{"meta":{"query_hash":"dcae4ae1c331","filters":{"topic":"Technical Engine Diagnostics and Monitoring"},"cohort_total":21,"direct_labels_cover":0,"predictions_cover":21,"exported":21,"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/dcae4ae1c331","api":"https://metacan.xera.ac/api/v1/cohort?topic=Technical+Engine+Diagnostics+and+Monitoring"},"results":[{"id":"W1520843488","doi":"10.4271/2000-01-0671","title":"Development of the High Speed 2ZZ-GE Engine","year":2000,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Technical Engine Diagnostics and Monitoring","field":"Engineering","cited_by":9,"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":"Yamaha Motor Company","keywords":"Computer science; Automotive engineering; Electrical engineering; Engineering","score_opus":0.008009990490887733,"score_gpt":0.2118708071861379,"score_spread":0.20386081669525016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1520843488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2878479,0.0021546127,0.6038107,0.00048291238,0.00094574,0.0026101116,0.0015693405,0.0058458713,0.09473277],"genre_scores_gemma":[0.5026213,0.0024876264,0.39052045,0.00021993092,0.00014877538,0.0009167547,0.004837927,0.0005347414,0.09771258],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994404,0.000023303668,0.000017589091,0.00007440707,0.00039288058,0.000051378127],"domain_scores_gemma":[0.9997911,0.000010886155,0.000010641312,0.000020058751,0.0001355924,0.000031739473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006490802,0.00053329085,0.00044954696,0.0007027351,0.00030716942,0.0006648568,0.0011565275,0.0006551423,0.0037197412],"category_scores_gemma":[0.0004184056,0.00040738858,0.0004943779,0.00029948752,0.00017566264,0.0007148113,0.0007839646,0.00060166186,0.00277816],"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":[0.00030369454,0.0004712024,0.0048936633,0.00063078385,0.00006394448,0.000703946,0.0001982031,0.01929973,0.75543535,0.009726603,0.009140291,0.19913264],"study_design_scores_gemma":[0.00017282683,0.0035508447,0.02443855,0.000093114984,0.000083968735,0.0018705428,0.00011611678,0.088319264,0.6573608,0.0006417726,0.22313109,0.00022112287],"about_ca_topic_score_codex":0.0021496466,"about_ca_topic_score_gemma":0.0036371064,"teacher_disagreement_score":0.0037197412,"about_ca_system_score_codex":0.00034008693,"about_ca_system_score_gemma":0.00083701056,"threshold_uncertainty_score":0.012443781},"labels":[],"label_agreement":null},{"id":"W1945302649","doi":"10.1115/gt2015-42078","title":"Assessment of Recoverable vs Unrecoverable Degradations of Gas Turbines Employed in Five Natural Gas Compressor Stations","year":2015,"lang":"en","type":"article","venue":"Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy","topic":"Technical Engine Diagnostics and Monitoring","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":true,"ca_institutions":"TransCanada (Canada); Nova Chemicals (Canada)","funders":"","keywords":"Gas compressor; Gas turbines; Natural gas; Automotive engineering; Gas engine; Compressor station; Combined cycle; Engineering; Environmental science; Mechanical engineering; Waste management","score_opus":0.009542023274207001,"score_gpt":0.2466492315117088,"score_spread":0.2371072082375018,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1945302649","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99920565,0.000029716348,0.00026891392,0.0000024217454,9.325466e-7,0.000011407209,0.00024137057,0.000008427785,0.0002312411],"genre_scores_gemma":[0.9987815,0.000051820636,0.00042251396,0.000002811294,5.5645336e-7,0.0000058174255,0.00031632715,0.000002487846,0.00041616205],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99962735,0.000020970234,0.000027649894,0.000067714835,0.00020189557,0.00005448179],"domain_scores_gemma":[0.99948883,0.00006954032,0.00011330141,0.000025128327,0.00026706027,0.00003611873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031057675,0.0003726672,0.00032845963,0.0010082466,0.00028228317,0.00034904305,0.0003896614,0.0003782095,0.00036527542],"category_scores_gemma":[0.00066442956,0.00014700965,0.00027599602,0.0008072516,0.0002842384,0.0002364564,0.00026287115,0.00020994994,0.00014866941],"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":[0.00096427946,0.00026267776,0.70063484,0.00025858387,0.00013300187,0.0008970434,0.0016385218,0.017520854,0.22434455,0.00013962357,0.00035359515,0.052852396],"study_design_scores_gemma":[0.0000056546965,0.00061545393,0.94321394,0.000008294666,0.00004861188,0.00019437558,0.0008272062,0.008028966,0.04644377,0.000022326463,0.0005730302,0.000018420327],"about_ca_topic_score_codex":0.05515746,"about_ca_topic_score_gemma":0.1123524,"teacher_disagreement_score":0.05515746,"about_ca_system_score_codex":0.00082884484,"about_ca_system_score_gemma":0.00037337947,"threshold_uncertainty_score":0.109672785},"labels":[],"label_agreement":null},{"id":"W1970222692","doi":"10.1115/2001-gt-0548","title":"Development of Fault Diagnosis and Failure Prediction Techniques for Small Gas Turbine Engines","year":2001,"lang":"en","type":"article","venue":"Volume 1: Aircraft Engine; Marine; Turbomachinery; Microturbines and Small Turbomachinery","topic":"Technical Engine Diagnostics and Monitoring","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gas turbines; Fault (geology); Turbine; Operating point; Combined cycle; Automotive engineering; Reliability engineering; Power (physics); Gas engine; Engineering; Computer science; Mechanical engineering; Electrical