605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001179538,0.00009746707,0.00015980388,0.000011882878,0.00002154514,0.000004033815,0.00031903054,0.00009246549,0.0000056942963],"category_scores_gemma":[0.00004987084,0.00007817386,0.00010638899,0.00016154099,0.00015622942,0.00005824857,0.00006619075,0.00023876598,9.936751e-7],"study_design_candidate":"bench_or_experimental","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":[0.000094457704,0.003067739,0.2694691,0.0048885085,0.0015231493,0.000012969416,0.29551178,0.041784495,0.16107988,0.12049195,0.019549368,0.08252661],"study_design_scores_gemma":[0.00096005213,0.00019457767,0.03665525,0.0006504528,0.00007465401,0.000002173905,0.0053033903,0.14387406,0.7768827,0.034703232,0.00015788675,0.00054156274],"about_ca_topic_score_codex":0.000032462638,"about_ca_topic_score_gemma":0.000009033498,"teacher_disagreement_score":0.6158028,"about_ca_system_score_codex":0.000026337086,"about_ca_system_score_gemma":0.00007034249,"threshold_uncertainty_score":0.31878376},"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,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_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","score_opus":0.025290178323483178,"score_gpt":0.22357158286180134,"score_spread":0.19828140453831816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2527718235","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32695723,0.0003669022,0.6688479,0.0008947762,0.000803351,0.0017229008,0.00024100248,0.00007458711,0.00009133856],"genre_scores_gemma":[0.9897588,0.000029181,0.009862039,0.000029926006,0.00006222484,0.0001249698,0.000008735228,0.000018282844,0.00010587833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992217,0.000019640045,0.0002594441,0.00014865967,0.00018493798,0.00016563706],"domain_scores_gemma":[0.99921477,0.00008656298,0.000053155443,0.00035293677,0.00021392986,0.00007863619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035802496,0.00012779156,0.00019297209,0.000016974209,0.000030052532,0.000022590984,0.00039894902,0.00009680107,0.0000026679172],"category_scores_gemma":[0.00014863483,0.0000840971,0.00013096575,0.0002068167,0.000041516436,0.00006811456,0.00006676791,0.000113579066,0.0000013045478],"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":[0.00003639112,0.00026328804,0.0007491025,0.00056722044,0.0003194475,2.4456838e-7,0.036388192,0.6842518,0.19752651,0.017438458,0.058695465,0.0037638526],"study_design_scores_gemma":[0.0012176745,0.00018063541,0.0051677213,0.000105082356,0.00013278452,0.0000024124988,0.006087372,0.7739659,0.20289072,0.004279701,0.0054705655,0.0004994004],"about_ca_topic_score_codex":0.00014149274,"about_ca_topic_score_gemma":0.000050027324,"teacher_disagreement_score":0.66280156,"about_ca_system_score_codex":0.000050164188,"about_ca_system_score_gemma":0.0001037946,"threshold_uncertainty_score":0.34293804},"labels":[],"label_agreement":null},{"id":"W2554318111","doi":"10.36001/phmconf.2010.v2i1.1748","title":"Robustness of a Structural Health Monitoring System under Drop-weight Impact Loading in Composites","year":2010,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Materials science; Composite number; Structural health monitoring; Robustness (evolution); Composite material; Transducer; Drop (telecommunication); Electrical impedance; Acoustics; Structural engineering; Engineering; Electrical engineering","score_opus":0.01275414348348307,"score_gpt":0.2485143846839068,"score_spread":0.23576024120042371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2554318111","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":[0.99245375,0.00009480047,0.0066258204,0.00006493932,0.00050151965,0.00012586369,0.000044671655,0.000032983702,0.000055655484],"genre_scores_gemma":[0.99845636,0.000014685248,0.0014320721,0.0000025422453,0.00007091903,0.0000024872031,0.0000032825353,0.000012640037,0.0000050172257],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927926,0.000022531374,0.00025572584,0.00009452325,0.00015421599,0.00019375757],"domain_scores_gemma":[0.999499,0.000054986787,0.000109793364,0.00016742782,0.000127139,0.00004165657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018904339,0.00011421694,0.00021754106,0.000019340152,0.00005936324,0.000021209342,0.00021015518,0.0000713143,0.000006686393],"category_scores_gemma":[0.0000118095695,0.00008236874,0.00011708576,0.000145989,0.00005961315,0.00012831307,0.000039964794,0.0002904165,2.334032e-7],"study_design_candidate":"simulation_or_modeling","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":[0.0000091936745,0.000029592824,0.030660383,0.00089440605,0.00014068243,3.0221202e-7,0.009655821,0.36692604,0.5883971,0.0016929355,0.00010943312,0.0014841197],"study_design_scores_gemma":[0.00032498315,0.00003561893,0.076842085,0.00041663885,0.000017443448,0.000008078522,0.0051257843,0.8735525,0.04336512,0.00014545443,0.000001948072,0.00016433145],"about_ca_topic_score_codex":0.00019334449,"about_ca_topic_score_gemma":0.000015550828,"teacher_disagreement_score":0.54503196,"about_ca_system_score_codex":0.000090956826,"about_ca_system_score_gemma":0.00007475161,"threshold_uncertainty_score":0.33588997},"labels":[],"label_agreement":null},{"id":"W2597873886","doi":"10.36001/phmconf.2013.v5i1.2333","title":"PHM for Astronauts – A New Application","year":2013,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Engineering Applied Research","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Space Agency","funders":"","keywords":"Environmental science; Computer science","score_opus":0.01474646998081539,"score_gpt":0.23052797666710342,"score_spread":0.21578150668628804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2597873886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31749696,0.0001975502,0.6728763,0.0023863418,0.00030814417,0.0020981338,0.00010645547,0.0004731533,0.0040569487],"genre_scores_gemma":[0.9917209,0.000016881511,0.007133646,0.00002206133,0.00008756799,0.0001725963,0.0000064472474,0.000023206478,0.0008166683],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936837,0.000005205749,0.00012738023,0.000112053785,0.0001549041,0.00023211118],"domain_scores_gemma":[0.99942946,0.000056290875,0.000023968074,0.0002763864,0.00014691075,0.000066961824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000095204945,0.000101306905,0.00011345109,0.000010110269,0.000042518775,0.00002621217,0.00035578775,0.00007460897,0.000041237803],"category_scores_gemma":[0.000019790834,0.00008032338,0.00012380625,0.000110163186,0.000047218233,0.00010341248,0.000060665025,0.00016019239,0.000034017212],"study_design_candidate":"simulation_or_modeling","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":[0.000010527262,0.00008739462,0.00037828903,0.000753945,0.00039869378,7.082355e-8,0.013487781,0.086626366,0.3868757,0.01749006,0.38951907,0.104372114],"study_design_scores_gemma":[0.0011117968,0.000090140755,0.0092367595,0.00009180464,0.000045544417,0.0000026451694,0.0026664268,0.8440646,0.091406554,0.010182395,0.040533394,0.00056792685],"about_ca_topic_score_codex":0.0000860819,"about_ca_topic_score_gemma":0.0000023632992,"teacher_disagreement_score":0.75743824,"about_ca_system_score_codex":0.000034793997,"about_ca_system_score_gemma":0.000047492,"threshold_uncertainty_score":0.32754922},"labels":[],"label_agreement":null},{"id":"W2622264126","doi":"10.36001/phmconf.2014.v6i1.2420","title":"A Novel Linear Polarization Resistance Corrosion Sensing Methodology for Aircraft Structure","year":2014,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Analytical Chemistry and Sensors","field":"Chemical Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Naval Air Systems Command; Office of Naval Research; U.S. Navy; Shanghai Jiao Tong University; Banaras Hindu University; Office of Naval Research Global; York University; Massachusetts Institute of Technology; Georgia Institute of Technology","keywords":"Corrosion; Materials science; Electrode; Polarization (electrochemistry); Composite material; Pitting corrosion; Metallurgy","score_opus":0.04206369925296189,"score_gpt":0.27862106274969106,"score_spread":0.23655736349672918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2622264126","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10528751,0.000011859378,0.8932278,0.0009100821,0.00009363067,0.00009668217,0.0001274538,0.000037826467,0.0002071429],"genre_scores_gemma":[0.92384714,0.0000018671934,0.07405455,0.00020444776,0.000116708004,7.0128317e-7,0.000021341388,0.000013115065,0.0017401219],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99923694,0.000036860172,0.00020672956,0.00019468526,0.00013524719,0.00018952547],"domain_scores_gemma":[0.9988851,0.00040621564,0.000112215086,0.0002346955,0.00031395626,0.00004784221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021477698,0.00012249191,0.00022076152,0.000005373795,0.000102726284,0.0000087512735,0.00020004742,0.00019829288,0.000016708793],"category_scores_gemma":[0.0011142148,0.00009213733,0.00020199452,0.00010543688,0.00012465261,0.000058124486,0.00006692355,0.000221676,6.131365e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027057109,0.000010207239,0.000031099058,0.00010400634,0.000018246355,3.0872055e-8,0.0010705842,0.0007050005,0.9932731,0.0043312204,0.0002645087,0.00016496518],"study_design_scores_gemma":[0.00043319937,0.000020171778,0.00009353146,0.00008229928,0.00007198401,0.0000029487608,0.0006845124,0.2841692,0.70896703,0.0031431303,0.0021450757,0.00018691421],"about_ca_topic_score_codex":0.0000144276355,"about_ca_topic_score_gemma":0.0000065662284,"teacher_disagreement_score":0.8191733,"about_ca_system_score_codex":0.000022718348,"about_ca_system_score_gemma":0.000028222272,"threshold_uncertainty_score":0.37572512},"labels":[],"label_agreement":null},{"id":"W2625303763","doi":"10.36001/phmconf.2014.v6i1.2501","title":"Investigating Vibration Properties of a Planetary Gear Set with a Cracked Tooth in a Planet Gear","year":2014,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":3,"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 Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Planet; Vibration; Gear train; Spiral bevel gear; Displacement (psychology); Fault (geology); SIGNAL (programming language); Structural engineering; Non-circular gear; Engineering; Computer science; Acoustics; Geology; Physics","score_opus":0.017696252860178813,"score_gpt":0.18696900352586618,"score_spread":0.16927275066568737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2625303763","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":[0.9991714,0.00002043643,0.00030935305,0.00009281429,0.000017097838,0.00008059863,0.000040068484,0.000023469962,0.00024476607],"genre_scores_gemma":[0.9989992,0.000009542738,0.0008780569,0.00003590535,0.000013134067,0.0000033291897,0.000028018529,0.0000092392875,0.000023608596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994187,0.000034091336,0.0001765341,0.00009358737,0.00015387691,0.00012326406],"domain_scores_gemma":[0.9996699,0.000021735266,0.00006360753,0.0001667162,0.00005421622,0.000023818546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016387794,0.000093338625,0.00018045811,0.000019631514,0.000030149286,0.000017520899,0.00016228123,0.000054452346,0.000008224558],"category_scores_gemma":[0.000022455863,0.00006242149,0.000046505058,0.00012846556,0.00010140188,0.00009379428,0.000037081252,0.00014508465,0.0000014280589],"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":[0.000060671024,0.000141239,0.13004544,0.0020203032,0.00082993135,0.0000015080915,0.1415615,0.53221494,0.18734336,0.0016260513,0.00163956,0.0025154827],"study_design_scores_gemma":[0.00034263745,0.00008837871,0.031074148,0.0002619345,0.00005325828,0.0000026417636,0.0035522336,0.95837885,0.0057161436,0.0002566072,0.00009010081,0.00018305054],"about_ca_topic_score_codex":0.00075388415,"about_ca_topic_score_gemma":0.0003224102,"teacher_disagreement_score":0.4261639,"about_ca_system_score_codex":0.000010971143,"about_ca_system_score_gemma":0.000034078133,"threshold_uncertainty_score":0.25454745},"labels":[],"label_agreement":null},{"id":"W2792537006","doi":"10.36001/phmconf.2013.v5i1.2295","title":"Novelty detection in airport baggage conveyor gear-motors using Synchro-squeezing transform and Self-organizing maps","year":2013,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"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 Waterloo","funders":"Mitacs","keywords":"Synchro; Context (archaeology); Wavelet transform; SIGNAL (programming language); Computer science; Thresholding; Continuous wavelet transform; Engineering; Artificial intelligence; Pattern recognition (psychology); Wavelet; Discrete wavelet transform; Image (mathematics)","score_opus":0.01060365780289911,"score_gpt":0.22913757124914788,"score_spread":0.21853391344624878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792537006","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":[0.9941993,0.000058757887,0.004057603,0.00013950205,0.00011068489,0.0005315806,0.000025967305,0.00029178863,0.00058484497],"genre_scores_gemma":[0.9959878,0.00015031015,0.0036850271,0.000060038463,0.000031769752,0.000034328208,0.0000024617943,0.00003631545,0.000011981926],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.998987,0.000036770492,0.0003177016,0.0002024733,0.00017578933,0.00028025673],"domain_scores_gemma":[0.99944144,0.000069524045,0.0000741663,0.00022505423,0.00013130954,0.000058509704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030680906,0.00020751072,0.0002576039,0.000041590036,0.00010011631,0.000044307788,0.00023117568,0.000164759,0.000039944676],"category_scores_gemma":[0.000040470695,0.00017945177,0.00011430347,0.00023169782,0.00008569609,0.00049332576,0.000079909565,0.00037612233,0.0000029733385],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015194625,0.0005006128,0.09815288,0.0024502284,0.0006084062,0.00000718081,0.09599802,0.0015034385,0.7439423,0.00065888086,0.003015698,0.053147115],"study_design_scores_gemma":[0.0010112348,0.000112738926,0.07871795,0.0005835627,0.00012022512,0.000043480264,0.0065955734,0.31621367,0.5927068,0.0020144929,0.0008940181,0.0009862486],"about_ca_topic_score_codex":0.0014132847,"about_ca_topic_score_gemma":0.00014954012,"teacher_disagreement_score":0.31471023,"about_ca_system_score_codex":0.00014178528,"about_ca_system_score_gemma":0.00004186724,"threshold_uncertainty_score":0.7317831},"labels":[],"label_agreement":null},{"id":"W2892631695","doi":"10.36001/phmconf.2018.v10i1.480","title":"Extensible System for Optical Character Recognition of Maintenance Documents","year":2018,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Image and Object Detection Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Optical character recognition; Computer science; Character (mathematics); Fidelity; Extensibility; Analytics; Database; Software engineering; World Wide Web; Operating system; Artificial intelligence; Image (mathematics); Telecommunications","score_opus":0.024926052206478484,"score_gpt":0.26263120740114915,"score_spread":0.23770515519467067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2892631695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10996558,0.00001402334,0.8841712,0.00086289743,0.00070488814,0.000568771,0.00006545716,0.00016412423,0.0034830591],"genre_scores_gemma":[0.9724941,0.000008531291,0.026810827,0.00018528494,0.0000737153,0.00002678851,0.0000013813489,0.00000517223,0.0003941973],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991847,0.000029480358,0.00023278952,0.00019546665,0.00017913057,0.00017841642],"domain_scores_gemma":[0.99809366,0.00004969458,0.00019649992,0.00037184526,0.0012601094,0.000028203349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003528499,0.00008800735,0.00016205703,0.000017623055,0.00010491155,0.00003973868,0.00056629017,0.0000695357,0.0000069469725],"category_scores_gemma":[0.000062960666,0.000062198786,0.00019522797,0.00016409947,0.0001933851,0.00044131602,0.00018484125,0.00007747093,0.0000076017036],"study_design_candidate":"bench_or_experimental","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":[0.00024972987,0.00052403385,0.00029991896,0.0012549609,0.00040202704,0.0000025837423,0.030442543,7.943373e-7,0.32801342,0.06487186,0.048615813,0.5253223],"study_design_scores_gemma":[0.00023920636,0.0002502345,0.0006057793,0.00017990639,0.000014208972,0.0000091988395,0.0006537921,0.001402199,0.9911033,0.0044833217,0.00095858093,0.0001003089],"about_ca_topic_score_codex":0.000030893865,"about_ca_topic_score_gemma":0.0000021067026,"teacher_disagreement_score":0.8625285,"about_ca_system_score_codex":0.000032031712,"about_ca_system_score_gemma":0.00006983837,"threshold_uncertainty_score":0.25363928},"labels":[],"label_agreement":null},{"id":"W2893702958","doi":"10.36001/phmconf.2018.v10i1.500","title":"New Adaptive Prognostics Approach Based on Hybrid Feature Selection with Application to Point Machine Monitoring","year":2018,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Machine Fault Diagnosis Techniques","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":"Advanced Micro Devices (Canada)","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Prognostics; Feature (linguistics); Feature selection; Component (thermodynamics); Field (mathematics); Computer science; Point (geometry); Selection (genetic algorithm); Model selection; Degradation (telecommunications); Artificial intelligence; Pattern recognition (psychology); Engineering; Data mining; Mathematics","score_opus":0.010450203018691913,"score_gpt":0.2460267562699881,"score_spread":0.2355765532512962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893702958","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":[0.055566344,0.000018366118,0.93994236,0.00078551675,0.00009607637,0.0010390229,0.00007010314,0.00048595364,0.0019962618],"genre_scores_gemma":[0.8896941,0.0000048444276,0.10981954,0.000109752786,0.00018896363,0.00009351555,0.000009280051,0.0000286588,0.000051346848],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918234,0.0000234892,0.00012496205,0.00021882686,0.00026048376,0.00018990393],"domain_scores_gemma":[0.9991649,0.000042362528,0.000060610295,0.00029239024,0.00036427449,0.00007546311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012722974,0.0001881859,0.00015832334,0.000026780404,0.00008931287,0.000029316969,0.00028997217,0.000073522366,0.0000056741173],"category_scores_gemma":[0.000038736285,0.00013577682,0.000077596684,0.00028575156,0.000047114798,0.00009778295,0.00004747334,0.00028605032,0.0000036535398],"study_design_candidate":"simulation_or_modeling","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":[0.0007443115,0.0012701377,0.04997185,0.00069396466,0.00088770885,0.0000017762272,0.029426033,0.13175409,0.046620674,0.0037661565,0.42386717,0.31099612],"study_design_scores_gemma":[0.00035746407,0.0007662531,0.0096507445,0.00021679576,0.00006167529,0.000004495854,0.000424194,0.6093023,0.37705997,0.00026108592,0.0015300923,0.00036493514],"about_ca_topic_score_codex":0.00012159363,"about_ca_topic_score_gemma":0.000012248321,"teacher_disagreement_score":0.8341277,"about_ca_system_score_codex":0.000091095506,"about_ca_system_score_gemma":0.000058685848,"threshold_uncertainty_score":0.5536818},"labels":[],"label_agreement":null},{"id":"W2893913719","doi":"10.36001/phmconf.2018.v10i1.495","title":"Ensemble Learning Based Surrogate Modeling for Gas Turbine Blisk Temperature Predictions","year":2018,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Turbomachinery Performance and Optimization","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":"Life Prediction Technologies (Canada); University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Surrogate model; Computer science; Computational fluid dynamics; Computational complexity theory; Ensemble forecasting; Ensemble learning; Artificial intelligence; Machine learning; Algorithm; Engineering","score_opus":0.014938976073894557,"score_gpt":0.22246161003076706,"score_spread":0.2075226339568725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893913719","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":[0.87084967,0.000059519432,0.12687944,0.00033287497,0.00047560778,0.00028367364,0.00010218387,0.00023567652,0.00078136474],"genre_scores_gemma":[0.99693114,0.000047572204,0.0023542775,0.00007882866,0.00020035708,0.000020563197,0.00004419619,0.000021922755,0.00030116105],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938357,0.000016014726,0.00016330756,0.00012254786,0.00011987863,0.00019465438],"domain_scores_gemma":[0.9993317,0.000029346404,0.00004112356,0.00017370917,0.0003898808,0.00003423768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001462352,0.00012390327,0.00012790928,0.000018720617,0.00025916658,0.00003855605,0.0001748145,0.00010638012,0.000026583384],"category_scores_gemma":[0.000037707494,0.00009459745,0.0001499978,0.00017339649,0.00006119734,0.00026280587,0.00002953245,0.00022108076,0.0000028439551],"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":[0.00001103184,0.000012539517,0.00024752136,0.000054354758,0.00003410068,2.7021436e-8,0.0029302007,0.98766476,0.0059741354,0.000053389784,0.0027535078,0.00026444282],"study_design_scores_gemma":[0.00030705635,0.00006475486,0.000088471395,0.000051859606,0.000030145064,7.6970906e-7,0.0006947804,0.9891176,0.008800855,0.000098437486,0.00063917204,0.000106090745],"about_ca_topic_score_codex":0.000011204082,"about_ca_topic_score_gemma":0.000014450643,"teacher_disagreement_score":0.12608147,"about_ca_system_score_codex":0.000023759514,"about_ca_system_score_gemma":0.000051212504,"threshold_uncertainty_score":0.3857572},"labels":[],"label_agreement":null},{"id":"W2894413419","doi":"10.36001/phmconf.2018.v10i1.470","title":"Data Analytics for Performance Monitoring of Gas Turbine Engine","year":2018,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Technical Engine Diagnostics and Monitoring","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Life Prediction Technologies (Canada)","funders":"","keywords":"Prognostics; Gas compressor; Turbine; Term (time); Automotive engineering; Gas turbines; Gas engine; Power (physics); Computer science; Engineering; Reliability engineering; Environmental science; Mechanical engineering","score_opus":0.058415307263812485,"score_gpt":0.28183272516794905,"score_spread":0.22341741790413655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894413419","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":[0.97826,0.0002132163,0.01917376,0.00012078784,0.0010487068,0.00021890175,0.0005440667,0.00010768844,0.00031290756],"genre_scores_gemma":[0.99254423,0.0003088711,0.0067043374,0.00000429931,0.00035629197,0.0000062095983,0.000011759865,0.000018037335,0.00004593598],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993149,0.0000038174912,0.00022499797,0.0001240392,0.00014234633,0.00018992418],"domain_scores_gemma":[0.9989115,0.00010635347,0.000057889723,0.00057970185,0.00030792545,0.000036633228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018252144,0.00011129925,0.00018172998,0.000013603665,0.00004794193,0.000009967054,0.0006813069,0.00006966644,0.000004978674],"category_scores_gemma":[0.0001512733,0.00008858046,0.000095049356,0.0001463411,0.00010779403,0.00013581797,0.00021346222,0.00012959697,8.7229716e-7],"study_design_candidate":"bench_or_experimental","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":[0.00016946754,0.0009153267,0.09583168,0.008048581,0.0033664182,0.0000021027013,0.025143154,0.12670419,0.28404093,0.0057926364,0.13415024,0.31583527],"study_design_scores_gemma":[0.00059412513,0.00022364614,0.014705083,0.00048547986,0.00016027178,0.0000014812416,0.00094804575,0.52304053,0.44783986,0.00026314057,0.011379181,0.00035913446],"about_ca_topic_score_codex":0.0000087526505,"about_ca_topic_score_gemma":8.9680725e-7,"teacher_disagreement_score":0.39633638,"about_ca_system_score_codex":0.000017757693,"about_ca_system_score_gemma":0.000028188037,"threshold_uncertainty_score":0.36122063},"labels":[],"label_agreement":null},{"id":"W2975044290","doi":"10.36001/phmconf.2019.v11i1.831","title":"Inter-Turn Short-Circuit Failure of PMSM Indicator based on Kalman Filtering in Operational Behavior","year":2019,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"","keywords":"Kalman filter; Residual; Short circuit; Catastrophic failure; Rotor (electric); Fault (geology); Fault detection and isolation; Voltage; Computer science; SIGNAL (programming language); Avionics; Engineering; Electronic circuit; Automotive engineering; Control theory (sociology); Electrical engineering; Artificial intelligence","score_opus":0.015921877123429917,"score_gpt":0.26166803581713766,"score_spread":0.24574615869370775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975044290","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":[0.9974094,0.000009808084,0.00079954916,0.00017965832,0.00009311731,0.00037119733,0.00016296464,0.000072267736,0.0009020504],"genre_scores_gemma":[0.9989893,0.000006219887,0.0007548251,0.00010458032,0.000017817014,0.00006371762,0.000019038514,0.000020319116,0.000024191904],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991287,0.000032847616,0.00028292992,0.00016294827,0.00023243437,0.0001601321],"domain_scores_gemma":[0.9994158,0.000067621746,0.00005361708,0.00035387473,0.00007680505,0.000032274707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018443295,0.00015171083,0.00022696774,0.000046956815,0.000020117663,0.00001754832,0.00045801993,0.000119512086,0.00025266496],"category_scores_gemma":[0.000027548065,0.00012531069,0.00016143879,0.000121345634,0.000061302686,0.00014079109,0.000087863475,0.0003132079,0.000005584975],"study_design_candidate":"observational","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":[0.000024330997,0.0007839083,0.7136539,0.000747516,0.0001501475,0.0000037340906,0.016019948,0.009306434,0.23312138,0.0030483692,0.015713321,0.007426992],"study_design_scores_gemma":[0.0011017703,0.0004161865,0.38296267,0.001218914,0.000070548784,0.000004678394,0.002107078,0.110901564,0.49841863,0.00034541902,0.001593176,0.000859371],"about_ca_topic_score_codex":0.000053830587,"about_ca_topic_score_gemma":0.000038958467,"teacher_disagreement_score":0.33069125,"about_ca_system_score_codex":0.000058736903,"about_ca_system_score_gemma":0.000048759157,"threshold_uncertainty_score":0.5110022},"labels":[],"label_agreement":null},{"id":"W2975326508","doi":"10.36001/phmconf.2019.v11i1.796","title":"Prognostics Model to Predict Brake Rotor Thickness Variation","year":2019,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Brake Systems and Friction Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Prognostics; Rotor (electric); Brake; Vibration; Envelope (radar); Crankshaft; Amplitude; Automotive engineering; Disc brake; Control theory (sociology); Root mean square; Frequency domain; Materials science; Engineering; Structural engineering; Mathematics; Acoustics; Physics; Computer science; Mechanical engineering","score_opus":0.015673031508897324,"score_gpt":0.21026284598900508,"score_spread":0.19458981448010776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975326508","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":[0.69008815,0.000044659188,0.2980608,0.0009169827,0.0010656477,0.001154647,0.00027080133,0.00028937455,0.008108946],"genre_scores_gemma":[0.9974211,0.00000960933,0.0011148032,0.00009966212,0.000053413736,0.000022628124,0.0000041885787,0.0000141260825,0.0012604599],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992789,0.000020910486,0.00020135897,0.00012594217,0.00023054192,0.00014232431],"domain_scores_gemma":[0.9993274,0.000025694893,0.00005401276,0.00028168634,0.00026063313,0.000050581428],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018144133,0.00010358849,0.0001677862,0.00001608236,0.000049099926,0.000030794126,0.00023567502,0.00009927014,0.000052404463],"category_scores_gemma":[0.000029823668,0.000078402816,0.00017142943,0.00022237303,0.000019022194,0.00013705104,0.000056175075,0.00013479107,0.000035735004],"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":[0.000013164722,0.00010171449,0.01109056,0.0004610588,0.0006419022,1.833457e-7,0.057411376,0.8642502,0.025881719,0.011204954,0.026637305,0.0023058662],"study_design_scores_gemma":[0.0001546381,0.000021524846,0.008662281,0.00005338205,0.00004780512,8.8322537e-7,0.0012344639,0.9875547,0.0005805795,0.00025195358,0.0013032918,0.00013445722],"about_ca_topic_score_codex":0.000043663935,"about_ca_topic_score_gemma":0.000008555739,"teacher_disagreement_score":0.30733296,"about_ca_system_score_codex":0.0000335414,"about_ca_system_score_gemma":0.00004676545,"threshold_uncertainty_score":0.3197174},"labels":[],"label_agreement":null},{"id":"W2975721124","doi":"10.36001/phmconf.2019.v11i1.857","title":"Model-based On-board Decision Making for Autonomous Aircraft","year":2019,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Institute for Advanced Research","keywords":"Prognostics; Component (thermodynamics); Backtracking; Contingency; Computer science; Fault (geology); Construct (python library); Fault detection and isolation; Contingency plan; Software; Engineering; Systems engineering; Artificial intelligence; Reliability engineering; Data mining; Computer security","score_opus":0.02731470898130036,"score_gpt":0.27185078298117293,"score_spread":0.24453607399987257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975721124","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":[0.06521535,0.000024531742,0.9312198,0.0016316703,0.0003619706,0.00034431298,0.00004771919,0.00009533889,0.0010592914],"genre_scores_gemma":[0.85728043,0.0000012300727,0.14111865,0.0012119653,0.0000228691,0.000013619168,0.0000022867423,0.000010342184,0.00033862703],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986894,0.00004371079,0.00024023135,0.0003813901,0.00033134292,0.00031388656],"domain_scores_gemma":[0.9980948,0.00059743697,0.00018673655,0.0007756321,0.0002964557,0.00004894272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005202403,0.00016954636,0.00022084186,0.000025807882,0.00020446611,0.00010190073,0.0013774605,0.00011689161,0.000012419794],"category_scores_gemma":[0.000075287695,0.00012545624,0.0003375078,0.00017220802,0.00006024742,0.00024273653,0.00022870497,0.0002133349,0.00001761682],"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":[0.00013240335,0.00020194537,0.005386521,0.00022492232,0.00008332103,0.0000010799373,0.019779548,0.83138454,0.0010412051,0.040453218,0.026796453,0.07451484],"study_design_scores_gemma":[0.00037719076,0.00015280415,0.00034789785,0.00025984863,0.00000890075,6.190196e-7,0.00014576611,0.9868427,0.0006510139,0.0103332065,0.0007075469,0.00017249293],"about_ca_topic_score_codex":0.000012612798,"about_ca_topic_score_gemma":0.0000018923059,"teacher_disagreement_score":0.7920651,"about_ca_system_score_codex":0.000049370872,"about_ca_system_score_gemma":0.000409257,"threshold_uncertainty_score":0.5115957},"labels":[],"label_agreement":null},{"id":"W2976348751","doi":"10.36001/phmconf.2019.v11i1.765","title":"Bearing Condition Monitoring based on the Indicator Generated in Time-frequency Domain","year":2019,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Condition