{"meta":{"query_hash":"2c5a9e04f9ca","filters":{"topic":"Energy Load and Power Forecasting"},"cohort_total":993,"direct_labels_cover":1,"predictions_cover":993,"exported":993,"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/2c5a9e04f9ca","api":"https://metacan.xera.ac/api/v1/cohort?topic=Energy+Load+and+Power+Forecasting"},"results":[{"id":"W14765699","doi":"10.1007/978-1-4471-2201-2_9","title":"Grey Predictors for Hourly Wind Speed and Power Forecasting","year":2012,"lang":"en","type":"book-chapter","venue":"Green energy and technology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Natural Resources Canada","funders":"","keywords":"Dispatchable generation; Wind power; Wind speed; Electric power system; Electricity; Grid; Wind power forecasting; Power (physics); Base load power plant; Reliability engineering; Automotive engineering; Meteorology; Engineering; Environmental science; Renewable energy; Distributed generation; Electrical engineering; Geography","score_opus":0.013451019453374138,"score_gpt":0.1793223661208178,"score_spread":0.1658713466674437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W14765699","genre_codex":"other","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":[0.07879455,0.08094991,0.004256065,0.0007109324,0.0047366805,0.00084876094,0.0007234796,0.004772582,0.82420707],"genre_scores_gemma":[0.87638134,0.0019486246,0.001432425,0.00009820174,0.0009464436,0.000016235486,0.00019897363,0.00041413849,0.118563615],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987553,0.0000035708968,0.0003099419,0.0003487436,0.00009234635,0.00049010804],"domain_scores_gemma":[0.99938804,0.000079542384,0.00010158055,0.00025245768,0.000056008044,0.00012237787],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009341275,0.00045512052,0.0004737613,0.0004590598,0.00012947475,0.00002272796,0.00015532617,0.0011662372,0.000052826294],"category_scores_gemma":[0.000018918445,0.0004589311,0.00006956155,0.00006127931,0.00021559176,0.000107261905,0.00012249265,0.00033469428,0.0000019798963],"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":[0.000035961264,0.0000123173795,0.0012865873,0.00032729958,0.00073220086,0.000052953656,0.00019515262,0.0003221204,0.00030366742,0.6173148,0.0019583416,0.3774586],"study_design_scores_gemma":[0.0007001865,0.00027576322,0.00005209725,0.00036624898,0.00019365294,0.0003552,0.000017296168,0.0033707048,0.0005638404,0.04462695,0.94851273,0.0009653392],"about_ca_topic_score_codex":0.000031614414,"about_ca_topic_score_gemma":0.00011974099,"teacher_disagreement_score":0.94655436,"about_ca_system_score_codex":0.000027389862,"about_ca_system_score_gemma":0.000013121815,"threshold_uncertainty_score":0.99978626},"labels":[],"label_agreement":null},{"id":"W1480347577","doi":"","title":"Using Weather Sensitivity To Forecast Thailand's Electricity Demand","year":2008,"lang":"en","type":"article","venue":"Edinburgh Research Explorer","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Energy Policy and Planning Office; Electricity Generating Authority of Thailand","keywords":"Electricity demand; Sensitivity (control systems); Electricity; Demand forecasting; Climate change; Quarter (Canadian coin); Econometrics; Environmental science; Meteorology; Environmental economics; Economics; Electricity generation; Engineering; Geography; Operations management; Power (physics)","score_opus":0.14856786020169271,"score_gpt":0.32843523984318806,"score_spread":0.17986737964149535,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1480347577","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":[0.96383774,0.00027162588,0.02006423,0.000102349244,0.0004866805,0.000245333,0.000007547557,0.000313887,0.014670613],"genre_scores_gemma":[0.9966623,0.00013837047,0.0013822524,0.000052038027,0.0011130436,0.000038484963,0.0000058567557,0.00006741928,0.0005402374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974802,0.00012671624,0.00022800505,0.00034705354,0.0007460951,0.0010719097],"domain_scores_gemma":[0.998661,0.00035047866,0.000015827429,0.0003353493,0.00017587932,0.00046149147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013576568,0.00022984337,0.00026119777,0.0004301461,0.00044935438,0.000056437184,0.00018017089,0.00012849382,0.00034607935],"category_scores_gemma":[0.0002662759,0.00021077656,0.00008846121,0.0009869738,0.00010099584,0.0002772783,0.00012721776,0.0006092951,0.000060083832],"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":[0.000308045,0.00028431643,0.03280636,0.000115889234,0.00037367336,0.0025489468,0.026061034,0.3791288,0.26075768,0.0005544881,0.2780713,0.018989436],"study_design_scores_gemma":[0.0017113055,0.0005073758,0.0034447752,0.00035903367,0.000032111082,0.0010379251,0.0009742592,0.51855093,0.2921301,0.0011709552,0.1780686,0.0020126298],"about_ca_topic_score_codex":0.00010289873,"about_ca_topic_score_gemma":0.000035509074,"teacher_disagreement_score":0.13942212,"about_ca_system_score_codex":0.00022624843,"about_ca_system_score_gemma":0.00007215141,"threshold_uncertainty_score":0.85952187},"labels":[],"label_agreement":null},{"id":"W1482131604","doi":"","title":"MODELING