engineering","score_opus":0.009817451845324916,"score_gpt":0.20465885937243378,"score_spread":0.19484140752710885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970222692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057863828,0.00018499655,0.93975675,0.00007089671,0.000017281758,0.000051449497,0.0000489223,0.0013923561,0.0006134882],"genre_scores_gemma":[0.6645475,0.00018248869,0.33395857,0.00001885441,0.000015488109,0.000114250455,0.00007770843,0.00004982207,0.0010352724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999051,0.000016776225,0.0000062607096,0.00002313232,0.000038792175,0.000009883796],"domain_scores_gemma":[0.9993538,0.0004074488,0.000052536474,0.000044966713,0.00012291322,0.000018280922],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024891834,0.00040323855,0.00041719124,0.00037593785,0.0002111269,0.00023963704,0.0005081703,0.00040418177,0.0009208078],"category_scores_gemma":[0.0018762033,0.00025622864,0.00032297583,0.00021208849,0.0002368829,0.00044391528,0.0002014107,0.00053876766,0.00018326694],"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":[0.00009068917,0.000054228898,0.001631382,0.00008503802,0.000025100873,0.00013600459,0.000090850706,0.78432953,0.020754833,0.0037044098,0.00064097386,0.18845704],"study_design_scores_gemma":[0.000004525388,0.000027344262,0.0002657083,0.0000030097526,0.0000036520712,0.000019677553,0.0000037207012,0.99556196,0.0031444402,0.0006805906,0.00028214644,0.0000031330549],"about_ca_topic_score_codex":0.0062601496,"about_ca_topic_score_gemma":0.0039741495,"teacher_disagreement_score":0.0062601496,"about_ca_system_score_codex":0.0004327585,"about_ca_system_score_gemma":0.00057884405,"threshold_uncertainty_score":0.012447417},"labels":[],"label_agreement":null},{"id":"W1997967727","doi":"10.1115/gt2013-94059","title":"Effects of Engine Wash Frequency on GT Degradation in Natural Gas Compressor Stations","year":2013,"lang":"en","type":"article","venue":"","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":true,"ca_institutions":"TransCanada (Canada); Nova Chemicals (Canada)","funders":"","keywords":"Gas compressor; Automotive engineering; Combined cycle; Turbine; Natural gas; Gas turbines; Engineering; Noise (video); Computer science; Mechanical engineering","score_opus":0.0035437761875255365,"score_gpt":0.1973121935478382,"score_spread":0.19376841736031264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1997967727","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99926907,0.000044215012,0.0003477596,0.000007907939,0.0000025880256,0.000004436117,0.00006472471,0.000030935927,0.00022837005],"genre_scores_gemma":[0.9995975,0.000019379351,0.00011253865,0.0000035294172,3.8758722e-7,0.000001697901,0.0000680109,0.0000042040524,0.00019283182],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99979335,0.000018087201,0.0000137118495,0.000059177964,0.000075898235,0.000039799146],"domain_scores_gemma":[0.99944776,0.00021131203,0.00007966954,0.00004257381,0.0001836781,0.000034944198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003019926,0.00035558638,0.00038618414,0.000351282,0.0003311056,0.00032940067,0.0002731518,0.00049489,0.0006390565],"category_scores_gemma":[0.0009913888,0.00017571675,0.00036867888,0.0003141042,0.00023964846,0.00027613685,0.00021288206,0.00034187894,0.00022352544],"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":[0.0035261728,0.0006885773,0.2964688,0.00035629555,0.00011464945,0.0014947838,0.0008886314,0.26861557,0.36267853,0.00015765727,0.0007308804,0.06427938],"study_design_scores_gemma":[0.0000281424,0.0039861687,0.4277431,0.000031026746,0.00007898898,0.00031331152,0.0011073548,0.24540097,0.32028824,0.00009693136,0.00086999236,0.000055740773],"about_ca_topic_score_codex":0.022130432,"about_ca_topic_score_gemma":0.024240121,"teacher_disagreement_score":0.022130432,"about_ca_system_score_codex":0.00065736944,"about_ca_system_score_gemma":0.00020853705,"threshold_uncertainty_score":0.04400325},"labels":[],"label_agreement":null},{"id":"W2014546172","doi":"10.1115/gt2014-25753","title":"An Efficient Component Map Generation Method for Prediction of Gas Turbine Performance","year":2014,"lang":"en","type":"article","venue":"Volume 6: Ceramics; Controls, Diagnostics and Instrumentation; Education; Manufacturing Materials and Metallurgy","topic":"Technical Engine Diagnostics and Monitoring","field":"Engineering","cited_by":28,"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":"Gas compressor; Operability; Computer science; Turbine; High fidelity; Performance prediction; Gas turbines; Range (aeronautics); Component (thermodynamics); Reliability engineering; Automotive engineering; Engineering; Simulation; Mechanical engineering; Aerospace engineering","score_opus":0.007625077735318321,"score_gpt":0.2270729461358133,"score_spread":0.219447868400495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014546172","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005947731,0.000064108965,0.9928294,0.000018803348,0.00000959516,0.000021691247,0.000036954963,0.0006577291,0.00041399378],"genre_scores_gemma":[0.32217875,0.00018590056,0.6745129,0.000033004686,0.00002046706,0.00017226966,0.00040473623,0.0002178193,0.002274056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998554,0.000030773033,0.0000064271785,0.00003238405,0.00006238398,0.000012699495],"domain_scores_gemma":[0.9997222,0.00012197163,0.00002400429,0.000022280066,0.0001006781,0.0000089193945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035575152,0.00069304643,0.0004069655,0.0005806288,0.00029126534,0.0004208347,0.0006161046,0.00053018145,0.0015776542],"category_scores_gemma":[0.00121926,0.00028039055,0.00040281628,0.0005322179,0.00024083728,0.00041974912,0.00036556373,0.00066086237,0.00044365303],"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":[0.000111193374,0.00004158521,0.00088565686,0.00011449174,0.00003043408,0.00008596948,0.00008816704,0.71160644,0.016267773,0.0034841704,0.001971019,0.26531312],"study_design_scores_gemma":[0.00000170922,0.000006513762,0.00010583761,0.0000018259701,0.0000021092767,0.00000866347,0.0000028996196,0.99743587,0.0017313819,0.00024039586,0.0004597314,0.0000032237035],"about_ca_topic_score_codex":0.0052154474,"about_ca_topic_score_gemma":0.004271393,"teacher_disagreement_score":0.0052154474,"about_ca_system_score_codex":0.00031675564,"about_ca_system_score_gemma":0.00071564736,"threshold_uncertainty_score":0.010370135},"labels":[],"label_agreement":null},{"id":"W2049067241","doi":"10.1115/gt2006-90037","title":"Prediction