{"meta":{"query_hash":"0b36f1c57c7b","filters":{"venue":"2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC)"},"cohort_total":5,"direct_labels_cover":0,"predictions_cover":5,"exported":5,"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/0b36f1c57c7b","api":"https://metacan.xera.ac/api/v1/cohort?venue=2018+International+Conference+on+Sensing%2CDiagnostics%2C+Prognostics%2C+and+Control+%28SDPC%29"},"results":[{"id":"W2921812469","doi":"10.1109/sdpc.2018.8664983","title":"Fault-Tolerant Control for a Quadrotor Helicopter with Parametric Uncertainty","year":2018,"lang":"en","type":"article","venue":"2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC)","topic":"Adaptive Control of Nonlinear 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":"Concordia University","funders":"","keywords":"Parametric statistics; Control theory (sociology); Actuator; Integral sliding mode; Sliding mode control; Fault (geology); Fault tolerance; Engineering; Robust control; Control engineering; Computer science; Control (management); Control system; Nonlinear system; Artificial intelligence; Mathematics; Reliability engineering","score_opus":0.025416671549287652,"score_gpt":0.25378978939366714,"score_spread":0.2283731178443795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921812469","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":[0.09207341,0.0016748491,0.8768542,0.0033944878,0.0076363184,0.0069477865,0.0025783416,0.000994685,0.00784595],"genre_scores_gemma":[0.9924773,0.00029635802,0.0028837658,0.0011357283,0.0025827228,0.00015880444,0.0000863043,0.00012008123,0.00025896457],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99691784,0.000104434184,0.0007984638,0.0007469934,0.0006608477,0.0007714097],"domain_scores_gemma":[0.9945144,0.0022094303,0.00030870808,0.00043965993,0.0021861712,0.00034162632],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043536988,0.0006681347,0.0008497957,0.00038043218,0.00021891591,0.0003797699,0.0003912226,0.000273541,0.00007704644],"category_scores_gemma":[0.0023414828,0.00057834876,0.00015803995,0.00014771802,0.00053803204,0.00020262161,0.00003873917,0.0003755072,0.00017220229],"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":[0.038571652,0.0043482925,0.10070245,0.001947936,0.02402148,0.0015875289,0.006532981,0.041432932,0.012731415,0.24174008,0.12265162,0.40373164],"study_design_scores_gemma":[0.010870133,0.0024900993,0.003456633,0.00058913516,0.00032080212,0.00006369224,0.00026403635,0.9473518,0.00033796031,0.00086999097,0.032370225,0.0010154917],"about_ca_topic_score_codex":0.00015444648,"about_ca_topic_score_gemma":0.00040606744,"teacher_disagreement_score":0.9059189,"about_ca_system_score_codex":0.00019900315,"about_ca_system_score_gemma":0.00014445925,"threshold_uncertainty_score":0.9996668},"labels":[],"label_agreement":null},{"id":"W2922007470","doi":"10.1109/sdpc.2018.8664949","title":"Cuckoo Search Optimized NN-Based Fault Diagnosis Approach for Power Transformer PHM","year":2018,"lang":"en","type":"article","venue":"2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC)","topic":"Power Transformer Diagnostics and Insulation","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":"National Research Council Canada","funders":"","keywords":"Cuckoo search; Particle swarm optimization; Artificial neural network; Transformer; Backpropagation; Computer science; Dissolved gas analysis; Reliability engineering; Fault (geology); Engineering; Genetic algorithm; Cuckoo; Machine learning; Data mining; Voltage","score_opus":0.028368375227344204,"score_gpt":0.25913304866103815,"score_spread":0.23076467343369395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922007470","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":[0.026344817,0.0006908412,0.93173397,0.0023301258,0.0046683145,0.003203972,0.002023009,0.00055727863,0.028447688],"genre_scores_gemma":[0.9806362,0.0024611815,0.013898778,0.0011783198,0.00086186273,0.00020431579,0.0005275048,0.00013310973,0.00009877609],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9966904,0.00007620098,0.0008345456,0.0007835358,0.00073045597,0.00088485517],"domain_scores_gemma