monitoring; Bearing (navigation); Frequency domain; Computer science; Fault (geology); Time domain; Fault detection and isolation; Vibration; SIGNAL (programming language); Gaussian; Real-time computing; Control theory (sociology); Artificial intelligence; Engineering; Control (management); Acoustics; Actuator; Computer vision","score_opus":0.010972231213690897,"score_gpt":0.24929905925161588,"score_spread":0.238326828037925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976348751","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":[0.9963922,0.000021470289,0.00015587716,0.0004260248,0.000118889046,0.00036901308,0.00004513636,0.00014343364,0.0023279837],"genre_scores_gemma":[0.9987607,0.000017162622,0.00094680267,0.00011773155,0.00003770259,0.000056430308,0.000009007554,0.000021809517,0.000032675172],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990956,0.000077833494,0.00020834267,0.00015166974,0.00026581585,0.00020074082],"domain_scores_gemma":[0.999298,0.00015630393,0.00006902774,0.0003912713,0.00005742413,0.000028001232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004007597,0.00014903201,0.00016625265,0.000032406508,0.000055762674,0.000036796977,0.0005237774,0.000111147434,0.00015867068],"category_scores_gemma":[0.000041356016,0.00010152237,0.00011614381,0.00021042369,0.000060179667,0.000117282434,0.000064891494,0.0003697251,0.000035659545],"study_design_candidate":"bench_or_experimental","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":[0.000022390708,0.00031464058,0.47868916,0.00030750874,0.00019361352,0.0000031239788,0.013825476,0.011721373,0.47258952,0.0066539412,0.014354137,0.0013251003],"study_design_scores_gemma":[0.0012600394,0.00020131719,0.22662978,0.0010982255,0.00003958148,0.0000019647669,0.0019765713,0.120060235,0.6405025,0.006831639,0.00057092763,0.0008271844],"about_ca_topic_score_codex":0.00007398638,"about_ca_topic_score_gemma":0.0000029620996,"teacher_disagreement_score":0.2520594,"about_ca_system_score_codex":0.000096308235,"about_ca_system_score_gemma":0.000040038485,"threshold_uncertainty_score":0.41399622},"labels":[],"label_agreement":null},{"id":"W2976519442","doi":"10.36001/phmconf.2019.v11i1.868","title":"Automating Visual Inspection with Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Pattern recognition (psychology); Context (archaeology); Segmentation; Object detection; Deep learning; Computer vision; Task (project management); Set (abstract data type); Convolution (computer science); Contextual image classification; Image (mathematics); Artificial neural network","score_opus":0.010700467205941325,"score_gpt":0.21091557566802552,"score_spread":0.20021510846208418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976519442","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":[0.98999304,0.000027746548,0.007604831,0.000030409723,0.00088139396,0.00020181383,0.000008241364,0.00019704759,0.0010554581],"genre_scores_gemma":[0.9996214,0.000002780047,0.000044322675,0.000021815002,0.0001642495,0.000005845813,0.0000029339653,0.000012544629,0.00012409742],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933225,0.000032849937,0.0001768131,0.00010741868,0.0001992694,0.00015137056],"domain_scores_gemma":[0.9995719,0.000037659498,0.00007767725,0.00012690961,0.00015912771,0.000026678194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015040582,0.00010338335,0.00014451536,0.000012467089,0.00010034594,0.000030106989,0.0000986042,0.00011176604,0.00003940083],"category_scores_gemma":[0.000008904641,0.000070476926,0.00011572554,0.00018392401,0.000048981823,0.0001813401,0.000032181586,0.00024487168,0.0000082977585],"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":[0.00015035237,0.00011375897,0.057044376,0.00032585775,0.0006105083,0.0000013121306,0.011646101,0.87008876,0.02106443,0.0028007485,0.011602529,0.02455128],"study_design_scores_gemma":[0.00039370303,0.00011240039,0.017900625,0.00006916204,0.00001267763,0.0000115975145,0.0015716237,0.9786266,0.0009871845,0.00001557659,0.00017664688,0.00012216836],"about_ca_topic_score_codex":0.00006008611,"about_ca_topic_score_gemma":0.000006353497,"teacher_disagreement_score":0.10853789,"about_ca_system_score_codex":0.00004938402,"about_ca_system_score_gemma":0.000027908854,"threshold_uncertainty_score":0.28739655},"labels":[],"label_agreement":null},{"id":"W2977179614","doi":"10.36001/phmconf.2019.v11i1.904","title":"Ensemble Linear Regression and Paris’ Law Based Methods for Structure Fatigue Crack Length Estimation and Prediction Using Ultrasonic Wave Data","year":2019,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Amplitude; Kurtosis; Ultrasonic sensor; Paris' law; Linear regression; Range (aeronautics); Structural engineering; Fracture mechanics; Mathematics; Computer science; Statistics; Materials science; Engineering; Acoustics; Crack closure; Physics","score_opus":0.06047587164199726,"score_gpt":0.31837571685997573,"score_spread":0.2578998452179785,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2977179614","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.43649256,0.00011976051,0.56223565,0.00009394373,0.00015222254,0.00036575992,0.00047778361,0.000032880696,0.000029435161],"genre_scores_gemma":[0.8081374,0.00006868402,0.19161373,0.00002141076,0.000026177366,0.0000024279534,0.00010891318,0.000012918605,0.000008333132],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936515,0.000042387845,0.00017065604,0.0001946976,0.00010123831,0.00012589636],"domain_scores_gemma":[0.9992589,0.00020149238,0.00009061341,0.0002855936,0.00013226908,0.000031156516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031352427,0.00012048366,0.00015520277,0.000009162708,0.00011069947,0.000040083196,0.00011399048,0.0001204053,0.0000072113585],"category_scores_gemma":[0.00007138507,0.000088187895,0.000037820177,0.000056984405,0.00006310709,0.0003596028,0.000059903858,0.00015023851,9.344604e-8],"study_design_candidate":"simulation_or_modeling","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":[0.00004691759,0.00004523909,0.0009043868,0.0015985367,0.00021324474,1.046204e-7,0.0073859743,0.09797077,0.82715577,0.002711325,0.0007654609,0.061202276],"study_design_scores_gemma":[0.00030975856,0.000034552224,0.0004876931,0.00013791262,0.0000685711,0.0000023740708,0.00048085098,0.9698227,0.027320422,0.0011077909,0.00013359124,0.0000937638],"about_ca_topic_score_codex":0.000027306722,"about_ca_topic_score_gemma":0.0000031345935,"teacher_disagreement_score":0.871852,"about_ca_system_score_codex":0.000021599357,"about_ca_system_score_gemma":0.00003826748,"threshold_uncertainty_score":0.3596198},"labels":[],"label_agreement":null},{"id":"W2980889552","doi":"10.36001/phmconf.2019.v11i1.801","title":"Tooth Crack Severity Assessment in the Early Stage of Crack Propagation Using Gearbox Dynamic Model","year":2019,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Gear and Bearing Dynamics Analysis","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":"University of Alberta","funders":"China Scholarship Council","keywords":"Impulse (physics); Structural engineering; Fracture mechanics; Crack growth resistance curve; Crack closure; Crack tip opening displacement; Kurtosis; Materials science; Mathematics; Engineering; Statistics; Physics","score_opus":0.018212477054709273,"score_gpt":0.2578762891171318,"score_spread":0.23966381206242252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980889552","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":[0.98824114,0.000010564282,0.010812566,0.00009129266,0.00005388091,0.00019995768,0.000058868,0.000014077837,0.00051766884],"genre_scores_gemma":[0.9982149,0.000022926255,0.0014110287,0.000022416743,0.0000058469104,0.0000030748186,0.0000047773015,0.000011075619,0.00030397918],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916977,0.000045225122,0.00022519425,0.00012430013,0.00027986246,0.00015565605],"domain_scores_gemma":[0.99937576,0.000028249437,0.00009442202,0.00034219713,0.00014412968,0.000015238652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035124336,0.000107906584,0.00018830673,0.000020206995,0.00003839503,0.000033619956,0.00034592554,0.000070647824,0.000014422343],"category_scores_gemma":[0.000008990969,0.00007373542,0.00017430822,0.00021263445,0.000055310953,0.00013862063,0.00008135491,0.00026882836,0.0000015798149],"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":[0.000005438354,0.00009691297,0.024341179,0.0002968541,0.00013416325,2.9468848e-7,0.014326685,0.92950535,0.02951431,0.0011046644,0.000030009178,0.00064416154],"study_design_scores_gemma":[0.0001154735,0.000015643373,0.050923645,0.000035220317,0.000026406587,2.4868123e-7,0.0017669818,0.9463884,0.00019763221,0.00044020623,0.000006229361,0.000083912455],"about_ca_topic_score_codex":0.00042863048,"about_ca_topic_score_gemma":0.000104963554,"teacher_disagreement_score":0.029316679,"about_ca_system_score_codex":0.00007358345,"about_ca_system_score_gemma":0.000086929904,"threshold_uncertainty_score":0.3006843},"labels":[],"label_agreement":null},{"id":"W3003512542","doi":"10.36001/phmconf.2010.v2i1.1933","title":"Diagnosability for Patterns in Distributed Discrete Event Systems","year":2010,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Business Process Modeling and Analysis","field":"Business, Management and Accounting","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Event (particle physics); Computer science; Distributed computing; Physics","score_opus":0.021160825849821933,"score_gpt":0.25220142759176084,"score_spread":0.2310406017419389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003512542","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":[0.9765613,0.000022854982,0.019617494,0.0027580392,0.00034855522,0.00031773982,0.00023370536,0.00003617789,0.00010413392],"genre_scores_gemma":[0.99932444,0.0000065299546,0.00004138667,0.00012288995,0.00027631447,0.0000735204,0.000074887415,0.000011003603,0.000069004134],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899644,0.000009532195,0.0003208181,0.0002449331,0.00020022708,0.00022803585],"domain_scores_gemma":[0.9987284,0.000068163776,0.0002531543,0.00032788105,0.0006124102,0.000009972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005091829,0.000141935,0.00025471934,0.000024101231,0.00013549757,0.00013987558,0.0004595192,0.000094624,0.000032840206],"category_scores_gemma":[0.00027839813,0.00009759756,0.00028437751,0.00030206316,0.000101351805,0.00049446593,0.00018668058,0.00021273899,0.0000043485275],"study_design_candidate":"observational","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":[0.000087340755,0.000857541,0.94184625,0.0041654645,0.0002844064,0.0000010609269,0.0029908107,0.008821984,0.0018798966,0.024722027,0.0072153513,0.0071278736],"study_design_scores_gemma":[0.0018164642,0.00001903113,0.19441865,0.00060754525,0.0003955487,0.0000011096859,0.011682861,0.7593083,0.00041037364,0.01683541,0.013617548,0.00088719174],"about_ca_topic_score_codex":0.0026063493,"about_ca_topic_score_gemma":0.00050907175,"teacher_disagreement_score":0.75048625,"about_ca_system_score_codex":0.000013735992,"about_ca_system_score_gemma":0.000040867933,"threshold_uncertainty_score":0.3979913},"labels":[],"label_agreement":null},{"id":"W3099805203","doi":"10.36001/phmconf.2020.v12i1.1261","title":"Life prediction for aircraft structure based on Bayesian inference: towards a digital twin ecosystem","year":2020,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Department of National Defence; National Research Council Canada; Government of Canada; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Inference; Data mining; Software; Bayesian probability; Bayesian inference; Machine learning; Artificial intelligence","score_opus":0.02153486751451185,"score_gpt":0.23884647669886255,"score_spread":0.21731160918435072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099805203","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":[0.55985856,0.000023439281,0.4215873,0.0030597611,0.00050363276,0.0016000046,0.008137301,0.0019831755,0.003246862],"genre_scores_gemma":[0.97561586,0.000002045004,0.023833828,0.00030626482,0.00013081863,0.00003546328,0.00003488884,0.00003727686,0.0000035694513],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989428,0.000027900182,0.00028231335,0.00024357846,0.0002692813,0.00023416308],"domain_scores_gemma":[0.9990467,0.00013132097,0.00009721517,0.00029541744,0.00027978813,0.00014956202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000097344215,0.00023303626,0.00026909294,0.000018419636,0.000081833656,0.00007614101,0.0004530908,0.00016683662,0.000025134781],"category_scores_gemma":[0.00067545124,0.00018631916,0.0002391112,0.00019954343,0.00007610354,0.00025260955,0.00007635992,0.00029685182,0.0000013729828],"study_design_candidate":"simulation_or_modeling","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":[0.0012356184,0.0008738837,0.14921248,0.018136557,0.0035312236,0.000012448658,0.14596009,0.08724159,0.16215819,0.07762739,0.30581334,0.04819718],"study_design_scores_gemma":[0.00157554,0.0011316863,0.011559368,0.0007300992,0.00013621661,0.0000045676757,0.0025524371,0.909172,0.026056947,0.044774614,0.001364286,0.0009422141],"about_ca_topic_score_codex":0.00000709692,"about_ca_topic_score_gemma":0.0000020312443,"teacher_disagreement_score":0.8219304,"about_ca_system_score_codex":0.00007689774,"about_ca_system_score_gemma":0.00023172742,"threshold_uncertainty_score":0.7597875},"labels":[],"label_agreement":null},{"id":"W3101680449","doi":"10.36001/phmconf.2020.v12i1.1155","title":"Noisy Multipath Parallel Hybrid Model for Remaining Useful Life Estimation (NMPM)","year":2020,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Robustness (evolution); Convolutional neural network; Artificial neural network; Artificial intelligence; Deep learning; Multipath propagation; Pattern recognition (psychology); Perceptron; Algorithm; Channel (broadcasting)","score_opus":0.04951003551731543,"score_gpt":0.2812955657095663,"score_spread":0.2317855301922509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101680449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20525108,0.000057608857,0.79060346,0.0023247667,0.00007137177,0.00058726716,0.0002993838,0.0005023895,0.00030269756],"genre_scores_gemma":[0.92111444,0.000051534713,0.077942595,0.000664533,0.000052991774,0.00009683527,0.000022352191,0.000033438777,0.000021271082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990812,0.00002032559,0.00028879294,0.00019696786,0.00018807383,0.0002246652],"domain_scores_gemma":[0.99925566,0.00011227017,0.00009502507,0.00025977232,0.0001787669,0.000098507095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001770583,0.00018269048,0.00025271298,0.000010252411,0.0000831741,0.000033551485,0.00042604198,0.00007921236,0.000009653675],"category_scores_gemma":[0.0003834821,0.00015423298,0.00025360048,0.000087740664,0.00005893179,0.0002356228,0.00011081753,0.00020976631,0.0000031822308],"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":[0.000032599542,0.000073910036,0.0012616323,0.0006089662,0.00018452332,4.96636e-7,0.032816894,0.82704324,0.0051768348,0.0021186525,0.12636523,0.0043170317],"study_design_scores_gemma":[0.00028683,0.000036120095,0.00037651922,0.000054991746,0.000030811054,4.993517e-7,0.00033479626,0.9919075,0.0052254186,0.0012485521,0.00032287004,0.00017509103],"about_ca_topic_score_codex":0.000016085507,"about_ca_topic_score_gemma":0.0000031939019,"teacher_disagreement_score":0.71586335,"about_ca_system_score_codex":0.000030555464,"about_ca_system_score_gemma":0.0000662951,"threshold_uncertainty_score":0.62894386},"labels":[],"label_agreement":null},{"id":"W3102209108","doi":"10.36001/phmconf.2020.v12i1.1129","title":"Fault