of Gas Turbine Blade Life: An Interdisciplinary Engineering Approach for Condition-Based Maintenance","year":2006,"lang":"en","type":"article","venue":"Volume 5: Marine; Microturbines and Small Turbomachinery; Oil and Gas Applications; Structures and Dynamics, Parts A and B","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":"TransCanada (Canada)","funders":"","keywords":"Limiting; Gas turbines; Turbine blade; Engineering; Blade (archaeology); Turbine; Pipeline transport; Reliability engineering; Maintenance engineering; Mechanical engineering; Computer science","score_opus":0.005050969299528639,"score_gpt":0.1980089703625647,"score_spread":0.19295800106303607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049067241","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05561619,0.00019702161,0.94167155,0.0001921495,0.000015102979,0.00005973879,0.000093945404,0.00029403737,0.0018602922],"genre_scores_gemma":[0.8709904,0.0003564571,0.12684773,0.00003990773,0.000048222868,0.00018179727,0.00017599201,0.000051516545,0.0013079926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981374,0.00005983035,0.00000926247,0.000046622106,0.00005920988,0.000011279279],"domain_scores_gemma":[0.99959034,0.00023318917,0.00006102824,0.000030629795,0.00006361657,0.000021196516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046344305,0.00089371635,0.0006279997,0.0006405303,0.00030636953,0.0007267867,0.0006161456,0.0013245866,0.0010574376],"category_scores_gemma":[0.0018690208,0.00036099248,0.0005619172,0.00026971562,0.00038081044,0.00081630936,0.00040960417,0.0005173775,0.00023781616],"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":[0.000017709981,0.000038437727,0.0010798777,0.000020874984,0.000014115064,0.00003960858,0.00002234754,0.97621983,0.0026531757,0.0014218204,0.00014091442,0.018331394],"study_design_scores_gemma":[0.0000012183631,0.00001669959,0.00021885324,0.0000020071593,0.0000024631145,0.000008616831,0.0000037454354,0.99851114,0.00026315264,0.0008716899,0.00009761165,0.0000026982248],"about_ca_topic_score_codex":0.0020685075,"about_ca_topic_score_gemma":0.0012174787,"teacher_disagreement_score":0.0020685075,"about_ca_system_score_codex":0.00038592526,"about_ca_system_score_gemma":0.00043700958,"threshold_uncertainty_score":0.004112959},"labels":[],"label_agreement":null},{"id":"W2081627109","doi":"10.1115/ipc2012-90048","title":"Prediction of Gas Turbine Performance Degradation Between Soakwashes in Natural Gas Compressor Stations","year":2012,"lang":"en","type":"article","venue":"","topic":"Technical Engine Diagnostics and Monitoring","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":true,"ca_institutions":"TransCanada (Canada); Nova Chemicals (Canada)","funders":"NOVA Chemicals","keywords":"Gas compressor; Automotive engineering; Gas turbines; Combined cycle; Natural gas; Turbine; Engine efficiency; Engineering; Computer science; Process engineering; Mechanical engineering; Internal combustion engine; Compression ratio; Waste management","score_opus":0.020993507098967967,"score_gpt":0.22901341601960654,"score_spread":0.20801990892063857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2081627109","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99744725,0.000023853769,0.0019783243,0.000019742569,0.0000039032566,0.000013003573,0.00012650742,0.00008387491,0.00030356646],"genre_scores_gemma":[0.9991955,0.000012229927,0.0004531137,0.00000251415,5.4451135e-7,0.0000043025516,0.00011226094,0.0000041759545,0.00021529313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99989414,0.000012440719,0.0000057655984,0.000037103244,0.000026077922,0.000024427067],"domain_scores_gemma":[0.99966073,0.00015871148,0.000045528057,0.000018016015,0.0000775874,0.0000393914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031285585,0.000731927,0.00039837387,0.00030621505,0.0003595556,0.00045798754,0.0003830173,0.0009043963,0.00042710814],"category_scores_gemma":[0.00077244034,0.0002752193,0.00046329133,0.0001994954,0.00026241364,0.00031427943,0.00019495621,0.00047970377,0.00016811586],"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":[0.0002962714,0.00017930364,0.0404289,0.000045384848,0.000026468251,0.00023959245,0.00008166141,0.93661636,0.0147532085,0.000088300665,0.00024218237,0.007002242],"study_design_scores_gemma":[0.0000067951933,0.00015814923,0.023042673,0.0000028323473,0.0000070888514,0.000017506933,0.000040565952,0.97147447,0.005164106,0.0000283051,0.000050122086,0.0000074813674],"about_ca_topic_score_codex":0.063142836,"about_ca_topic_score_gemma":0.034404147,"teacher_disagreement_score":0.063142836,"about_ca_system_score_codex":0.0012618713,"about_ca_system_score_gemma":0.00048927637,"threshold_uncertainty_score":0.12555063},"labels":[],"label_agreement":null},{"id":"W2166589174","doi":"10.1109/ijcnn.2014.6889694","title":"Dynamie neural networks for jet engine degradation prediction and prognosis","year":2014,"lang":"en","type":"article","venue":"","topic":"Technical Engine Diagnostics and Monitoring","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":"Concordia University","funders":"","keywords":"Nonlinear autoregressive exogenous model; Artificial neural network; Jet engine; Autoregressive model; Computer science; Turbine; Fault (geology); Artificial intelligence; Machine learning; Engineering; Aerospace