OF THE MAXIMUM ENTROPY PROBLEM AS AN OPTIMAL CONTROL PROBLEM AND ITS APPLICATION TO PDF ESTIMATION OF ELECTRICITY PRICE","year":2013,"lang":"en","type":"article","venue":"Iranian journal of electrical and electronic engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"Estimator; Principle of maximum entropy; Probability density function; Electricity; Mathematical optimization; Random variable; Entropy (arrow of time); Moment (physics); Electricity market; Probability distribution; Electricity price; Mathematics; Econometrics; Computer science; Statistics; Engineering","score_opus":0.0031972312622305945,"score_gpt":0.17969394721844992,"score_spread":0.17649671595621932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1482131604","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":[0.72378886,0.0013856965,0.2743649,0.000059962098,0.000020619136,0.00027132028,6.505018e-7,0.000023853801,0.00008412421],"genre_scores_gemma":[0.99648166,0.0001383033,0.0032600283,0.000014064121,0.000057629655,0.000015332538,6.352715e-7,0.000026234122,0.0000061174555],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867696,0.000025366167,0.0005440171,0.000118167714,0.00021401203,0.00042147416],"domain_scores_gemma":[0.9994283,0.000056844056,0.00012754141,0.00009097572,0.00014350736,0.00015283153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027174482,0.00018068819,0.000320222,0.00017678215,0.000045955596,0.000025893421,0.00017392595,0.00008400839,0.0000074822246],"category_scores_gemma":[0.00005496099,0.00014277149,0.00006260771,0.00045346105,0.000009315667,0.00028424707,0.000015022961,0.000393234,0.0000012242889],"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":[0.00003252111,0.00002341467,0.000037733807,0.000090958754,0.000053623586,3.2852228e-7,0.0001639865,0.8697413,0.117814586,0.0019982266,0.0000023685855,0.01004094],"study_design_scores_gemma":[0.00048986037,0.0005380316,0.00014076543,0.00008949229,0.000044355005,0.000106835905,0.000006729067,0.9756395,0.021734701,0.0010308292,0.000036928384,0.00014195433],"about_ca_topic_score_codex":0.000014425244,"about_ca_topic_score_gemma":0.000001349363,"teacher_disagreement_score":0.27269277,"about_ca_system_score_codex":0.000083589934,"about_ca_system_score_gemma":0.000054692013,"threshold_uncertainty_score":0.58220524},"labels":[],"label_agreement":null},{"id":"W1492878499","doi":"10.24297/ijct.v14i2.2077","title":"A Statistical Analysis and Datamining Approach for Wind Speed Predication","year":2014,"lang":"en","type":"article","venue":"INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY","topic":"Energy Load and Power Forecasting","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":"Horizon College and Seminary","funders":"Jain University","keywords":"Wind speed; Wind power; Weibull distribution; Computer science; Artificial neural network; Data mining; Engineering; Meteorology; Artificial intelligence; Statistics; Geography","score_opus":0.007839946743636412,"score_gpt":0.2348783121051017,"score_spread":0.2270383653614653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1492878499","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":[0.099627815,0.000056697772,0.8994735,0.00016383496,0.0004255911,0.000024013872,0.000010608366,0.000044826957,0.00017310175],"genre_scores_gemma":[0.8633823,0.000015435324,0.13636518,0.00002496269,0.00017122747,3.9950396e-7,0.000029633778,0.0000072956595,0.0000035748724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999416,0.000009683523,0.00026714083,0.0000827189,0.00013095653,0.000093533585],"domain_scores_gemma":[0.9995426,0.00012865436,0.00010653445,0.000068468886,0.00011824107,0.000035515684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018944907,0.000069972324,0.00016763125,0.00060152233,0.000018667006,0.000031579584,0.0002612998,0.00006647293,0.0000029077235],"category_scores_gemma":[0.00007506781,0.00006678724,0.00004030475,0.00015536159,0.000049438364,0.00008794505,0.00003834916,0.00012310427,2.6491