Diagnostics and Prognostics for Vehicle Springs and Stablizer Bar","year":2020,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Prognostics; Stabilizer (aeronautics); Robustness (evolution); Bar (unit); Bushing; Engineering; Downtime; Automotive engineering; Spring (device); Fault detection and isolation; Acceleration; Fault (geology); Computer science; Structural engineering; Reliability engineering; Electrical engineering","score_opus":0.017661169426164355,"score_gpt":0.21166835717119803,"score_spread":0.19400718774503367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3102209108","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":[0.9904593,0.0007167303,0.0053213453,0.0022333162,0.0001957545,0.00052920607,0.00018297254,0.00011600112,0.00024532244],"genre_scores_gemma":[0.99908566,0.00025333796,0.0002762608,0.00024114968,0.000060311377,0.000021122594,0.0000010815429,0.000012949712,0.000048143047],"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999525,0.000011238947,0.00013491002,0.00010838932,0.00009140877,0.00012903848],"domain_scores_gemma":[0.99958134,0.00012279146,0.00003225778,0.000081560036,0.000111271955,0.000070761074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007144451,0.00009296773,0.00014468255,0.0000039861497,0.00006497374,0.00003947798,0.0000953152,0.00006347176,0.0000043181276],"category_scores_gemma":[0.00018247901,0.00007295863,0.00006277081,0.00005566201,0.00007298183,0.000078787954,0.000052201463,0.00010099378,0.0000011854994],"study_design_candidate":"simulation_or_modeling","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":[0.00038090342,0.0003186486,0.08293837,0.010071679,0.0026218346,0.000007327558,0.3050332,0.0077010514,0.23903458,0.028466972,0.1557742,0.16765124],"study_design_scores_gemma":[0.0024597663,0.00034240907,0.012010468,0.00015522605,0.00016254294,0.000005848185,0.014359777,0.86603713,0.013406959,0.00055161817,0.089973785,0.0005344453],"about_ca_topic_score_codex":0.000013983421,"about_ca_topic_score_gemma":0.000003655365,"teacher_disagreement_score":0.8583361,"about_ca_system_score_codex":0.0000069160446,"about_ca_system_score_gemma":0.00001442401,"threshold_uncertainty_score":0.29751667},"labels":[],"label_agreement":null},{"id":"W3103821869","doi":"10.36001/phmconf.2020.v12i1.1136","title":"Automatic detection of rare observations during production tests using statistical models","year":2020,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Safran Electronics (Canada)","funders":"CHIST-ERA; Safran Aircraft Engines; Safran; Association Nationale de la Recherche et de la Technologie","keywords":"Interpretability; Computer science; Cluster analysis; Context (archaeology); Machine learning; Anomaly detection; Representation (politics); Artificial intelligence; Data mining; Feature (linguistics); Variable (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.0841597462602211,"score_gpt":0.27522073547337605,"score_spread":0.19106098921315495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3103821869","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41936347,0.0000056770796,0.5794841,0.0008389674,0.000031599742,0.00015671008,0.000017427103,0.00008515426,0.000016864475],"genre_scores_gemma":[0.94021785,0.0000070826454,0.05965501,0.000054340126,0.000026212725,0.000016262875,0.0000010170224,0.0000050643334,0.000017180115],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991952,0.000039596773,0.00025579406,0.0002095989,0.00019376707,0.000106038824],"domain_scores_gemma":[0.9990507,0.000030166106,0.00021085414,0.00030922866,0.00035769632,0.000041375326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008963896,0.000081313556,0.00012662342,0.000012606846,0.00019347071,0.000026414591,0.00041451052,0.00004994359,0.0000049476585],"category_scores_gemma":[0.00007447624,0.00006803143,0.000098582954,0.00045908304,0.00010015759,0.0004767791,0.00019174496,0.00012004583,7.259748e-7],"study_design_candidate":"bench_or_experimental","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":[0.000009074,0.0001923334,0.00073484454,0.0004182497,0.00008863695,3.5325957e-7,0.02300578,0.007980322,0.8953275,0.052839804,0.00041729468,0.018985823],"study_design_scores_gemma":[0.00007359606,0.000043347794,0.007567657,0.000035069796,0.00001655345,0.000005192287,0.0005934785,0.8269634,0.15660313,0.007986203,0.000019042145,0.000093281415],"about_ca_topic_score_codex":0.000049835344,"about_ca_topic_score_gemma":0.000002887799,"teacher_disagreement_score":0.81898314,"about_ca_system_score_codex":0.000029763216,"about_ca_system_score_gemma":0.00008812726,"threshold_uncertainty_score":0.27742416},"labels":[],"label_agreement":null},{"id":"W3105904714","doi":"10.36001/phmconf.2020.v12i1.1130","title":"Fault Detection and Isolation for Brake Rotor Thickness Variation","year":2020,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Brake; Automotive engineering; Fault detection and isolation; Vibration; Robustness (evolution); Threshold braking; Engineering; Acceleration; Serviceability (structure); Fault (geology); Control theory (sociology); Computer science; Structural engineering; Acoustics; Actuator; Artificial intelligence; Electrical engineering","score_opus":0.0122077418767219,"score_gpt":0.1978957919000041,"score_spread":0.18568805002328218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3105904714","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":[0.52941775,0.000066857814,0.46826568,0.0010386589,0.00028262127,0.0005745527,0.00009997953,0.0000865182,0.00016734563],"genre_scores_gemma":[0.9996064,0.000010775306,0.00017268931,0.0000678179,0.00006899982,0.0000307639,0.0000031113648,0.000009043852,0.000030409878],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996314,0.000014690971,0.00012295276,0.000085332875,0.00006678028,0.00007883195],"domain_scores_gemma":[0.99969465,0.000030960222,0.000048646933,0.000070050744,0.00013378664,0.000021915868],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009650683,0.00006534455,0.00009781686,0.0000037824302,0.00006559571,0.000029344852,0.000079832964,0.000071340495,0.0000027634967],"category_scores_gemma":[0.000027875287,0.000053498523,0.00007458104,0.000060061906,0.000016706486,0.00012279801,0.000018483992,0.00007241811,6.505764e-7],"study_design_candidate":"simulation_or_modeling","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":[0.000074139,0.00003063789,0.001291651,0.000908985,0.000352017,8.086974e-8,0.061072785,0.023795407,0.8621118,0.004567722,0.00088757154,0.044907223],"study_design_scores_gemma":[0.000320167,0.00003562381,0.0048003467,0.000014595239,0.00002049324,5.9265e-7,0.0008030953,0.9920114,0.0011952419,0.00027659448,0.0004518475,0.0000699808],"about_ca_topic_score_codex":0.000028085627,"about_ca_topic_score_gemma":0.0000112028,"teacher_disagreement_score":0.968216,"about_ca_system_score_codex":0.000014008046,"about_ca_system_score_gemma":0.00001255318,"threshold_uncertainty_score":0.21816064},"labels":[],"label_agreement":null},{"id":"W3136134194","doi":"10.36001/phmconf.2015.v7i1.2543","title":"Creep Mechanisms vis-à-vis Power Law vs. Grain Boundary Sliding in α-β Titanium Alloys for Physics Based Prognostics","year":2015,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Metallurgy and Material Forming","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Life Prediction Technologies (Canada)","funders":"","keywords":"Creep; Materials science; Grain boundary; Grain Boundary Sliding; Titanium alloy; Power law; Constitutive equation; MATLAB; Structural engineering; Alloy; Mechanics; Metallurgy; Composite material; Computer science; Engineering; Physics; Finite element method; Mathematics; Microstructure","score_opus":0.031345629327500985,"score_gpt":0.24093196481531723,"score_spread":0.20958633548781624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136134194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3720117,0.00014817985,0.6092988,0.00096866937,0.0049631996,0.0021114626,0.00063882704,0.00050480146,0.009354355],"genre_scores_gemma":[0.98688215,0.000004600039,0.012679507,0.00022024583,0.00006098361,0.00003274954,0.000021306889,0.000031589087,0.00006689047],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999068,0.000033144624,0.00024234469,0.00015065158,0.00020909193,0.00029675945],"domain_scores_gemma":[0.9993633,0.00006747673,0.00006698948,0.00022582585,0.00020657641,0.000069800175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049015635,0.00017537516,0.0002520478,0.000011265589,0.00008829886,0.00005705183,0.0002952754,0.00012341584,0.000020543384],"category_scores_gemma":[0.00008522719,0.00014663248,0.00018234485,0.000112351554,0.00007375546,0.0002467864,0.0000843775,0.00015268498,0.000005631255],"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":[0.0001895015,0.000295928,0.00013725743,0.0009830702,0.00036293807,0.000005227051,0.026999729,0.0049188985,0.17788707,0.7766154,0.010103211,0.0015018032],"study_design_scores_gemma":[0.0045494735,0.0008480408,0.0003581547,0.00076707883,0.00026270296,0.000007942819,0.011420813,0.20537665,0.55801356,0.1629108,0.053728577,0.0017562221],"about_ca_topic_score_codex":0.000048417343,"about_ca_topic_score_gemma":0.000029812842,"teacher_disagreement_score":0.6148704,"about_ca_system_score_codex":0.000055824465,"about_ca_system_score_gemma":0.00010672573,"threshold_uncertainty_score":0.59794986},"labels":[],"label_agreement":null},{"id":"W3136377744","doi":"10.36001/phmconf.2012.v4i1.2119","title":"Initial Condition Monitoring Experience on a Wind Turbine","year":2012,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre Intégré de Santé et Services Sociaux de la Gaspésie","funders":"","keywords":"Turbine; Environmental science; Wind power; Condition monitoring; Marine engineering; Computer science; Engineering; Aerospace engineering; Electrical engineering","score_opus":0.049092888311798276,"score_gpt":0.33436600671976285,"score_spread":0.2852731184079646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136377744","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":[0.99718267,0.00007189894,0.0000753633,0.00006776801,0.0015149041,0.00014614566,0.000028511555,0.00021884643,0.0006938919],"genre_scores_gemma":[0.9985184,0.000041309067,0.0007782072,0.0000321066,0.00056498224,0.00001809263,0.0000020938621,0.000016896069,0.000027930584],"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99916726,0.00002522967,0.00018259915,0.00009675328,0.0002268936,0.000301289],"domain_scores_gemma":[0.9994568,0.00005875523,0.00005582906,0.00025338755,0.00009970005,0.000075492855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010755312,0.00013796003,0.0001385194,0.000014380901,0.000100464305,0.000014875384,0.00026910275,0.00010224669,0.000025239593],"category_scores_gemma":[0.00003617483,0.00010703764,0.00009780107,0.00011538383,0.00010454003,0.0003130576,0.000065675384,0.00026176684,0.000005568179],"study_design_candidate":"bench_or_experimental","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":[0.00016055719,0.00035594797,0.15650983,0.0015133779,0.00042869375,0.000004231031,0.5539834,0.0017506451,0.13494503,0.0109767355,0.024613475,0.114758044],"study_design_scores_gemma":[0.00031282392,0.00011830424,0.32112917,0.00037977577,0.000022343613,0.000010609038,0.0069093206,0.00061896764,0.66767347,0.0009778647,0.0014534242,0.00039391746],"about_ca_topic_score_codex":0.000034442506,"about_ca_topic_score_gemma":2.0730825e-7,"teacher_disagreement_score":0.5470741,"about_ca_system_score_codex":0.00009122564,"about_ca_system_score_gemma":0.00001874458,"threshold_uncertainty_score":0.4364868},"labels":[],"label_agreement":null},{"id":"W3143116247","doi":"10.36001/phmconf.2012.v4i1.2108","title":"Correction of Data Gathered by Degraded Transducers for Damage Prognosis in Composite Structures","year":2012,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Ultrasonics and Acoustic Wave Propagation","field":"Engineering","cited_by":3,"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; Université de Sherbrooke","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Composite number; Computer science; Transducer; Acoustics; Physics; Algorithm","score_opus":0.03572097917189901,"score_gpt":0.256641239681856,"score_spread":0.22092026050995697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143116247","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":[0.9866928,0.00015542595,0.011631864,0.00007939346,0.0003103003,0.0002988729,0.00071345136,0.000021390313,0.000096492055],"genre_scores_gemma":[0.99797463,0.00004172518,0.0018142094,0.000009025143,0.00002232343,0.000009367492,0.000096843265,0.000011057247,0.000020807245],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999488,0.000018182895,0.00016788156,0.00008515891,0.00009743762,0.00014333944],"domain_scores_gemma":[0.9996065,0.00007028512,0.00005978075,0.00017176846,0.00006887541,0.000022819288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016903857,0.000082088794,0.00012857578,0.000008871131,0.000029993991,0.00000935802,0.0002666895,0.00006207654,0.000007884238],"category_scores_gemma":[0.00002437944,0.00006444444,0.00006449684,0.00009069405,0.00005441482,0.00023078888,0.000027178236,0.00010171769,1.2505112e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002846561,0.0001532823,0.0076250182,0.00038663702,0.00017476505,2.5505734e-8,0.02044987,0.0026954631,0.9382533,0.0002445966,0.016011896,0.013976671],"study_design_scores_gemma":[0.00089774485,0.000073898984,0.032324284,0.0001564251,0.00014238423,0.0000014995414,0.006561967,0.42061594,0.5377485,0.00056214485,0.00061998726,0.00029519873],"about_ca_topic_score_codex":0.000050234798,"about_ca_topic_score_gemma":0.000008313826,"teacher_disagreement_score":0.41792047,"about_ca_system_score_codex":0.000019320607,"about_ca_system_score_gemma":0.000017980607,"threshold_uncertainty_score":0.2627968},"labels":[],"label_agreement":null},{"id":"W3147078440","doi":"10.36001/phmconf.2012.v4i1.2088","title":"Performance Based Anomaly Detection Analysis of a Gas Turbine Engine by Artificial Neural Network Approach","year":2012,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Life Prediction Technologies (Canada)","funders":"","keywords":"Artificial neural network; Anomaly detection; Gas turbines; Computer science; Artificial intelligence; Anomaly (physics); Turbine; Machine learning; Pattern recognition (psychology); Engineering; Aerospace engineering; Mechanical engineering; Physics","score_opus":0.01896077175208427,"score_gpt":0.22322342096384107,"score_spread":0.2042626492117568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3147078440","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":[0.85172516,0.00009426271,0.14759187,0.00008394163,0.000088797795,0.00009910505,0.000042386528,0.00014807835,0.00012637698],"genre_scores_gemma":[0.9957693,0.000011750007,0.0040359846,0.00002225048,0.00005748188,0.000016697037,0.00002331767,0.000014324123,0.000048885227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896294,0.00002263496,0.00030228583,0.0001590967,0.00022064816,0.0003324251],"domain_scores_gemma":[0.9991413,0.00007308566,0.0001898121,0.00037753955,0.00017401738,0.000044253557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019119226,0.00016899797,0.0003227943,0.000043695294,0.00006580869,0.0000058742075,0.00034504948,0.00013470314,0.000024515492],"category_scores_gemma":[0.000095072886,0.00013364787,0.00031359683,0.001106209,0.00014337506,0.00021680622,0.000105085615,0.00030037068,7.8012e-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":[0.000026019867,0.00011181289,0.0033581916,0.000064377615,0.0003323073,2.669152e-8,0.0006036193,0.93505764,0.052381307,0.00021283762,0.00017666335,0.0076752147],"study_design_scores_gemma":[0.00012832702,0.00003138602,0.0024496648,0.000011154984,0.00025335673,5.2502014e-7,0.00037798638,0.8834454,0.11306934,0.000022374168,0.00007943321,0.00013107652],"about_ca_topic_score_codex":0.00001803024,"about_ca_topic_score_gemma":0.0000010495135,"teacher_disagreement_score":0.14404413,"about_ca_system_score_codex":0.000044933087,"about_ca_system_score_gemma":0.000014560094,"threshold_uncertainty_score":0.54500026},"labels":[],"label_agreement":null},{"id":"W3149130192","doi":"10.36001/phmconf.2010.v2i1.1945","title":"Sensor