engineering","score_opus":0.006487268094681454,"score_gpt":0.19918442156169633,"score_spread":0.19269715346701488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166589174","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11718302,0.002787079,0.8746552,0.00050601846,0.00008855679,0.00004450279,0.00011072365,0.00052388693,0.004100974],"genre_scores_gemma":[0.9678187,0.00056874094,0.028772723,0.000079087375,0.000027840228,0.000056576097,0.00009222034,0.000010658148,0.0025735556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984014,0.00005093856,0.000012523179,0.000033256096,0.00004159725,0.000021480477],"domain_scores_gemma":[0.99960774,0.00022201047,0.000046676643,0.000014202268,0.000100486825,0.000008923001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007180681,0.00040760302,0.00037496377,0.0003293856,0.00020804351,0.00049298856,0.00044621283,0.0005764142,0.00059691624],"category_scores_gemma":[0.0017732431,0.00020711032,0.00017568878,0.00028308848,0.00023032262,0.00047493193,0.00028578728,0.00052639126,0.00013711007],"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":[0.00006382372,0.00003129897,0.001000441,0.000030714764,0.000024492476,0.000030795014,0.000016371227,0.9430872,0.001176567,0.0014757984,0.0002344396,0.052828066],"study_design_scores_gemma":[0.0000011660726,0.0000074033233,0.00012721679,0.0000017829778,0.0000021277715,0.0000020919842,0.0000017309583,0.99935645,0.00019076787,0.00025932404,0.00004865645,0.0000013162283],"about_ca_topic_score_codex":0.007388906,"about_ca_topic_score_gemma":0.0052740066,"teacher_disagreement_score":0.007388906,"about_ca_system_score_codex":0.00056147395,"about_ca_system_score_gemma":0.0004825909,"threshold_uncertainty_score":0.01469183},"labels":[],"label_agreement":null},{"id":"W2475195361","doi":"10.1061/9780784479926.045","title":"Effective Radius at the Tangent Point and Its Uses","year":2016,"lang":"en","type":"article","venue":"","topic":"Technical Engine Diagnostics and Monitoring","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":"SNC-Lavalin (Canada)","funders":"","keywords":"Tangent; RADIUS; Track (disk drive); Tangent vector; Point (geometry); Acceleration; Effective radius; Mathematics; Mathematical analysis; Geometry; Computer science; Physics; Classical mechanics; Quantum mechanics","score_opus":0.0049273537791339524,"score_gpt":0.19686227791493932,"score_spread":0.19193492413580537,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2475195361","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.070052154,0.0012963611,0.92172104,0.000059905833,0.000053163796,0.00002379946,0.00012878461,0.0006868562,0.00597792],"genre_scores_gemma":[0.9115178,0.0005951558,0.085865326,0.000020546724,0.000044206594,0.00003344873,0.00017097445,0.00018090096,0.0015716348],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9985072,0.00031334363,0.00010816882,0.0004083368,0.00053136697,0.00013158779],"domain_scores_gemma":[0.9961138,0.0014449185,0.0005504483,0.0007541657,0.00091401726,0.00022260121],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013301617,0.0007753876,0.00069286546,0.0031339463,0.00042254815,0.001600546,0.0016484202,0.000816863,0.0018525004],"category_scores_gemma":[0.007858993,0.00032604107,0.0006852533,0.0008871695,0.0015152661,0.0024797122,0.0010963159,0.0006640086,0.00096262834],"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":[0.00040002234,0.00007058246,0.018115634,0.00064003246,0.00010164261,0.00066513784,0.0005894177,0.5506318,0.04394168,0.22753225,0.0014615497,0.15585022],"study_design_scores_gemma":[0.00001495997,0.00030384012,0.009097879,0.00012764453,0.00007070995,0.0011095047,0.0001746247,0.8806378,0.039880373,0.058832463,0.009546236,0.00020384471],"about_ca_topic_score_codex":0.001532709,"about_ca_topic_score_gemma":0.00042791036,"teacher_disagreement_score":0.0031339463,"about_ca_system_score_codex":0.00057042006,"about_ca_system_score_gemma":0.00042531316,"threshold_uncertainty_score":0.0070346594},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34391493,0.001751587,0.626009,0.0006657433,0.00013293543,0.0005153818,0.008061459,0.015120047,0.003828979],"genre_scores_gemma":[0.87656367,0.00039479963,0.11774786,0.00005927704,0.000037165126,0.0002620751,0.004292037,0.00011302303,0.00053015],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991142,0.00020793795,0.00010665466,0.00019503137,0.00033254354,0.000043686625],"domain_scores_gemma":[0.99837095,0.00062263536,0.00022557391,0.00028313688,0.0004534402,0.0000443483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010967734,0.0011774348,0.00075297203,0.0021144554,0.00028722984,0.0011328906,0.00061415817,0.00055165676,0.0007241471],"category_scores_gemma":[0.0040430226,0.00024382424,0.00041390202,0.0017640381,0.00023873532,0.0011566563,0.00054663717,0.0005651766,0.00042160242],"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":[0.0013662535,0.00080980576,0.041119233,0.0009775754,0.0002509106,0.00059032394,0.00048336253,0.1189327,0.1344723,0.004911643,0.0062473402,0.6898386],"study_design_scores_gemma":[0.000032622156,0.0003779618,0.025405714,0.000055881646,0.00005693671,0.00032158277,0.00019782255,0.8806296,0.08425293,0.003699313,0.0049106535,0.00005891789],"about_ca_topic_score_codex":0.0011601642,"about_ca_topic_score_gemma":0.0008950215,"teacher_disagreement_score":0.0021144554,"about_ca_system_score_codex":0.00049788173,"about_ca_system_score_gemma":0.00051262544,"threshold_uncertainty_score":0.0058003664},"labels":[],"label_agreement":null},{"id":"W2902784847","doi":"10.2478/ntpe-2018-0061","title":"Operational Characteristic of Selected Marine Turbounits Powered by Steam from Auxiliary Oil-Fired Boilers","year":2018,"lang":"en","type":"article","venue":"New Trends in Production Engineering","topic":"Technical Engine Diagnostics and Monitoring","field":"Engineering","cited_by":16,"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":"Independent Electricity System Operator; Uniwersytet Szczeciński","keywords":"Steam turbine; Turbine; Environmental science; Marine engineering; Engineering; Auxiliary power unit; Petroleum engineering; Power consumption; Waste management; Automotive engineering; Process engineering; Power (physics); Mechanical engineering; Electrical