":[0.9959307,0.001680925,0.00013662109,0.00039172755,0.0014826717,0.00037740514],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048750604,0.0006681021,0.00066461944,0.00040400747,0.00027761504,0.00043148772,0.00041016634,0.00037037727,0.00030915678],"category_scores_gemma":[0.0012002591,0.0006659913,0.00023802042,0.00016181078,0.00053049566,0.00028884847,0.000022225744,0.00044181495,0.00008218592],"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":[0.012075262,0.009039845,0.04690758,0.0024482296,0.009922272,0.0002643371,0.0138460845,0.079522625,0.0134154055,0.22821672,0.15995006,0.42439157],"study_design_scores_gemma":[0.008060132,0.0014154228,0.0030747897,0.00032268013,0.00030952808,0.000012305651,0.00025345472,0.9533439,0.009008534,0.0013010872,0.021737073,0.0011610828],"about_ca_topic_score_codex":0.00007879039,"about_ca_topic_score_gemma":0.000045024077,"teacher_disagreement_score":0.95429134,"about_ca_system_score_codex":0.00016078423,"about_ca_system_score_gemma":0.00015658236,"threshold_uncertainty_score":0.99957913},"labels":[],"label_agreement":null},{"id":"W2922024423","doi":"10.1109/sdpc.2018.8664864","title":"An Hilbert-Huang Spectrum Technique for Fault Detection in Rolling Element Bearings","year":2018,"lang":"en","type":"article","venue":"2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC)","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":"Lakehead University","funders":"","keywords":"Hilbert–Huang transform; Rolling-element bearing; Bearing (navigation); Fault (geology); Feature extraction; Fault detection and isolation; Computer science; SIGNAL (programming language); Feature (linguistics); Vibration; Field (mathematics); Pattern recognition (psychology); Artificial intelligence; Control theory (sociology); Acoustics; Computer vision; Mathematics; Actuator; Physics","score_opus":0.015377863974229692,"score_gpt":0.2827571348775311,"score_spread":0.26737927090330144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922024423","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":[0.15702617,0.00030904828,0.826235,0.0017167556,0.003056298,0.0044754767,0.00037341766,0.001243889,0.005563962],"genre_scores_gemma":[0.9891346,0.0008796228,0.007920925,0.0004867965,0.0010936689,0.0002650775,0.00008816211,0.00009686876,0.000034297023],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99752235,0.000071215705,0.00071979436,0.00066680595,0.0004046935,0.00061515253],"domain_scores_gemma":[0.99799615,0.00069258065,0.00019019913,0.00037202195,0.00055402337,0.00019500843],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00058693177,0.00048514036,0.0004726898,0.00049931236,0.00016476476,0.0002969159,0.0003569489,0.00028606533,0.000058308346],"category_scores_gemma":[0.0013539301,0.0005261655,0.00009399593,0.00012457727,0.00020460917,0.0002790573,0.000056541543,0.00044095327,0.000025718075],"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":[0.0032977092,0.0035808657,0.11030199,0.0010101097,0.0019512018,0.0003646076,0.005173935,0.0057202084,0.261679,0.13627161,0.018926976,0.4517218],"study_design_scores_gemma":[0.0029738063,0.0022651213,0.00735613,0.000746852,0.00012308659,0.000037490285,0.00017889732,0.76457995,0.194852,0.016683701,0.009008057,0.0011949356],"about_ca_topic_score_codex":0.00045640406,"about_ca_topic_score_gemma":0.0021693758,"teacher_disagreement_score":0.83210844,"about_ca_system_score_codex":0.00027249573,"about_ca_system_score_gemma":0.00005029788,"threshold_uncertainty_score":0.99971896},"labels":[],"label_agreement":null},{"id":"W2922292104","doi":"10.1109/sdpc.2018.8665002","title":"Active Fault-Tolerant Tracking Control of a Quadrotor UAV","year":2018,"lang":"en","type":"article","venue":"2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC)","topic":"Fault Detection and Control Systems","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":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Control theory (sociology); Actuator; Controller (irrigation); Kalman filter; Estimator; Fault tolerance; Fault (geology); Control engineering; Engineering; Fault detection and isolation; Computer science; Tracking (education); Linear-quadratic regulator; Control (management); Artificial intelligence; Mathematics","score_opus":0.01996591467655686,"score_gpt":0.24985387002438353,"score_spread":0.22988795534782666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922292104","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":[0.5468401,0.0028200555,0.34205145,0.0043243556,0.029142763,0.007299289,0.0031125806,0.0020817758,0.062327646],"genre_scores_gemma":[0.99699837,0.0004433879,0.00021977957,0.0005747955,0.0014530019,0.00004627828,0.000027014956,0.000073828436,0.00016355125],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99721116,0.00013578037,0.0008970265,0.00055274664,0.0006485902,0.000554727],"domain_scores_gemma":[0.9967011,0.000965838,0.00034854043,0.0003662764,0.0013588738,0.0002593729],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034429823,0.000512128,0.00076527894,0.00031383807,0.00017517139,0.00022763081,0.00032893821,0.0002813342,0.00017590895],"category_scores_gemma":[0.0016165581,0.00050272606,0.00017664928,0.00012010574,0.00046578664,0.00022570984,0.000035163102,0.00041957296,0.00015392587],"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":[0.010407174,0.0021689038,0.0239106,0.0009535693,0.009960401,0.00061173213,0.011656293,0.0043186694,0.1203452,0.11827798,0.022456866,0.6749326],"study_design_scores_gemma":[0.01041897,0.0016188505,0.0076676435,0.0009832256,0.0003334484,0.00009684191,0.0012326166,0.9424496,0.010690385,0.0016414835,0.021685155,0.0011817242],"about_ca_topic_score_codex":0.00023590164,"about_ca_topic_score_gemma":0.00029954835,"teacher_disagreement_score":0.938131,"about_ca_system_score_codex":0.00013457064,"about_ca_system_score_gemma":0.0000924689,"threshold_uncertainty_score":0.99974245},"labels":[],"label_agreement":null},{"id":"W2922355815","doi":"10.1109/sdpc.2018.8664946","title":"The Application of TOPSIS Decision and Random Forests Method in Tone Recognition","year":2018,"lang":"en","type":"article","venue":"2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC)","topic":"Music and Audio Processing","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":"Carleton University; Queen's University","funders":"","keywords":"TOPSIS; Piano; Tone (literature); Computer science; Speech recognition; Mel-frequency cepstrum; Artificial intelligence; Pattern recognition (psychology); Random forest; Feature extraction; Mathematics; Acoustics","score_opus":0.026552649667214944,"score_gpt":0.3158201262179539,"score_spread":0.289267476550739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922355815","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":[0.02953392,0.00040500273,0.96399873,0.0031235,0.0007730728,0.0004826942,0.000022348373,0.000039693245,0.0016210485],"genre_scores_gemma":[0.9660786,0.0011428453,0.03158071,0.0008076317,0.00030838026,0.000017799674,0.000011394763,0.000011785505,0.000040871724],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981472,0.00013061381,0.00054084766,0.0005027187,0.00041018697,0.00026844093],"domain_scores_gemma":[0.99534327,0.0029494036,0.0003705247,0.00029227737,0.0009473204,0.000097223616],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009271551,0.00020765088,0.00030151865,0.00016814201,0.00022801073,0.00032620216,0.00036865385,0.00011340752,0.000009554085],"category_scores_gemma":[0.003092219,0.00016432592,0.00004387466,0.00013719019,0.00036489777,0.00025343357,0.00014359006,0.00017703981,0.000017938259],"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":[0.00029312383,0.000075599266,0.0054544024,0.000014624965,0.000041861305,0.0000064902983,0.000484929,0.0000072874827,0.00039345442,0.032206696,0.0006929095,0.96032864],"study_design_scores_gemma":[0.006319608,0.0007334392,0.041470055,0.0008052859,0.0000795883,0.00005697529,0.00022170674,0.73823154,0.00440153,0.20085804,0.0062490045,0.0005731925],"about_ca_topic_score_codex":0.00016846227,"about_ca_topic_score_gemma":0.00068247854,"teacher_disagreement_score":0.9597554,"about_ca_system_score_codex":0.00003725112,"about_ca_system_score_gemma":0.00008955486,"threshold_uncertainty_score":0.67010164},"labels":[],"label_agreement":null}]}