and Actuator Fault Isolation Using Parameter Interval based Method for Nonlinear Dynamic Systems","year":2010,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nautical Research Society","funders":"","keywords":"Actuator; Control theory (sociology); Interval (graph theory); Nonlinear system; Fault detection and isolation; Isolation (microbiology); Computer science; Fault (geology); Interval arithmetic; Control engineering; Mathematics; Engineering; Physics; Artificial intelligence; Geology; Mathematical analysis; Biology; Control (management)","score_opus":0.01762213656239334,"score_gpt":0.2771027117123861,"score_spread":0.25948057514999273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3149130192","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":[0.67984396,0.000021681339,0.31858268,0.00009922098,0.0008361389,0.0003658758,0.00016177715,0.000065403525,0.000023264123],"genre_scores_gemma":[0.97005814,0.0000021387293,0.029723106,0.00003642726,0.00006790649,0.000021750542,0.0000044534113,0.000019440546,0.00006661059],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933076,0.000047359208,0.00022596758,0.00013624628,0.00010962196,0.00015005293],"domain_scores_gemma":[0.99932116,0.00017711,0.000083641266,0.00019318468,0.00018175755,0.00004313701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028816762,0.000128255,0.00021311129,0.000016615186,0.0000794399,0.000059909114,0.00012188051,0.00014183816,0.0000054608545],"category_scores_gemma":[0.00008043846,0.000097016586,0.00017608386,0.000064916625,0.000046687746,0.0001143165,0.000020524012,0.00019748745,8.8144816e-7],"study_design_candidate":"simulation_or_modeling","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":[0.000056516783,0.00003765997,0.00018687818,0.00060738705,0.00028686898,2.1597876e-7,0.0052843136,0.014135653,0.97168833,0.00014851285,0.00033482796,0.0072328434],"study_design_scores_gemma":[0.00040640906,0.00002852427,0.000093687326,0.000041597857,0.00003963837,0.000006117382,0.0015582092,0.99273545,0.0037013898,0.000024865483,0.0012501524,0.00011397941],"about_ca_topic_score_codex":0.00008679863,"about_ca_topic_score_gemma":0.00002721118,"teacher_disagreement_score":0.9785998,"about_ca_system_score_codex":0.000025129777,"about_ca_system_score_gemma":0.000029746465,"threshold_uncertainty_score":0.39562216},"labels":[],"label_agreement":null},{"id":"W3151433605","doi":"10.36001/phmconf.2010.v2i1.1958","title":"Diagnosis as Planning Revisited: An Abridged Report","year":2010,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Psychology","score_opus":0.028057738184166948,"score_gpt":0.2882608180578948,"score_spread":0.2602030798737278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3151433605","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":[0.9715946,0.00011805229,0.020349039,0.0034071712,0.0007307486,0.00021733416,0.00003353317,0.00022775248,0.003321787],"genre_scores_gemma":[0.98331726,0.000013379355,0.015299202,0.00064247765,0.00011450139,0.000015422209,0.00001186801,0.000010725718,0.00057517696],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99840415,0.00010065865,0.00034055248,0.00043136554,0.00040286945,0.000320394],"domain_scores_gemma":[0.9977643,0.00022007003,0.00033834713,0.0011432908,0.0004037327,0.0001302986],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010694023,0.00017871113,0.0002335864,0.000022618136,0.00035828256,0.00018680787,0.0016367078,0.00014920159,0.00004540254],"category_scores_gemma":[0.00026525918,0.0001353805,0.00022006193,0.0002988727,0.00014511458,0.0007036092,0.00039966818,0.0006148605,0.000013657145],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045517234,0.0006943159,0.4388095,0.00034153296,0.000589967,0.00031649446,0.22856729,0.00096466875,0.027477419,0.08033932,0.14589062,0.07596333],"study_design_scores_gemma":[0.0034845849,0.0018648448,0.35281688,0.003406558,0.00055266294,0.0027419436,0.015701232,0.16086215,0.15522696,0.079509,0.21807134,0.005761861],"about_ca_topic_score_codex":0.00025282244,"about_ca_topic_score_gemma":0.0000052797373,"teacher_disagreement_score":0.21286607,"about_ca_system_score_codex":0.00001277094,"about_ca_system_score_gemma":0.0002621469,"threshold_uncertainty_score":0.5520657},"labels":[],"label_agreement":null},{"id":"W3152285607","doi":"10.36001/phmconf.2013.v5i1.2300","title":"Physics-Based Prognostics for LCF Crack Nucleation Life of IMI 685 Aero-engine Compressor Disc","year":2013,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Mechanical Failure Analysis and Simulation","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Life Prediction Technologies (Canada)","funders":"","keywords":"Nucleation; Prognostics; Materials science; Probabilistic logic; Gas compressor; Fracture (geology); Structural engineering; Low-cycle fatigue; Log-normal distribution; Mechanics; Mathematics; Physics; Engineering; Metallurgy; Mechanical engineering; Statistics; Reliability engineering; Thermodynamics; Composite material","score_opus":0.02310982396818815,"score_gpt":0.22441580993521706,"score_spread":0.20130598596702892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152285607","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":[0.5667582,0.00006263898,0.42998043,0.0014204684,0.0001682474,0.0009907229,0.00022843409,0.00009698694,0.00029383466],"genre_scores_gemma":[0.9958156,0.000011205872,0.0039085182,0.000070808004,0.000059597012,0.000028393502,0.000039869657,0.00001598533,0.000050032315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926287,0.000021542932,0.0002854354,0.00010842403,0.0001831552,0.00013858566],"domain_scores_gemma":[0.9989516,0.00016780612,0.00011306434,0.00023391038,0.000485964,0.000047649075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010249876,0.00011475604,0.00023848693,0.000008348607,0.00005350141,0.0000245463,0.00020428738,0.00007784655,0.000081670056],"category_scores_gemma":[0.00010601605,0.00008402348,0.00028397757,0.00012139255,0.00004968367,0.0001543775,0.000031722884,0.00009492282,0.0000048793822],"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":[0.0000448957,0.0005547084,0.0021799065,0.0017155865,0.0010167267,7.2467444e-8,0.006517933,0.7391117,0.16814065,0.020995095,0.04282639,0.01689633],"study_design_scores_gemma":[0.00028046468,0.000047270874,0.0013877267,0.000048733957,0.000091732894,2.3507656e-8,0.0003665049,0.9770754,0.018529328,0.0016085185,0.0004551358,0.00010917781],"about_ca_topic_score_codex":0.000027602478,"about_ca_topic_score_gemma":0.0000022993545,"teacher_disagreement_score":0.42905736,"about_ca_system_score_codex":0.000012876365,"about_ca_system_score_gemma":0.000026584683,"threshold_uncertainty_score":0.3426378},"labels":[],"label_agreement":null},{"id":"W3152986159","doi":"10.36001/phmconf.2011.v3i1.2083","title":"Using the Validated FMEA to Update Trouble Shooting Manuals: a Case Study of APU TSM Revision","year":2011,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Trouble shooting; Reliability engineering; Aeronautics; Engineering; Computer science; Forensic engineering","score_opus":0.36694450207544316,"score_gpt":0.4257202316249976,"score_spread":0.058775729549554456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152986159","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":[0.9921664,0.00004557263,0.0060884114,0.0007395963,0.000116550254,0.00041592115,0.000040475243,0.000013722032,0.00037333454],"genre_scores_gemma":[0.9970642,0.00003308606,0.0024835796,0.00015653325,0.00002779151,0.0000037298532,7.265484e-7,0.000009908613,0.00022049544],"study_design_codex":"qualitative","study_design_gemma":"qualitative","domain_scores_codex":[0.99633765,0.000754614,0.0009836425,0.00045055934,0.001190468,0.00028303755],"domain_scores_gemma":[0.99604386,0.00044776947,0.0007085517,0.0013051858,0.0014014494,0.00009315528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047461493,0.00019091886,0.0005229258,0.000063552136,0.00052096107,0.00010643572,0.0016853194,0.00008187561,0.00024622894],"category_scores_gemma":[0.0008088034,0.00009638154,0.00049643026,0.0014137228,0.00020386661,0.00038228644,0.0008272005,0.00025568975,0.000018197834],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023676938,0.0014609902,0.03914282,0.000036751982,0.0008768773,0.000079815625,0.885557,0.0028665096,0.004161812,0.0021481118,0.0065053594,0.056927208],"study_design_scores_gemma":[0.000760624,0.0003598627,0.0032753325,0.00010753025,0.00047358466,0.00009161555,0.96524733,0.0150208175,0.005777492,0.0077970023,0.00073859794,0.0003501794],"about_ca_topic_score_codex":0.0046202643,"about_ca_topic_score_gemma":0.00034040128,"teacher_disagreement_score":0.07969038,"about_ca_system_score_codex":0.00002397393,"about_ca_system_score_gemma":0.000113408445,"threshold_uncertainty_score":0.69844884},"labels":[],"label_agreement":null},{"id":"W3153526116","doi":"10.36001/phmconf.2011.v3i1.1972","title":"Damage Identification in Frame Structures, Using Damage Index, Based on H2-Norm","year":2011,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Norm (philosophy); Frame (networking); Index (typography); Structural engineering; Mathematics; Computer science; Engineering; Political science; Telecommunications","score_opus":0.055800186502980076,"score_gpt":0.28704505127065044,"score_spread":0.23124486476767037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153526116","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":[0.99709105,0.000012525826,0.0015681351,0.000038416365,0.00039551628,0.0002361612,0.000043243024,0.00015673408,0.00045820375],"genre_scores_gemma":[0.99562067,0.000010445701,0.0042139073,0.000058285386,0.000046329573,0.000009355444,0.000004627835,0.000023259281,0.000013141764],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990151,0.000048450172,0.000294511,0.00017205391,0.0002302489,0.00023964122],"domain_scores_gemma":[0.9991918,0.000044083103,0.00010439139,0.0004959435,0.000118410244,0.00004535603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021122723,0.00016123019,0.00017238267,0.000047069407,0.00007292392,0.000022308659,0.000485689,0.00017467857,0.00003753727],"category_scores_gemma":[0.00004434668,0.00013364265,0.00009463374,0.00023597454,0.00010578653,0.00019542297,0.00006983137,0.00041884487,0.0000012754091],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039241987,0.00048804664,0.49627757,0.0039404165,0.0003716911,0.000022502829,0.20582245,0.055037953,0.14165014,0.027041668,0.0075640446,0.0613911],"study_design_scores_gemma":[0.00021819107,0.000035744797,0.8292352,0.00016445667,0.000012594722,8.786454e-7,0.0010079365,0.11198169,0.049848285,0.0072268434,0.000044689932,0.00022350914],"about_ca_topic_score_codex":0.00062704616,"about_ca_topic_score_gemma":0.000023930092,"teacher_disagreement_score":0.33295763,"about_ca_system_score_codex":0.00014511483,"about_ca_system_score_gemma":0.00006193721,"threshold_uncertainty_score":0.54497886},"labels":[],"label_agreement":null},{"id":"W3154189971","doi":"10.36001/phmconf.2011.v3i1.2053","title":"Fault-Tolerant Trajectory Tracking Control of a Quadrotor Helicopter Using Gain-Scheduled PID and Model Reference Adaptive Control","year":2011,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Adaptive Control of Nonlinear Systems","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Concordia University","funders":"","keywords":"PID controller; Control theory (sociology); Trajectory; Tracking (education); Control engineering; Fault tolerance; Computer science; Control (management); Reference model; Engineering; Artificial intelligence; Temperature control; Psychology; Distributed computing","score_opus":0.07342470427248048,"score_gpt":0.2466185645658867,"score_spread":0.17319386029340622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154189971","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":[0.6645215,0.0005586285,0.33293787,0.00002872021,0.00014342305,0.00082189357,0.00038404713,0.00007832069,0.00052561006],"genre_scores_gemma":[0.99540466,0.00002548249,0.004323041,0.000075394615,0.00006245297,0.000033124856,0.0000014898764,0.000045054298,0.000029320357],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99820393,0.0001413015,0.00061965926,0.00029512422,0.0003262079,0.00041379963],"domain_scores_gemma":[0.9982203,0.00015742812,0.00031449625,0.0003959431,0.00079518533,0.000116627816],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00045791242,0.0003419472,0.0007526094,0.000037953952,0.000078524725,0.000019881714,0.00042655456,0.00021456227,0.000020388854],"category_scores_gemma":[0.000070843154,0.00026897987,0.00029079113,0.00012286246,0.0003435555,0.0003297489,0.00005877067,0.0003809435,0.0000018592026],"study_design_candidate":"simulation_or_modeling","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":[0.001629517,0.0005517208,0.0052665463,0.0010230818,0.0035827202,0.000010136479,0.18678886,0.16219191,0.6279905,0.006214804,0.00019846154,0.0045517283],"study_design_scores_gemma":[0.002444834,0.00013494585,0.0025499526,0.00025574816,0.00017475516,0.0000069688667,0.003751604,0.9841744,0.0060201134,0.00015889097,0.00001672026,0.00031110528],"about_ca_topic_score_codex":0.00020865639,"about_ca_topic_score_gemma":0.000033708973,"teacher_disagreement_score":0.82198244,"about_ca_system_score_codex":0.000073091986,"about_ca_system_score_gemma":0.00017308483,"threshold_uncertainty_score":0.9999762},"labels":[],"label_agreement":null},{"id":"W3155178634","doi":"10.36001/phmconf.2011.v3i1.1962","title":"Integrated Robust Fault Detection, Diagnosis and Reconfigurable Control System with Actuator Saturation","year":2011,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Fault Detection and Control Systems","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":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Actuator; Fault detection and isolation; Control theory (sociology); Saturation (graph theory); Fault (geology); Control engineering; Computer science; Control (management); Engineering; Artificial intelligence; Geology; Mathematics; Seismology","score_opus":0.017623606417014222,"score_gpt":0.1822662122170714,"score_spread":0.16464260580005718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155178634","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":[0.96659154,0.00021566566,0.026390245,0.000111664434,0.000702484,0.0007901614,0.00011657628,0.0004295484,0.0046521444],"genre_scores_gemma":[0.9995624,0.00003786669,0.00010071943,0.000030173045,0.000033647113,0.0001062007,0.0000015177358,0.00001748242,0.00011000121],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926764,0.000059191607,0.00022703066,0.00015026206,0.0001335782,0.00016227065],"domain_scores_gemma":[0.9993117,0.000042624775,0.00009597314,0.00019727673,0.00029492687,0.00005752128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015343545,0.00015884556,0.00022855372,0.000018438792,0.00012306719,0.00004375116,0.00013695734,0.00011994703,0.000027632239],"category_scores_gemma":[0.000025066103,0.00010286992,0.00009147496,0.00015739672,0.000079001504,0.00022415632,0.000006383939,0.00018789877,0.0000048357497],"study_design_candidate":"simulation_or_modeling","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":[0.0021764664,0.00069201423,0.028843625,0.0057118167,0.010029432,0.000019254307,0.26858333,0.026249327,0.38090128,0.00812905,0.021702986,0.24696143],"study_design_scores_gemma":[0.0051509426,0.0007398821,0.018516483,0.001053506,0.00044789657,0.00014100468,0.10085278,0.6554854,0.21073087,0.00009632964,0.0055948296,0.0011900859],"about_ca_topic_score_codex":0.00055633264,"about_ca_topic_score_gemma":0.00028602767,"teacher_disagreement_score":0.62923604,"about_ca_system_score_codex":0.00006219822,"about_ca_system_score_gemma":0.000030128325,"threshold_uncertainty_score":0.41949135},"labels":[],"label_agreement":null},{"id":"W3155345140","doi":"10.36001/phmconf.2017.v9i1.2476","title":"Improvement