engineering","score_opus":0.006241754502356602,"score_gpt":0.20644650526140748,"score_spread":0.2002047507590509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902784847","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99840933,0.00003168488,0.001098326,0.0000026869957,0.0000019050273,0.0000031570519,0.00008775089,0.000035900797,0.0003293784],"genre_scores_gemma":[0.99968576,0.000011245818,0.000093693845,7.9149004e-7,6.8909486e-7,0.0000016434735,0.000059903872,0.000005172438,0.00014111411],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99987686,0.000021023054,0.000009821624,0.00002041925,0.000050034967,0.000021841552],"domain_scores_gemma":[0.99953055,0.00021379127,0.000063658044,0.000029701216,0.000101920545,0.000060448845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012256864,0.0003074334,0.0002834166,0.0007811182,0.00013396295,0.00018780744,0.00016531699,0.00014004197,0.0010803167],"category_scores_gemma":[0.00052032183,0.000081850216,0.0001549961,0.0003479465,0.00019684389,0.00017169068,0.00016069821,0.00014451485,0.00022301031],"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":[0.0029655243,0.00015510186,0.12179041,0.00026428115,0.00013975492,0.0010205517,0.00069631595,0.020584323,0.798164,0.00035846044,0.0005321112,0.05332918],"study_design_scores_gemma":[0.000039147217,0.0024133453,0.4729407,0.000020119325,0.0001127948,0.0009644055,0.00053519406,0.052051798,0.46930724,0.00023153504,0.0013312275,0.000052546977],"about_ca_topic_score_codex":0.00048116056,"about_ca_topic_score_gemma":0.0004656101,"teacher_disagreement_score":0.0010803167,"about_ca_system_score_codex":0.00012622566,"about_ca_system_score_gemma":0.000073613446,"threshold_uncertainty_score":0.0036139488},"labels":[],"label_agreement":null},{"id":"W2919473679","doi":"","title":"Calculation of LCC and LCP of a rail vehicle with the software support","year":2015,"lang":"en","type":"article","venue":"Logistyka","topic":"Technical Engine Diagnostics and Monitoring","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":"Transport Canada","funders":"","keywords":"Software; Computer science; Transport engineering; Engineering; Operating system","score_opus":0.016594963253283523,"score_gpt":0.21702183029583383,"score_spread":0.2004268670425503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919473679","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39547867,0.00034262193,0.55627865,0.00012197236,0.00013122041,0.00009066118,0.002646628,0.015464724,0.029444927],"genre_scores_gemma":[0.9375443,0.000081391256,0.056694143,0.0000110777055,0.000015287118,0.00005859564,0.0012084978,0.00059925584,0.003787479],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998542,0.000014631152,0.000005803924,0.00003363864,0.00007413572,0.000017508091],"domain_scores_gemma":[0.9997004,0.00008924599,0.000019196164,0.000039599796,0.0001377245,0.000013774519],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015819063,0.00029088763,0.00020446129,0.0014168444,0.00027985402,0.00040311652,0.00039650997,0.00034755564,0.0075389626],"category_scores_gemma":[0.0010033031,0.00012178075,0.00031509277,0.0008080112,0.00012366715,0.0003201673,0.00024315774,0.00021046163,0.001286717],"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":[0.000522833,0.00011154191,0.023835018,0.0006794562,0.000088674045,0.0004325597,0.00038225244,0.42104024,0.07474659,0.012649716,0.013592416,0.4519187],"study_design_scores_gemma":[0.000023526127,0.00009529936,0.016223911,0.000027439304,0.000033845423,0.00017308714,0.00007546813,0.93441147,0.036186777,0.0022531126,0.01045208,0.00004392858],"about_ca_topic_score_codex":0.007823299,"about_ca_topic_score_gemma":0.00414483,"teacher_disagreement_score":0.007823299,"about_ca_system_score_codex":0.00029744557,"about_ca_system_score_gemma":0.0005020325,"threshold_uncertainty_score":0.025220394},"labels":[],"label_agreement":null},{"id":"W2928000850","doi":"10.1088/1742-6596/1172/1/012074","title":"Biofuels as an alternative fuel for West Pomeranian fishing fleet","year":2019,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Technical Engine Diagnostics and Monitoring","field":"Engineering","cited_by":8,"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":"Independent Electricity System Operator","keywords":"Alternative fuels; Biofuel; Fishing; Diesel fuel; Renewable fuels; Environmental science; Waste management; Engineering; Business; Fishery; Biology","score_opus":0.017234929165622477,"score_gpt":0.2563424692348299,"score_spread":0.23910754006920742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2928000850","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98414326,0.003015757,0.0023658695,0.00028667154,0.00008202428,0.00002290431,0.00018868761,0.000009473545,0.009885444],"genre_scores_gemma":[0.9838472,0.004080197,0.0039947093,0.000050547213,0.000011795804,0.0000100640955,0.000118784206,0.000005065277,0.007881555],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99992776,0.000007756502,0.000002771073,0.000013773933,0.00003357336,0.0000143139805],"domain_scores_gemma":[0.9999745,0.0000033767867,0.000007330449,0.0000012252432,0.000009789337,0.000003895675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012061847,0.00023544295,0.00010425398,0.0005339386,0.0003564556,0.000561494,0.0001761007,0.00019615552,0.0019239737],"category_scores_gemma":[0.000085067884,0.000054552118,0.00017980344,0.0004558331,0.0001266922,0.000438399,0.00026782553,0.00020290299,0.00020384305],"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":[0.0013560613,0.00034600415,0.07115733,0.001078434,0.00010347705,0.0036020607,0.0006948906,0.00852702,0.6001658,0.0076594795,0.0011556052,0.30415392],"study_design_scores_gemma":[0.00006388571,0.0030338347,0.19775382,0.000763879,0.00031603602,0.0020257367,0.00987712,0.012797617,0.617213,0.004239252,0.15183365,0.00008227092],"about_ca_topic_score_codex":0.0027290764,"about_ca_topic_score_gemma":0.009851484,"teacher_disagreement_score":0.0027290764,"about_ca_system_score_codex":0.00045259637,"about_ca_system_score_gemma":0.00032677423,"threshold_uncertainty_score":0.006436348},"labels":[],"label_agreement":null},{"id":"W2938452214","doi":"10.3390/app9081540","title":"Selection of Diagnostic Symptoms and Injection Subsystems of Marine Reciprocating Internal Combustion Engines","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Technical Engine Diagnostics and Monitoring","field":"Engineering","cited_by":14,"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":"Independent Electricity System Operator","keywords":"Reciprocating motion; Reliability (semiconductor); Selection (genetic algorithm); Scope (computer science); Combustion; Computer science; Diagnostic test; Internal combustion engine; Residual; Engineering; Automotive engineering; Artificial intelligence; Chemistry; Physics; Algorithm","score_opus":0.005541164497149192,"score_gpt":0.20858267542519135,"score_spread":0.20304151092804215,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938452214","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9538821,0.00017069613,0.044323124,0.000020356516,0.000011079959,0.000088539935,0.00011284735,0.00017087496,0.0012205071],"genre_scores_gemma":[0.9807341,0.00006334118,0.018534489,0.000007506447,0.000005349707,0.0000517456,0.00018787927,0.000022407374,0.0003931848],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9995382,0.00012390352,0.000027596481,0.00009571947,0.00015218447,0.00006236983],"domain_scores_gemma":[0.99891305,0.0004963884,0.000117099466,0.0000987213,0.0002815628,0.00009333597],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007139981,0.0005361941,0.00045797642,0.0014627067,0.00026725518,0.00037583054,0.00036860947,0.00040139304,0.001006122],"category_scores_gemma":[0.0024744938,0.00015670226,0.00034735823,0.00039186017,0.00031328594,0.0003026668,0.00037346868,0.00017809104,0.00020387281],"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":[0.002973967,0.0004212015,0.060831435,0.0004906493,0.00007387154,0.00056766625,0.00074438466,0.014070323,0.7711917,0.0023602445,0.00037700107,0.1458975],"study_design_scores_gemma":[0.00016604482,0.0059191585,0.1977668,0.00003948125,0.0003134959,0.0011368409,0.00083765615,0.08125033,0.70767534,0.0015811526,0.0032136405,0.00010002239],"about_ca_topic_score_codex":0.00039770512,"about_ca_topic_score_gemma":0.00040807194,"teacher_disagreement_score":0.0014627067,"about_ca_system_score_codex":0.00023151858,"about_ca_system_score_gemma":0.0002394201,"threshold_uncertainty_score":0.0037760735},"labels":[],"label_agreement":null},{"id":"W2959510169","doi":"10.3390/jmse7070213","title":"Assessing the Unreliability of Systems during the Early Operation Period of a Ship—A Case Study","year":2019,"lang":"en","type":"article","venue":"Journal of Marine Science and Engineering","topic":"Technical Engine Diagnostics and Monitoring","field":"Engineering","cited_by":16,"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":"Independent Electricity System Operator; Ministerstwo Edukacji i Nauki","keywords":"Crew; Failure mode and effects analysis; Reliability engineering; Component (thermodynamics); Variety (cybernetics); Hydrometeorology; Computer science; Service (business); Marine engineering; Forensic engineering; Engineering; Aeronautics; Meteorology","score_opus":0.008639176556755352,"score_gpt":0.24838060178333013,"score_spread":0.23974142522657477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2959510169","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9977487,0.00007040553,0.0015504196,0.000021715374,0.0000035101828,0.000030298288,0.00011537794,0.000010529629,0.00044896046],"genre_scores_gemma":[0.99826056,0.0000718425,0.001082152,0.000004052493,0.000005994356,0.00001302247,0.00014009047,0.0000044068756,0.00041795085],"study_design_codex":"observational","study_design_gemma":"case_report","domain_scores_codex":[0.9990452,0.00022258302,0.00010920792,0.0001714028,0.00028166448,0.000169912],"domain_scores_gemma":[0.99315006,0.0039087594,0.0010960004,0.00059533265,0.00087787287,0.00037197242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001608769,0.0006046414,0.00048044673,0.0030104963,0.0009591002,0.0006712641,0.00079906755,0.0011106905,0.000924379],"category_scores_gemma":[0.0041100406,0.00031624027,0.00070563384,0.001700126,0.0009252769,0.00084586506,0.0008381356,0.00066137366,0.00017322804],"study_design_candidate":"case_report","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":[0.00113912,0.0011231169,0.6893748,0.00051232835,0.00036244717,0.017225819,0.006232684,0.20600034,0.023217238,0.0018909367,0.0013432511,0.051577866],"study_design_scores_gemma":[0.000027664339,0.0035525241,0.83959997,0.00008865235,0.00019098917,0.005998055,0.008306258,0.12373299,0.014590288,0.0010536753,0.0027267227,0.00013223266],"about_ca_topic_score_codex":0.008052083,"about_ca_topic_score_gemma":0.010295233,"teacher_disagreement_score":0.008052083,"about_ca_system_score_codex":0.0009405212,"about_ca_system_score_gemma":0.00038960943,"threshold_uncertainty_score":0.016010404},"labels":[],"label_agreement":null},{"id":"W2966974655","doi":"10.3390/en12163105","title":"Mathematical Modeling of the Working Conditions of the Ship’s Utilization Boiler in Order to Evaluate Its Performance","year":2019,"lang":"en","type":"article","venue":"Energies","topic":"Technical Engine Diagnostics and Monitoring","field":"Engineering","cited_by":4,"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":"Independent Electricity System Operator; Uniwersytet Szczeciński","keywords":"Boiler (water heating); Exhaust gas; Flash boiler; Waste management; Environmental science; Engineering; Nuclear engineering; Process engineering; Petroleum engineering; Steam drum; Superheated