of a Hydrogenerator Prognostic Model by using Partial Discharge Measurement Analysis","year":2017,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"École de Technologie Supérieure; Hydro-Québec","funders":"","keywords":"Engineering; Reliability engineering; Partial discharge; Maintenance engineering; Condition monitoring; Key (lock); Computer science; Voltage","score_opus":0.040079797376934095,"score_gpt":0.28731878368534663,"score_spread":0.24723898630841254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155345140","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":[0.9715484,0.00009164439,0.02729583,0.000139702,0.000065719076,0.00032878987,0.00033360504,0.00006003745,0.0001362902],"genre_scores_gemma":[0.9987018,0.000042124862,0.0011334588,0.000019496056,0.00002155963,0.00004733595,0.0000052091395,0.000017057673,0.000011929573],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988135,0.00001938987,0.00031903395,0.00017612173,0.00045566645,0.00021629104],"domain_scores_gemma":[0.99872905,0.000012898558,0.00023074837,0.0006772106,0.00029556997,0.000054504304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036434445,0.00017199256,0.00032837223,0.00001870796,0.00015445934,0.000047309597,0.0006249471,0.000074958225,0.000014245933],"category_scores_gemma":[0.00009267458,0.0001314745,0.00038712195,0.00009295444,0.00012515952,0.0001751896,0.00019641093,0.00012494539,3.305702e-7],"study_design_candidate":"bench_or_experimental","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":[0.0000085291,0.00034269804,0.032496076,0.00029217484,0.0026524535,2.1821837e-7,0.0038010783,0.030942028,0.921268,0.0005161168,0.005873803,0.0018068594],"study_design_scores_gemma":[0.00012377893,0.000022463697,0.0005604439,0.000040200284,0.00039465885,6.14208e-8,0.000082005026,0.6181048,0.38040778,0.00011252732,0.000030158317,0.00012112406],"about_ca_topic_score_codex":0.00021339735,"about_ca_topic_score_gemma":0.00003338588,"teacher_disagreement_score":0.58716273,"about_ca_system_score_codex":0.000067344205,"about_ca_system_score_gemma":0.00006406063,"threshold_uncertainty_score":0.53613746},"labels":[],"label_agreement":null},{"id":"W3155954770","doi":"10.36001/phmconf.2011.v3i1.1990","title":"Condition Based Maintenance Optimization for Multi-component Systems","year":2011,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Component (thermodynamics); Computer science; Physics","score_opus":0.04395768489596985,"score_gpt":0.2322739693166734,"score_spread":0.18831628442070356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3155954770","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":[0.011961778,0.00003693618,0.98612565,0.00007407506,0.00050398975,0.000599656,0.00015984132,0.00010044966,0.000437624],"genre_scores_gemma":[0.95424086,0.000054987468,0.045377012,0.000047103647,0.000020312873,0.000088142246,0.000039483395,0.000017178392,0.00011494823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993318,0.000026686641,0.0002308795,0.00013250585,0.00010285182,0.00017528844],"domain_scores_gemma":[0.99917835,0.000030576815,0.0000932059,0.00022650373,0.00043943664,0.000031931297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019994125,0.00011504462,0.00015223604,0.000012075759,0.00007682921,0.000017862945,0.00019935599,0.00009366892,0.000028602512],"category_scores_gemma":[0.000053873053,0.000088590554,0.0001599782,0.00009078078,0.00010358963,0.0001934197,0.000019971229,0.000080825215,0.0000016031357],"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":[0.000017368277,0.00006827179,0.00009497417,0.0002622334,0.000038118313,6.624475e-8,0.0030802262,0.9906008,0.0006360729,0.0024936893,0.0025234516,0.00018470747],"study_design_scores_gemma":[0.00047581852,0.000031366875,0.0004369996,0.000103179875,0.00002233066,6.0270435e-7,0.0011603319,0.99437666,0.0028463288,0.00012459258,0.00030855226,0.00011323116],"about_ca_topic_score_codex":0.00004602793,"about_ca_topic_score_gemma":0.000002786483,"teacher_disagreement_score":0.94227904,"about_ca_system_score_codex":0.00005608229,"about_ca_system_score_gemma":0.000035495643,"threshold_uncertainty_score":0.36126179},"labels":[],"label_agreement":null},{"id":"W3156074825","doi":"10.36001/phmconf.2011.v3i1.1994","title":"Comparison of Parallel and Single Neural Networks in Heart Arrhythmia Detection by Using ECG Signal Analysis","year":2011,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Cardiac arrhythmia; Artificial neural network; Computer science; Pattern recognition (psychology); SIGNAL (programming language); Artificial intelligence; Speech recognition; Cardiology; Internal medicine; Medicine","score_opus":0.07046536101610062,"score_gpt":0.3083353862875996,"score_spread":0.23787002527149897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156074825","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":[0.9874032,0.00023661049,0.0121091055,0.0000991569,0.000035895526,0.00006587182,0.000006489599,0.000008392285,0.000035273908],"genre_scores_gemma":[0.9985528,0.000012424702,0.0013200586,0.000025541181,0.00004440949,0.0000015118244,0.000003431035,0.0000058300493,0.00003401557],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916804,0.00006883981,0.00029242408,0.00015980794,0.00015907954,0.00015178738],"domain_scores_gemma":[0.9994015,0.00003985672,0.00017812048,0.00017150656,0.0001592849,0.000049719663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001879992,0.00010047374,0.0004347237,0.000041582174,0.00006039759,0.000008118438,0.000069702204,0.00009253388,0.000017061207],"category_scores_gemma":[0.000019561188,0.00007642115,0.0002821467,0.00046368854,0.00015725695,0.000072836796,0.00004921838,0.00020251946,1.0744324e-7],"study_design_candidate":"observational","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":[0.00008750983,0.00019971296,0.94365287,0.00004332117,0.00059550506,6.4311047e-7,0.007851529,0.005663132,0.032700133,0.0000023472842,0.00006503968,0.009138243],"study_design_scores_gemma":[0.00036907688,0.00022603216,0.07209274,0.00005758241,0.0009529423,0.000003487585,0.005405127,0.90496504,0.015793342,0.000024253994,0.000009668705,0.000100706464],"about_ca_topic_score_codex":0.0012200719,"about_ca_topic_score_gemma":0.00007520118,"teacher_disagreement_score":0.8993019,"about_ca_system_score_codex":0.000025308447,"about_ca_system_score_gemma":0.000018601888,"threshold_uncertainty_score":0.31163642},"labels":[],"label_agreement":null},{"id":"W3157636745","doi":"10.36001/phmconf.2013.v5i1.2301","title":"A New Prognostic Approach for Hydro-generator Stator Windings","year":2013,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Power System Reliability and Maintenance","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Condition-based maintenance; Reliability engineering; Identification (biology); Stator; Condition monitoring; Root cause; Computer science; Context (archaeology); Generator (circuit theory); Root cause analysis; Failure mode and effects analysis; Prognostics; Engineering; Data mining; Power (physics); Mechanical engineering","score_opus":0.014135470617960541,"score_gpt":0.20136747781857633,"score_spread":0.18723200720061578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3157636745","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":[0.7044821,0.00038464178,0.2865575,0.0009442387,0.0009828518,0.002942781,0.00027358683,0.0003336802,0.0030986366],"genre_scores_gemma":[0.9891744,0.000013275503,0.0094952425,0.00008427554,0.0000846225,0.00014349673,0.000006303743,0.000020967735,0.0009774124],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991325,0.000018109877,0.00022683114,0.00017503095,0.0001495068,0.00029801734],"domain_scores_gemma":[0.99926734,0.00006002938,0.000061272054,0.000291383,0.00022390946,0.00009605274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016528588,0.00015100063,0.00021564456,0.000008313927,0.00006930853,0.000047471178,0.00038934432,0.000097599186,0.00003108174],"category_scores_gemma":[0.00008578865,0.00010716295,0.0002259807,0.00011595424,0.00007501058,0.00022727616,0.00006391634,0.00012753799,0.0000104794835],"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":[0.000022632763,0.00021678461,0.0043387976,0.0028027939,0.0007013629,2.7932015e-7,0.07692641,0.003132741,0.04461268,0.0095008565,0.8494044,0.00834026],"study_design_scores_gemma":[0.004445456,0.0006602068,0.012126017,0.0008886027,0.00040097543,0.000028297292,0.029266197,0.74335057,0.09825881,0.017212223,0.09069052,0.0026721566],"about_ca_topic_score_codex":0.00010101329,"about_ca_topic_score_gemma":0.0000025194304,"teacher_disagreement_score":0.7587139,"about_ca_system_score_codex":0.00004273515,"about_ca_system_score_gemma":0.00010473564,"threshold_uncertainty_score":0.43699783},"labels":[],"label_agreement":null},{"id":"W3177843345","doi":"10.36001/phmconf.2017.v9i1.2471","title":"Effect of Ambient Temperature on Performance of Gas Turbine Engine","year":2017,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Technical Engine Diagnostics and Monitoring","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Life Prediction Technologies (Canada)","funders":"","keywords":"Gas compressor; Automotive engineering; Gas turbines; Gas engine; Work (physics); Turbine; Exhaust gas; Fouling; Environmental science; Power (physics); Thrust specific fuel consumption; Engine efficiency; Fuel efficiency; Computer science; Engineering; Mechanical engineering; Internal combustion engine; Compression ratio; Waste management; Thermodynamics; Chemistry","score_opus":0.007845552224044703,"score_gpt":0.23382047570924738,"score_spread":0.22597492348520268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177843345","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":[0.99888295,0.00006839781,0.000017509354,0.00007407808,0.00033913486,0.000121932564,0.000051500152,0.00002909041,0.00041539336],"genre_scores_gemma":[0.9995576,0.00022972537,0.0000859447,0.000004470718,0.000053053864,0.000006648315,0.0000019917895,0.000012346493,0.000048195834],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994276,0.0000103318125,0.00017077626,0.00008614922,0.0001711067,0.00013400165],"domain_scores_gemma":[0.99916315,0.000101160396,0.000094261806,0.0004906389,0.00012055838,0.00003020223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018267921,0.00012308576,0.0002412623,0.000010594504,0.000059524707,0.000010890619,0.00044286074,0.00008884888,0.000005416736],"category_scores_gemma":[0.00016467969,0.000082605446,0.00017181272,0.00004404812,0.00010416384,0.000070114336,0.00009979632,0.00022841024,8.07963e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016058354,0.0002896297,0.01600567,0.004244456,0.00054689485,0.000002342301,0.0047948402,0.0694118,0.84580314,0.0019233518,0.008969117,0.047848154],"study_design_scores_gemma":[0.00034973864,0.00041157284,0.022234816,0.0004507836,0.000033022323,5.0943476e-7,0.00003771848,0.0089865085,0.96715,0.000013803765,0.00022793042,0.00010360821],"about_ca_topic_score_codex":0.000018106755,"about_ca_topic_score_gemma":4.6801756e-7,"teacher_disagreement_score":0.121346824,"about_ca_system_score_codex":0.000014208328,"about_ca_system_score_gemma":0.000013058132,"threshold_uncertainty_score":0.33685523},"labels":[],"label_agreement":null},{"id":"W3215130512","doi":"10.36001/phmconf.2021.v13i1.2988","title":"Noise Factor Analysis for Health Monitoring, with Application to Brake Rotors","year":2021,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Sensor Technology and Measurement Systems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Robustness (evolution); Computer science; Prognostics; Signal processing; Noise (video); Control theory (sociology); Brake; Control engineering; Electronic engineering; Engineering; Artificial intelligence; Data mining; Control (management); Computer hardware; Automotive engineering; Digital signal processing","score_opus":0.04164497100917263,"score_gpt":0.29569058422319044,"score_spread":0.2540456132140178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215130512","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21071069,0.00004642134,0.78174454,0.0066561783,0.00014679828,0.0005322581,0.00003905505,0.000079783385,0.00004429531],"genre_scores_gemma":[0.98738277,0.0000074981267,0.011938217,0.00030253033,0.0000301513,0.00008638051,0.000002454004,0.0000044126978,0.00024556307],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900866,0.000047834823,0.00018421446,0.00030930102,0.00025113142,0.0001988824],"domain_scores_gemma":[0.9984899,0.00003822785,0.00017270441,0.0006836606,0.0005515717,0.00006389822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021121933,0.00009790871,0.00021792209,0.000028083085,0.0001629735,0.000036404537,0.00066061114,0.000065658256,0.0000019940749],"category_scores_gemma":[0.00003949504,0.00006967677,0.00019400343,0.0007838391,0.000043126223,0.00012486352,0.00006157511,0.00008806726,0.0000020177788],"study_design_candidate":"observational","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":[0.0000685042,0.0007438766,0.6476811,0.0004508151,0.0038033277,0.0000014507434,0.09304584,0.0020990605,0.15166402,0.039974682,0.008097691,0.052369595],"study_design_scores_gemma":[0.0011054218,0.00052114663,0.36780038,0.00018946918,0.00024218592,0.000007844778,0.0088389665,0.00812313,0.59776294,0.001048072,0.013695256,0.00066516333],"about_ca_topic_score_codex":0.00008304002,"about_ca_topic_score_gemma":0.00005372631,"teacher_disagreement_score":0.7766721,"about_ca_system_score_codex":0.000050555966,"about_ca_system_score_gemma":0.00020390196,"threshold_uncertainty_score":0.2841336},"labels":[],"label_agreement":null},{"id":"W3217650763","doi":"10.36001/phmconf.2021.v13i1.3052","title":"On failure prediction and failure identification modeling in a gas turbine system: a survey of classification approaches in a three-class problem","year":2021,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Engineering Diagnostics and Reliability","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","funders":"Ministère de la Défense Nationale; University of Toronto; National Research Council Canada; Defence Research and Development Canada","keywords":"Computer science; Identification (biology); Class (philosophy); Data collection; Set (abstract data type); Warning system; Data set; Data mining; Machine learning; Event (particle physics); Artificial intelligence; Reliability