steam","score_opus":0.03334458559736807,"score_gpt":0.261971245698542,"score_spread":0.22862666010117394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966974655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047005378,0.001055258,0.91771734,0.00036465522,0.00018138008,0.00014929654,0.0005915304,0.0004833874,0.032451842],"genre_scores_gemma":[0.9283492,0.0018301111,0.042811844,0.00010291411,0.000085527514,0.00063557894,0.0005842098,0.00018328973,0.02541728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996532,0.00008467278,0.000021270165,0.0000758627,0.00012167534,0.000043346463],"domain_scores_gemma":[0.99967086,0.00014729211,0.000046778077,0.000034110504,0.000089639645,0.000011259355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000522669,0.0012231393,0.0006601832,0.0007114321,0.0004770383,0.0012520434,0.0011216267,0.0014655992,0.0035419313],"category_scores_gemma":[0.0012794909,0.00039213803,0.0011591861,0.000491911,0.0006516212,0.0010711816,0.00061793637,0.0010404701,0.0012990995],"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":[0.000038423426,0.000035277986,0.0007415225,0.00018750735,0.000023148976,0.00015406116,0.00010204364,0.96668476,0.010581574,0.013965075,0.0004964088,0.0069902064],"study_design_scores_gemma":[0.000005415247,0.000034278037,0.00037916956,0.0000116383935,0.000013505228,0.00005697437,0.000021345899,0.99345,0.0023610017,0.0018351895,0.0018210651,0.000010427575],"about_ca_topic_score_codex":0.004094384,"about_ca_topic_score_gemma":0.001741951,"teacher_disagreement_score":0.004094384,"about_ca_system_score_codex":0.00066886237,"about_ca_system_score_gemma":0.00093526015,"threshold_uncertainty_score":0.011848867},"labels":[],"label_agreement":null},{"id":"W2970530649","doi":"10.1109/icphm.2019.8819433","title":"Data-Driven Model Selection Study for Long-Term Performance Deterioration of Gas Turbines","year":2019,"lang":"en","type":"article","venue":"","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":"Life Prediction Technologies (Canada)","funders":"","keywords":"Term (time); Gas turbines; Computer science; Reliability engineering; Selection (genetic algorithm); Power (physics); Degradation (telecommunications); Performance enhancement; Data modeling; Automotive engineering; Engineering; Machine learning; Mechanical engineering","score_opus":0.029482289950693862,"score_gpt":0.27478876568928295,"score_spread":0.2453064757385891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2970530649","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.824629,0.0007950472,0.17122485,0.00061881595,0.000054664757,0.00024538892,0.000711296,0.00050472026,0.0012161849],"genre_scores_gemma":[0.98697877,0.0001329842,0.011088875,0.000054494907,0.000016332779,0.00018171864,0.00089710904,0.000028450067,0.000621217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99846077,0.0011278732,0.00006521907,0.00013895077,0.00010127613,0.00010584819],"domain_scores_gemma":[0.98557705,0.012587388,0.0004952624,0.00021598581,0.0009854984,0.00013887453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0065731886,0.0015997263,0.0012335406,0.0009566639,0.0006061335,0.0010906185,0.001184782,0.0011004935,0.0014425535],"category_scores_gemma":[0.009844444,0.0006252561,0.0018328156,0.00045925032,0.00042071036,0.0005882972,0.0007948739,0.0016189419,0.0002019913],"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":[0.0003158173,0.00024809904,0.008037213,0.00012946773,0.00030776736,0.00015565268,0.00007985409,0.9811288,0.0009957167,0.0005752319,0.00028441387,0.0077420785],"study_design_scores_gemma":[0.000014815808,0.00011860873,0.0011864703,0.000005912016,0.000042584747,0.000010520228,0.000028846698,0.9979832,0.00037535545,0.0001525622,0.000074258496,0.0000067676056],"about_ca_topic_score_codex":0.01135099,"about_ca_topic_score_gemma":0.0068629137,"teacher_disagreement_score":0.01135099,"about_ca_system_score_codex":0.0008155711,"about_ca_system_score_gemma":0.001307868,"threshold_uncertainty_score":0.03476274},"labels":[],"label_agreement":null},{"id":"W2983847724","doi":"10.18372/2310-5461.42.13760","title":"GAS TURBINE PLANT ON THE BASIS OF THE CONVERTED AVIATION ENGINE WITH HEAT REGENERATION","year":2019,"lang":"en","type":"article","venue":"Science-based technologies","topic":"Technical Engine Diagnostics and Monitoring","field":"Engineering","cited_by":3,"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":"Combined cycle; Heat engine; Automotive engineering; Gas turbines; Thermodynamic cycle; Thermal efficiency; Engineering; Engine efficiency; Turbine; Aviation; Work (physics); Internal combustion engine; Environmental science; Mechanical engineering; Aerospace engineering; Compression ratio; Combustion; Chemistry","score_opus":0.00713960557757069,"score_gpt":0.17771709284518003,"score_spread":0.17057748726760935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2983847724","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.983536,0.00028223486,0.007364587,0.000028457476,0.00003327244,0.00006977001,0.00017458037,0.00018347254,0.008327576],"genre_scores_gemma":[0.9950681,0.00009773358,0.00254262,0.00000414871,0.0000016515451,0.000008706041,0.000116095594,0.000009140487,0.0021519617],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999577,0.000005844758,0.0000015626645,0.0000071515583,0.000019969817,0.0000077654095],"domain_scores_gemma":[0.99997556,0.000005264444,0.0000025635961,0.0000066434727,0.0000063877287,0.00000341495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008199709,0.00021144902,0.00030831338,0.00025009183,0.00025305865,0.00030519147,0.00033787053,0.00023106718,0.002156603],"category_scores_gemma":[0.000106180814,0.00010975091,0.00048771832,0.00023703901,0.00019070313,0.00018279992,0.00010415501,0.00022037911,0.0004084152],"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":[0.0014927593,0.00041021084,0.015229024,0.0011025292,0.00018406434,0.0027790964,0.00018481506,0.33189625,0.57202345,0.00839704,0.0012432446,0.06505752],"study_design_scores_gemma":[0.0003351844,0.0038677426,0.05298284,0.00007061267,0.00032923988,0.0022682804,0.00023836653,0.50862056,0.40562755,0.0018843904,0.023687974,0.00008723526],"about_ca_topic_score_codex":0.0058961725,"about_ca_topic_score_gemma":0.007959773,"teacher_disagreement_score":0.0058961725,"about_ca_system_score_codex":0.00025133652,"about_ca_system_score_gemma":0.00038351162,"threshold_uncertainty_score":0.011723697},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