engineering; Engineering","score_opus":0.04441908455242971,"score_gpt":0.2144957241773948,"score_spread":0.17007663962496508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217650763","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":[0.99082243,0.00007579864,0.007553863,0.0002106914,0.000057621586,0.00026603753,0.00096248445,0.000025950274,0.000025121088],"genre_scores_gemma":[0.99921495,0.000042403557,0.0004116821,0.0000016110272,0.000006911948,0.000032248754,0.00027855058,0.000009298619,0.0000023426069],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905044,0.00007412809,0.00040783774,0.00018998771,0.0001642344,0.000113368136],"domain_scores_gemma":[0.9992769,0.00011474783,0.00007500991,0.00027627219,0.00023540678,0.000021667332],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067117123,0.000108179695,0.00020585739,0.00003028092,0.00002115455,0.000021203294,0.000120914556,0.00013375618,0.000001230961],"category_scores_gemma":[0.00017884115,0.000088839,0.000057242625,0.00032120093,0.000043659194,0.000087101966,0.000037840262,0.00022905538,3.8565338e-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":[0.000012839986,0.00021171325,0.02751585,0.0018287529,0.00006444768,4.7182928e-7,0.004919747,0.9513771,0.009562563,0.0035467707,0.0005768741,0.00038287928],"study_design_scores_gemma":[0.00019380022,0.000015179786,0.078492865,0.00030774483,0.000011014481,0.000001054082,0.0013432385,0.9182634,0.0008550109,0.00044100606,0.0000040492523,0.00007163387],"about_ca_topic_score_codex":0.00020105156,"about_ca_topic_score_gemma":0.0012100607,"teacher_disagreement_score":0.050977018,"about_ca_system_score_codex":0.00007225099,"about_ca_system_score_gemma":0.000046326048,"threshold_uncertainty_score":0.36227491},"labels":[],"label_agreement":null},{"id":"W4307715539","doi":"10.36001/phmconf.2022.v14i1.3142","title":"A Framework to Rank Prognostics Health Indicators with Application to Brake Rotors","year":2022,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Machine Fault Diagnosis Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Prognostics; Identifiability; Robustness (evolution); Brake; Monotonic function; Reliability engineering; Computer science; Engineering; Statistics; Automotive engineering; Mathematics","score_opus":0.009249970534846398,"score_gpt":0.2782051735100188,"score_spread":0.2689552029751724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307715539","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":[0.7656321,0.000084811545,0.21494748,0.012633846,0.00019693682,0.0044964263,0.00057730224,0.0008457788,0.0005853139],"genre_scores_gemma":[0.98038226,0.000014580565,0.016780006,0.0016245504,0.00003136159,0.0011032774,0.000011214465,0.00003185286,0.000020896827],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989019,0.000052008527,0.00021841312,0.00020376546,0.00037168252,0.0002522355],"domain_scores_gemma":[0.99919283,0.00008538985,0.00008349009,0.00043249162,0.00007847192,0.00012733525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030154912,0.00014991067,0.00020610605,0.000045960674,0.000181392,0.00002131041,0.00060337724,0.000050478044,0.00003587203],"category_scores_gemma":[0.000070973394,0.00012172551,0.000085306165,0.0007637601,0.000040673276,0.000051356707,0.00026311562,0.00039445653,0.000004007983],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001225114,0.00093660114,0.12631585,0.0008922483,0.0005689216,0.0000018842516,0.23315015,0.06532335,0.0021216094,0.03021931,0.41702467,0.12332288],"study_design_scores_gemma":[0.002094155,0.0062361863,0.22787714,0.0015449846,0.00027506167,0.000035745434,0.038320538,0.038718432,0.05978187,0.013838465,0.6069784,0.0042989906],"about_ca_topic_score_codex":0.00012660072,"about_ca_topic_score_gemma":0.000021118523,"teacher_disagreement_score":0.21475016,"about_ca_system_score_codex":0.0001398566,"about_ca_system_score_gemma":0.00010791911,"threshold_uncertainty_score":0.49638224},"labels":[],"label_agreement":null},{"id":"W4307715675","doi":"10.36001/phmconf.2022.v14i1.3168","title":"Voltage-Based Physical Layer Fault Diagnosis for Controller Area Network","year":2022,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Real-Time Systems Scheduling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Troubleshooting; Reliability (semiconductor); Fault (geology); Physical layer; Controller (irrigation); Automotive industry; Computer science; CAN bus; Voltage; Real-time computing; Reliability engineering; Embedded system; Engineering; Computer network; Electrical engineering; Wireless; Telecommunications; Power (physics)","score_opus":0.03614686357989949,"score_gpt":0.262169568752326,"score_spread":0.2260227051724265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307715675","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.26536223,0.00018291423,0.72161305,0.007864665,0.0014480251,0.0020808212,0.00029036624,0.00024259715,0.00091530813],"genre_scores_gemma":[0.9930735,0.000002805406,0.0049255635,0.0008394911,0.00020034709,0.000584262,0.000005367845,0.000016275308,0.00035236654],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980491,0.00019238093,0.00030964243,0.0004329275,0.00057048385,0.00044546812],"domain_scores_gemma":[0.9977848,0.00072988326,0.00031877036,0.00067280675,0.00041498762,0.00007873851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074134546,0.00020145015,0.0003979957,0.000014488334,0.0005724588,0.0001240744,0.0018440131,0.000058309695,0.000028268409],"category_scores_gemma":[0.000110851426,0.00015292535,0.000677528,0.00039549964,0.000112388145,0.00029953307,0.0007032749,0.00029142047,0.000004719822],"study_design_candidate":"simulation_or_modeling","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":[0.00023659068,0.0022521329,0.012881244,0.00035025133,0.0011772029,0.0000071213367,0.06457384,0.33128715,0.01174723,0.1446837,0.40907145,0.021732086],"study_design_scores_gemma":[0.0016925201,0.00034280977,0.00093409524,0.00006273471,0.0000574601,0.0000033324282,0.0019531166,0.97049326,0.0029917015,0.008683183,0.012397895,0.00038787405],"about_ca_topic_score_codex":0.00009867299,"about_ca_topic_score_gemma":0.00000369091,"teacher_disagreement_score":0.72771126,"about_ca_system_score_codex":0.00007670414,"about_ca_system_score_gemma":0.00026764703,"threshold_uncertainty_score":0.6236115},"labels":[],"label_agreement":null},{"id":"W4321462109","doi":"10.36001/phmconf.2022.v14i1.3180","title":"Fault Injection Method and Ground-truth Development to Enable a Low-cost Bearing Fault Monitoring System in the Automotive Industry","year":2022,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Gear and Bearing Dynamics Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Bearing (navigation); Automotive engineering; Fault (geology); Automotive industry; Prognostics; Ground truth; Engineering; Computer science; Reliability engineering; Geology; Artificial intelligence; Aerospace engineering","score_opus":0.01979919208925357,"score_gpt":0.25048705531226073,"score_spread":0.23068786322300716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321462109","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":[0.99289674,0.000024063473,0.0061165197,0.00007840589,0.00022890823,0.00021079619,0.000021916394,0.000048820537,0.00037382287],"genre_scores_gemma":[0.99851346,0.0000037591126,0.0012323259,0.000023772975,0.000031979118,0.000070521135,0.0000034503037,0.000012828344,0.00010791716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.99899316,0.00009878933,0.00021141728,0.00017983819,0.0002972776,0.00021954004],"domain_scores_gemma":[0.99954224,0.000075014425,0.00005262468,0.00019321869,0.000100179226,0.00003675413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007739282,0.00012762098,0.00017717326,0.000045515393,0.00029595182,0.00007263552,0.0003269216,0.00008284714,0.0000049457335],"category_scores_gemma":[0.000031495903,0.00009708202,0.00008528735,0.00048713505,0.000020097765,0.00009435387,0.00029651748,0.0006365406,0.0000012550864],"study_design_candidate":"simulation_or_modeling","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":[0.000011295202,0.00007922569,0.015233676,0.0002968788,0.00035905605,0.000004252605,0.19023491,0.77773666,0.0021332449,0.00056134077,0.00017843989,0.013171001],"study_design_scores_gemma":[0.00063999643,0.000085803826,0.12440906,0.00038309547,0.00015429317,0.000059209055,0.4690079,0.39747068,0.0059870062,0.00024039748,0.0009071752,0.00065538293],"about_ca_topic_score_codex":0.00057981804,"about_ca_topic_score_gemma":0.000022362241,"teacher_disagreement_score":0.38026598,"about_ca_system_score_codex":0.00027134013,"about_ca_system_score_gemma":0.00005421513,"threshold_uncertainty_score":0.39588898},"labels":[],"label_agreement":null},{"id":"W4388115827","doi":"10.36001/phmconf.2023.v15i1.3517","title":"Ensemble Learning Based Convolutional Neural Networks for Remaining Useful Life Prediction of Aircraft Engines","year":2023,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Advanced Sensor Technologies Research","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Prognostics; Convolutional neural network; Computer science; Turbofan; Artificial intelligence; Ensemble learning; Deep learning; Machine learning; Hyperparameter; Artificial neural network; Function (biology); Feature (linguistics); Data mining; Engineering","score_opus":0.04598939354175328,"score_gpt":0.26545962604512974,"score_spread":0.21947023250337647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388115827","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":[0.7405803,0.0001186945,0.25676218,0.0005938308,0.00035199957,0.0004490369,0.00018068818,0.0008685875,0.000094648305],"genre_scores_gemma":[0.99808973,0.00006378655,0.0015428384,0.000016906986,0.000060534356,0.000037877613,0.000032090815,0.000025566133,0.0001306635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990048,0.00003888704,0.00024323098,0.00015026894,0.00023919856,0.00032366288],"domain_scores_gemma":[0.99882334,0.0004996188,0.000073042844,0.00021704695,0.00034815705,0.00003878296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031138997,0.000121677396,0.0001980489,0.000042755484,0.00012128998,0.000010654429,0.0003023336,0.00014609659,0.000008531265],"category_scores_gemma":[0.0007990823,0.00010636078,0.00019168684,0.00041321854,0.00018440888,0.00012979253,0.00010243521,0.0003662397,0.000001034529],"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":[0.000017007991,0.000007383767,0.0024709718,0.00010957621,0.000050167964,1.7930842e-7,0.00061301707,0.9839493,0.00970404,0.00016515912,0.0021050037,0.0008081989],"study_design_scores_gemma":[0.0002927555,0.00006748506,0.005947272,0.000045952846,0.000011500388,6.1792537e-7,0.0024240492,0.9850428,0.0054266606,0.00021018225,0.000447275,0.00008345013],"about_ca_topic_score_codex":0.000005383594,"about_ca_topic_score_gemma":0.0000017678585,"teacher_disagreement_score":0.25750938,"about_ca_system_score_codex":0.000039675302,"about_ca_system_score_gemma":0.00004991166,"threshold_uncertainty_score":0.43372667},"labels":[],"label_agreement":null},{"id":"W4388115851","doi":"10.36001/phmconf.2023.v15i1.3521","title":"Cooling Fan Failure Modes to Enable Development of Automotive ECU Fan Health Monitoring System","year":2023,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Refrigeration and Air Conditioning Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Overheating (electricity); Automotive engineering; Automotive industry; Turbofan; Engineering; Failure mode and effects analysis; Electronic control unit; Fan-in; Catastrophic failure; Fault detection and isolation; Airflow; Preventive maintenance; Reliability engineering; Mechanical engineering; Electrical engineering; Actuator","score_opus":0.030061651021571042,"score_gpt":0.2563427106758093,"score_spread":0.22628105965423828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388115851","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":[0.9806838,0.000041337473,0.016489556,0.0008665709,0.00024236785,0.00027376137,0.00006264353,0.0009664592,0.00037348302],"genre_scores_gemma":[0.9921417,0.000017738972,0.0076431767,0.000013002942,0.000021666674,0.000026126781,0.0000066393477,0.000012940135,0.00011696098],"study_design_codex":"qualitative","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9991656,0.000019646002,0.00027926767,0.00012491486,0.0001893243,0.0002212782],"domain_scores_gemma":[0.9994313,0.000029863757,0.000081422324,0.00020084808,0.00021569752,0.000040853753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002228594,0.000114072995,0.00020536706,0.000043365122,0.00018743581,0.000029284336,0.0002909058,0.000071412825,0.0000030934323],"category_scores_gemma":[0.00003804641,0.00009287912,0.00008177833,0.0003502669,0.000042668587,0.00011167989,0.00012620578,0.00013686778,0.0000094637935],"study_design_candidate":"bench_or_experimental","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":[0.000018997764,0.00010098609,0.0040475726,0.0039491244,0.0010116559,0.0000031048598,0.42283452,0.30869663,0.16620809,0.0222291,0.03966121,0.03123902],"study_design_scores_gemma":[0.00023223093,0.000054785796,0.005314319,0.0012055136,0.000011381086,0.000001636336,0.17455767,0.029844183,0.7868756,0.00032966884,0.0012711597,0.0003018758],"about_ca_topic_score_codex":0.000021947137,"about_ca_topic_score_gemma":0.0000071572335,"teacher_disagreement_score":0.6206675,"about_ca_system_score_codex":0.000117381525,"about_ca_system_score_gemma":0.00012702237,"threshold_uncertainty_score":0.37875006},"labels":[],"label_agreement":null},{"id":"W4388115866","doi":"10.36001/phmconf.2023.v15i1.3509","title":"Promoting Explainability in Data-Driven Models for Anomaly Detection: A Step Toward Diagnosis","year":2023,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Interpretability; Anomaly detection; Computer science; False positive paradox; Anomaly (physics); Transparency (behavior); Data mining; Artificial intelligence; Machine learning; Data science; Computer security","score_opus":0.0977526734080074,"score_gpt":0.31089493756523984,"score_spread":0.21314226415723242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388115866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16473877,0.000013485407,0.8288388,0.0049353805,0.00008174588,0.0008921981,0.00014577605,0.0002852483,0.00006854316],"genre_scores_gemma":[0.98302275,0.000038407667,0.016140835,0.00007660615,0.00002702596,0.0006129562,0.000007297745,0.0000072541193,0.00006688784],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988068,0.000055977394,0.00028096946,0.0004452362,0.0001764382,0.00023461677],"domain_scores_gemma":[0.9984169,0.00015494403,0.00015163768,0.0009672231,0.00026972257,0.000039584225],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006018035,0.00011025895,0.00016704854,0.00003056088,0.00020307797,0.00006724776,0.0016360459,0.0000817582,0.0000028740803],"category_scores_gemma":[0.00008385728,0.000090549045,0.00015591066,0.0007544663,0.00009284769,0.00072034233,0.0009999397,0.00014210945,0.000002088369],"study_design_candidate":"simulation_or_modeling","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":[0.000055197546,0.0013202453,0.014867816,0.0011322077,0.00029189856,0.0000033239353,0.17920102,0.0043607205,0.0068651224,0.10225521,0.02551898,0.66412824],"study_design_scores_gemma":[0.00016886837,0.00006774606,0.002818987,0.00003162982,0.000008681284,0.0000016355149,0.0016398295,0.96405506,0.008191067,0.021271417,0.0016057417,0.00013933078],"about_ca_topic_score_codex":0.00016963191,"about_ca_topic_score_gemma":0.00006888766,"teacher_disagreement_score":0.9596943,"about_ca_system_score_codex":0.000051141556,"about_ca_system_score_gemma":0.00010847076,"threshold_uncertainty_score":0.3692483},"labels":[],"label_agreement":null},{"id":"W4388115954","doi":"10.36001/phmconf.2023.v15i1.3525","title":"Accelerated Degradation Test on Electric Scroll Compressor Using Controlled Continuous Liquid