9975012,0.00027862872,0.0010857298,0.00002075489,0.000027771865,0.000004497155,0.00013684478,0.000080128215,0.0008644272],"genre_scores_gemma":[0.9994729,0.00006010563,0.000118430944,0.0000050086574,0.000003957642,0.0000018758226,0.00011626651,0.000008164973,0.00021331114],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9996822,0.00004666717,0.000025127052,0.00007557446,0.00009797622,0.000072486684],"domain_scores_gemma":[0.9992943,0.00031971707,0.00008693907,0.00006932028,0.0001807001,0.000048937098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028448133,0.0003711007,0.0003675393,0.00022305048,0.00021096953,0.00043700996,0.00017460808,0.00029995816,0.00085973047],"category_scores_gemma":[0.000959734,0.00012699241,0.00023259256,0.00019395757,0.00017849226,0.00026425725,0.00021881964,0.00023448076,0.00037050826],"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":[0.0081518935,0.00070213963,0.07851006,0.0006272863,0.00024688634,0.0011150917,0.00038111373,0.06430002,0.7831198,0.0002518823,0.0014934032,0.061100412],"study_design_scores_gemma":[0.000034242657,0.00414672,0.28774714,0.000036870802,0.000184635,0.00046994092,0.00035249605,0.06958131,0.6360094,0.00015491746,0.001200576,0.00008173798],"about_ca_topic_score_codex":0.0010268115,"about_ca_topic_score_gemma":0.0010611048,"teacher_disagreement_score":0.0010268115,"about_ca_system_score_codex":0.0001588801,"about_ca_system_score_gemma":0.00012182443,"threshold_uncertainty_score":0.002876103},"labels":[],"label_agreement":null},{"id":"W4394264825","doi":"10.6084/m9.figshare.21435660","title":"Investigation of the sealing performance of the gearbox sealing system of high-speed trains","year":2022,"lang":"en","type":"dataset","venue":"Figshare","topic":"Technical Engine Diagnostics and Monitoring","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 Toronto","funders":"","keywords":"Train; Automotive engineering; Computer science; Engineering; Mechanical engineering; Materials science; Geography; Cartography","score_opus":0.023164886879886555,"score_gpt":0.20458627922031325,"score_spread":0.1814213923404267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394264825","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006493232,0.0002907357,0.00025684707,0.000109169065,0.00007638728,0.000016794262,0.9907251,0.0010014673,0.0010302302],"genre_scores_gemma":[0.006777213,0.00010344048,0.00054075336,0.000036110316,0.000014219249,0.000034048648,0.9915724,0.0000499967,0.0008717485],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99912995,0.00009253209,0.00008535657,0.00028027326,0.00028068092,0.00013109436],"domain_scores_gemma":[0.9985563,0.00036720725,0.0001481898,0.00036727803,0.00045601773,0.0001050171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009327942,0.002065307,0.0010084944,0.0023479853,0.0004683084,0.0011532761,0.0016823651,0.0021891003,0.008381562],"category_scores_gemma":[0.002991025,0.0003792345,0.0019760947,0.0021311755,0.00036599333,0.0009106814,0.0010528443,0.0010421765,0.015211512],"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":[0.00070820766,0.0003274068,0.028648432,0.0030164719,0.0004593201,0.00022474844,0.00012046427,0.010838099,0.0024744233,0.0008282685,0.9287796,0.023574527],"study_design_scores_gemma":[0.00075578754,0.00032192116,0.21199356,0.0007926403,0.0004305861,0.00043661377,0.00047543933,0.018002125,0.006249523,0.0029969502,0.7573053,0.00023954904],"about_ca_topic_score_codex":0.026144892,"about_ca_topic_score_gemma":0.05257742,"teacher_disagreement_score":0.026144892,"about_ca_system_score_codex":0.0008770892,"about_ca_system_score_gemma":0.0010808221,"threshold_uncertainty_score":0.051985443},"labels":[],"label_agreement":null},{"id":"W95861574","doi":"10.20381/ruor-4897","title":"Modeling Behaviour of Damaged Turbine Blades for Engine Health Diagnostics and Prognostics","year":2011,"lang":"en","type":"dissertation","venue":"Library and Archives Canada (Government of Canada)","topic":"Technical Engine Diagnostics and Monitoring","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Prognostics; Turbine; Turbine blade; Engineering; Mechanical engineering; Aerospace engineering; Computer science; Reliability engineering","score_opus":0.005238633505928402,"score_gpt":0.16571311122255844,"score_spread":0.16047447771663004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W95861574","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.38953522,0.00060214323,0.5913647,0.0005083489,0.00009936405,0.00017498541,0.0006770888,0.00093874213,0.016099377],"genre_scores_gemma":[0.9653764,0.0003953673,0.024582231,0.000036808327,0.000013316786,0.000060551396,0.00028378153,0.00006519018,0.00918641],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999311,0.000015188688,0.000004075586,0.000012895415,0.000024507375,0.000012180246],"domain_scores_gemma":[0.9998029,0.00007127749,0.000026205274,0.000027785953,0.000057030094,0.0000148272175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023624615,0.0004361772,0.00044496937,0.00030578184,0.00029429168,0.00052266376,0.00060061365,0.0009095359,0.0029728776],"category_scores_gemma":[0.000897147,0.00028652174,0.00046661537,0.00028192034,0.0002786833,0.00043584302,0.00023909935,0.0004452579,0.00080696784],"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":[0.000026640684,0.000031803396,0.0019077872,0.000031580617,0.0000059246563,0.000057386867,0.000060261315,0.981763,0.008265636,0.0015856541,0.00034088694,0.0059234025],"study_design_scores_gemma":[0.0000010196933,0.000009346649,0.00029021502,0.0000020144075,0.0000016575455,0.0000079121355,0.000008561637,0.9987281,0.00044441252,0.00027607835,0.00022872449,0.0000018761594],"about_ca_topic_score_codex":0.019578755,"about_ca_topic_score_gemma":0.016188724,"teacher_disagreement_score":0.019578755,"about_ca_system_score_codex":0.0007090059,"about_ca_system_score_gemma":0.0008221822,"threshold_uncertainty_score":0.03892958},"labels":[],"label_agreement":null}]}