Slugging","year":2023,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Refrigeration and Air Conditioning Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Refrigerant; Gas compressor; Heat pump; Scroll compressor; Heat exchanger; Mechanical engineering; Engineering; Automotive engineering; Materials science","score_opus":0.03987084803179233,"score_gpt":0.26041141223949543,"score_spread":0.2205405642077031,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388115954","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":[0.9956133,0.000039617833,0.0012907775,0.00055730355,0.00020673788,0.0003470003,0.000048518017,0.00098157,0.0009152018],"genre_scores_gemma":[0.99920124,0.000074407755,0.00020443604,0.0000844508,0.00003145483,0.000025440082,0.00002262451,0.000019197929,0.0003367448],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991119,0.000034476416,0.00027016355,0.00014711029,0.000195774,0.00024056564],"domain_scores_gemma":[0.99919105,0.00019873204,0.000109645356,0.0002351523,0.0002405458,0.000024853984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018048949,0.00015540344,0.00027747726,0.000066516186,0.0002188119,0.00008547564,0.0002969457,0.00011724137,0.000025306676],"category_scores_gemma":[0.00023665155,0.000117945485,0.00016605105,0.0005911988,0.000069074624,0.00017549508,0.000056218985,0.0002383903,0.000017113573],"study_design_candidate":"bench_or_experimental","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":[0.00014711339,0.00014017292,0.000911798,0.00013653637,0.0004261312,0.000003987645,0.0033732357,0.07657238,0.88062334,0.004421802,0.03063615,0.0026073384],"study_design_scores_gemma":[0.0021127418,0.0001916347,0.0018573247,0.00015772771,0.00005268467,0.0000033015174,0.0015725045,0.7621699,0.23027396,0.00064775464,0.00064145605,0.00031899769],"about_ca_topic_score_codex":0.000012351577,"about_ca_topic_score_gemma":0.0000013144906,"teacher_disagreement_score":0.68559754,"about_ca_system_score_codex":0.000049246435,"about_ca_system_score_gemma":0.000046802063,"threshold_uncertainty_score":0.4809677},"labels":[],"label_agreement":null},{"id":"W4388115974","doi":"10.36001/phmconf.2023.v15i1.3520","title":"Automotive Electronic Control Unit Ground Line Health Monitoring Method","year":2023,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Real-time simulation and control systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Automotive engineering; Automotive industry; CAN bus; Electronic control unit; Engineering; Fault (geology); Reliability (semiconductor); Controller (irrigation); Embedded system; Computer science; Real-time computing; Reliability engineering; Electrical engineering","score_opus":0.030949480584504738,"score_gpt":0.30361378273017914,"score_spread":0.2726643021456744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388115974","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":[0.75112784,0.0019218156,0.22102232,0.009636619,0.0033537217,0.002852294,0.00043662914,0.0031572909,0.0064914455],"genre_scores_gemma":[0.99859244,0.00013944654,0.00014117965,0.00008881103,0.00016148979,0.000022165526,0.000007091431,0.000025728465,0.00082164025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986798,0.00015946919,0.00032060593,0.00015830465,0.00025201836,0.0004297707],"domain_scores_gemma":[0.9990891,0.00019421658,0.00011135605,0.000273235,0.00025783142,0.00007420985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007271879,0.00016117611,0.00032746856,0.000028030647,0.00013345764,0.00004493392,0.00029381996,0.00008712563,0.000021192038],"category_scores_gemma":[0.00004582663,0.00012735589,0.00023419963,0.00040360657,0.00003975484,0.00016953576,0.000041529187,0.00028309497,0.00002864929],"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":[0.000087798064,0.000154533,0.006921277,0.00087989273,0.002855351,0.000003034744,0.08473381,0.78275186,0.020627175,0.017530242,0.013047151,0.07040785],"study_design_scores_gemma":[0.0017767062,0.0001512355,0.024579993,0.00015574043,0.000054509957,0.0000037254451,0.010662036,0.95481265,0.0014273775,0.0018659091,0.0041808854,0.00032923018],"about_ca_topic_score_codex":0.00022564502,"about_ca_topic_score_gemma":0.000011432955,"teacher_disagreement_score":0.24746458,"about_ca_system_score_codex":0.00010380282,"about_ca_system_score_gemma":0.00014980018,"threshold_uncertainty_score":0.51934224},"labels":[],"label_agreement":null},{"id":"W4388115977","doi":"10.36001/phmconf.2023.v15i1.3526","title":"Failure Mode Investigation to Enable LiDAR Health Monitoring for Automotive Application","year":2023,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Lidar; Ranging; Remote sensing; Computer science; Failure mode and effects analysis; Environmental science; Reliability engineering; Engineering; Telecommunications; Geology","score_opus":0.03171605501514125,"score_gpt":0.3129996263042539,"score_spread":0.28128357128911263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388115977","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":[0.73638207,0.000005896432,0.2387123,0.023164267,0.000121589765,0.0009459124,0.000218945,0.00030930204,0.0001397401],"genre_scores_gemma":[0.971783,0.0000015581821,0.027834548,0.000060666855,0.000074056385,0.00006656677,0.000017602706,0.000009426009,0.00015257268],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993693,0.000013763308,0.00013507465,0.00017092862,0.000104340674,0.00020657289],"domain_scores_gemma":[0.9992915,0.00006924659,0.000106744745,0.00022327853,0.00026675186,0.00004252678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011023666,0.00008383085,0.00012614572,0.000012246827,0.00017582266,0.000018603669,0.00021276732,0.00003849806,0.0000011975368],"category_scores_gemma":[0.00004878307,0.00006732779,0.00009039589,0.0002869981,0.0000810504,0.00010931658,0.00013798417,0.00010716192,0.000007030651],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004046281,0.00020201356,0.037364915,0.00029251009,0.00038211956,1.041782e-7,0.08714715,0.034188244,0.13825057,0.4936627,0.044623997,0.16384523],"study_design_scores_gemma":[0.00041426576,0.0001708049,0.0051382417,0.00019487861,0.000026443284,1.303693e-7,0.042342156,0.03274434,0.3693497,0.5455989,0.0036728727,0.0003472252],"about_ca_topic_score_codex":0.0001829325,"about_ca_topic_score_gemma":0.0000024505107,"teacher_disagreement_score":0.23540094,"about_ca_system_score_codex":0.000040404288,"about_ca_system_score_gemma":0.0000796765,"threshold_uncertainty_score":0.27455476},"labels":[],"label_agreement":null},{"id":"W4388183399","doi":"10.36001/phmconf.2023.v15i1.3532","title":"Using Charge Determination Design of Experiments to Develop A Refrigerant Charge Health Status Model for Heat Pump Systems","year":2023,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Refrigeration and Air Conditioning Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Refrigerant; Air source heat pumps; Heat pump; Condenser (optics); Coefficient of performance; Gas compressor; Intercooler; Heat exchanger; Hybrid heat; Superheating; Mechanical engineering; Water cooling; Thermodynamics; Nuclear engineering; Engineering; Process engineering; Automotive engineering","score_opus":0.14747363287455645,"score_gpt":0.3367796835041672,"score_spread":0.18930605062961076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388183399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32666224,0.000083092,0.67089796,0.000365198,0.000288601,0.0009889007,0.0003573086,0.00032650662,0.0000301564],"genre_scores_gemma":[0.9875965,0.00009373082,0.011914266,0.00004607792,0.000012816041,0.00011577829,0.000023795326,0.00001939537,0.00017761947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905,0.000030976895,0.00032364158,0.00014004072,0.00017712632,0.00027818227],"domain_scores_gemma":[0.999282,0.00004020215,0.00008386982,0.00020308184,0.00034718544,0.00004366802],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003204634,0.00012256941,0.00022786135,0.000053970125,0.00014663684,0.00002920361,0.00020075451,0.00007885727,0.0000035854584],"category_scores_gemma":[0.00007414443,0.00010122389,0.00007205184,0.00035075052,0.000038106205,0.0001509595,0.000058244073,0.00007094502,0.000003321614],"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":[0.000029080313,0.00007159972,0.000104431994,0.00089188816,0.0001654715,2.951023e-7,0.07631984,0.4995652,0.40087122,0.005480102,0.014177062,0.0023238026],"study_design_scores_gemma":[0.00017696086,0.00003974032,0.000039199847,0.00013744619,0.0000048466236,4.3889895e-7,0.001832622,0.93371916,0.06369184,0.00015532257,0.00010059724,0.00010183627],"about_ca_topic_score_codex":0.000031725864,"about_ca_topic_score_gemma":0.0000011878177,"teacher_disagreement_score":0.66093427,"about_ca_system_score_codex":0.000115921976,"about_ca_system_score_gemma":0.00018527427,"threshold_uncertainty_score":0.41277906},"labels":[],"label_agreement":null},{"id":"W4393029470","doi":"10.36001/phmconf.2014.v6i1.2353","title":"Learning Diagnosis Based on Evolving Fuzzy Finite State Automaton","year":2014,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Advanced Data Processing Techniques","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":"Ontario Tech University","funders":"","keywords":"Computer science; Fuzzy logic; Fuzzy set operations; Ambiguity; Fuzzy number; Defuzzification; Neuro-fuzzy; Fuzzy set; Event (particle physics); USable; Artificial intelligence; Fuzzy control system; Data mining","score_opus":0.012393940722428378,"score_gpt":0.2362482723268687,"score_spread":0.22385433160444032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393029470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37606832,0.00020547185,0.604958,0.0011190585,0.0005306188,0.00058387313,0.00031543412,0.004232843,0.011986393],"genre_scores_gemma":[0.9869225,0.00008019035,0.012674626,0.0001606603,0.000023785688,0.000031177948,0.00001188778,0.000033106346,0.0000620426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911225,0.000056708937,0.00019214522,0.00017602985,0.00022400715,0.00023885517],"domain_scores_gemma":[0.99900776,0.0003340747,0.00009359139,0.00036804378,0.00015347918,0.000043041917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002825283,0.000167764,0.0001844725,0.000022450105,0.00015323835,0.00004749238,0.0004166238,0.000075670716,0.000024977406],"category_scores_gemma":[0.00039870295,0.00013655108,0.00012112737,0.00016541041,0.000098239194,0.0002816503,0.000104516745,0.00036303228,0.000008393526],"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":[0.000029292676,0.00020287269,0.017535793,0.0011007717,0.00017476382,0.000003096869,0.025161987,0.7245422,0.005992542,0.0016535863,0.042852994,0.18075009],"study_design_scores_gemma":[0.00022273474,0.000114811985,0.0019755613,0.00045039257,0.000016034408,6.844052e-7,0.00041422332,0.95529455,0.030391878,0.0039316947,0.0068835435,0.00030386786],"about_ca_topic_score_codex":0.00001592825,"about_ca_topic_score_gemma":0.000002735093,"teacher_disagreement_score":0.6108542,"about_ca_system_score_codex":0.000047605034,"about_ca_system_score_gemma":0.000032032098,"threshold_uncertainty_score":0.55683917},"labels":[],"label_agreement":null},{"id":"W4404289411","doi":"10.36001/phmconf.2024.v16i1.3918","title":"Health Indicator Development for Low-Voltage Battery Diagnostics and Prognostics in Electric Vehicles","year":2024,"lang":"en","type":"article","venue":"Annual Conference of the PHM Society","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"Prognostics; Low voltage; Battery (electricity); Automotive engineering; Voltage; Computer science; Reliability engineering; Engineering; Electrical engineering; Physics; Power (physics)","score_opus":0.020956002738014987,"score_gpt":0.27729267174209654,"score_spread":0.25633666900408153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404289411","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":[0.97226244,0.0028963333,0.022267928,0.001407291,0.00012011438,0.0006918962,0.00010169315,0.00022921892,0.000023055758],"genre_scores_gemma":[0.9953132,0.0017160581,0.0027253611,0.000081968734,0.000015850843,0.000094205345,0.000006314917,0.00002223876,0.000024767161],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9990848,0.000012649996,0.00023203202,0.0001719174,0.00015917484,0.0003394537],"domain_scores_gemma":[0.9993451,0.0003803414,0.000028953837,0.00015388953,0.00005207194,0.000039647948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021780578,0.00012304763,0.00017675293,0.00005826938,0.00006142977,0.000036756206,0.00027812753,0.00009216674,0.0000021753685],"category_scores_gemma":[0.00018930633,0.000097867116,0.000046774276,0.00028966964,0.00010487851,0.00010414903,0.00016105473,0.000326863,0.0000021306714],"study_design_candidate":"design_other","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":[0.000026609398,0.00027270112,0.05005797,0.009546814,0.00045523327,0.000011357763,0.03612957,0.0015796282,0.020457458,0.003857608,0.051355183,0.82624984],"study_design_scores_gemma":[0.002637258,0.0010283729,0.32817855,0.0054854266,0.00008689713,0.000039402556,0.017149583,0.25098798,0.31763825,0.028905509,0.04527714,0.0025856562],"about_ca_topic_score_codex":0.0000038420976,"about_ca_topic_score_gemma":0.0000073576807,"teacher_disagreement_score":0.8236642,"about_ca_system_score_codex":0.00012561271,"about_ca_system_score_gemma":0.00020458482,"threshold_uncertainty_score":0.3990905},"labels":[],"label_agreement":null},{"id":"W4415551264","doi":"10.36001/phmconf.2025.v17i1.4315","title":"Maintenance, Engineering, and Operational Decision-Making Metrics Derived from Simple Maintenance and Aircraft Datasets","year":2025,"lang":"","type":"article","venue":"Annual Conference of the PHM Society","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Unavailability; Reliability (semiconductor); Component (thermodynamics); Set (abstract data type); Maintenance actions; Downtime; Predictive maintenance; Dependability; Maintenance engineering","score_opus":0.00887361509470956,"score_gpt":0.2367908826973616,"score_spread":0.22791726760265205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415551264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3274986,0.0030396017,0.6593296,0.0020190317,0.0012080206,0.0009756682,0.005458987,0.000097373006,0.0003730923],"genre_scores_gemma":[0.9627196,0.004593314,0.0319484,0.0004184853,0.00005178295,0.00002220972,0.00010943245,0.000029090997,0.000107655214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99773085,0.000064094915,0.0007123162,0.00063423207,0.00035036114,0.00050815346],"domain_scores_gemma":[0.99752766,0.00088427507,0.00018551314,0.00067314075,0.0006194876,0.000109925226],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005183523,0.00043261828,0.0005719865,0.00007951787,0.00031458924,0.00023550833,0.0005730969,0.00031649446,0.0000739763],"category_scores_gemma":[0.0018261451,0.0003622878,0.00020842189,0.0006963993,0.00045286614,0.00065932714,0.0007690085,0.00053005986,0.0000019165493],"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":[0.0005346655,0.000646732,0.02033038,0.0031457904,0.0032156697,0.000018124596,0.030881196,0.5330202,0.012288634,0.07092137,0.19499867,0.12999856],"study_design_scores_gemma":[0.0011829525,0.000055100303,0.02450593,0.0018696667,0.00021567372,0.0000063090597,0.0030702075,0.947894,0.0008350731,0.009279933,0.0105351955,0.00054997636],"about_ca_topic_score_codex":0.0002047939,"about_ca_topic_score_gemma":0.000047350073,"teacher_disagreement_score":0.635221,"about_ca_system_score_codex":0.00014605747,"about_ca_system_score_gemma":0.0002391361,"threshold_uncertainty_score":0.99988294},"labels":[],"label_agreement":null}]}