963e-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":[0.000071930044,0.00006509419,0.0281544,0.000038281654,0.002976668,0.0000138949945,0.00020507751,0.7005266,0.0018316252,0.019680265,0.0012910834,0.24514507],"study_design_scores_gemma":[0.00044862658,0.00010012828,0.0029970687,0.00002239705,0.00011889271,0.00012056707,0.000020546038,0.9908853,0.000486926,0.0013524498,0.0033722625,0.000074796364],"about_ca_topic_score_codex":0.0000013302395,"about_ca_topic_score_gemma":7.939165e-7,"teacher_disagreement_score":0.7637545,"about_ca_system_score_codex":0.000027550434,"about_ca_system_score_gemma":0.000007647482,"threshold_uncertainty_score":0.27235043},"labels":[],"label_agreement":null},{"id":"W1517243424","doi":"10.1109/icnn.1994.375040","title":"A decomposition approach to forecasting electric power system commercial load using an artificial neural network","year":2002,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Technical University of Nova Scotia","funders":"","keywords":"Artificial neural network; Backpropagation; Electric power system; Computer science; Set (abstract data type); Electrical load; Power (physics); Artificial intelligence; Machine learning; Data mining; Engineering; Voltage; Electrical engineering","score_opus":0.05050799430647605,"score_gpt":0.2326354485069009,"score_spread":0.18212745420042487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1517243424","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":[0.8565564,0.00013823083,0.09361311,0.000008169704,0.00079627987,0.00018812873,0.0000021941028,0.0007549788,0.047942482],"genre_scores_gemma":[0.9865407,6.721655e-7,0.0122916335,0.00008118492,0.0009777097,0.000012818065,0.000009410693,0.00006575845,0.000020166872],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984187,0.000053954103,0.00037427244,0.00026809034,0.00022852796,0.0006564539],"domain_scores_gemma":[0.99946654,0.0000386296,0.000040125557,0.00019979916,0.000052203763,0.00020268929],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020899423,0.0002527056,0.00025247154,0.00011720241,0.00027366204,0.00013467317,0.00015973616,0.00011446739,0.0000412361],"category_scores_gemma":[0.000009716737,0.00025981668,0.00007819902,0.00061859033,0.000008933354,0.0002559941,0.000031833522,0.0002133092,0.000023963119],"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":[0.000011016812,0.000044771205,0.00010197265,0.000032831475,0.000018741326,0.000008105021,0.00047530024,0.98557574,0.0034752714,0.0008206992,0.0004505734,0.008984997],"study_design_scores_gemma":[0.000117187476,0.000073907955,0.000059490107,0.000050235496,0.000019994792,0.00013979388,0.000086139036,0.99813765,0.00080472213,0.000011654131,0.00017654702,0.00032264693],"about_ca_topic_score_codex":0.000057170026,"about_ca_topic_score_gemma":0.000035094432,"teacher_disagreement_score":0.12998421,"about_ca_system_score_codex":0.00024024484,"about_ca_system_score_gemma":0.0000073543133,"threshold_uncertainty_score":0.9999854},"labels":[],"label_agreement":null},{"id":"W1522299423","doi":"10.1109/psce.2004.1397570","title":"Short-term electricity price modeling and forecasting using wavelets and multivariate time series","year":2005,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":42,"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 British Columbia","funders":"","keywords":"Wavelet; Multivariate statistics; Term (time); Electricity price forecasting; Series (stratigraphy); Electricity market; Time series; Wavelet transform; Spot contract; Electricity; Computer science; Econometrics; Mathematical optimization; Economics; Mathematics; Finance; Artificial intelligence; Engineering; Machine learning; Geology","score_opus":0.0243386313237814,"score_gpt":0.22097219123528128,"score_spread":0.1966335599114999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1522299423","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":[0.9423522,0.00037533752,0.05124013,0.0000122787405,0.000043572243,0.00006715048,0.000001971526,0.0002654258,0.005641968],"genre_scores_gemma":[0.9597622,0.000051527248,0.039842818,0.000016620314,0.00014906177,0.0000021516155,0.0000029707273,0.00003718579,0.00013543495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912155,0.0000120362465,0.0002389562,0.0001964349,0.000087329965,0.00034367142],"domain_scores_gemma":[0.9997277,0.000049045357,0.000018655643,0.0000841546,0.000026785776,0.0000936615],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014676584,0.00018921058,0.00018849934,0.00008050021,0.00014474917,0.00007656288,0.000048032118,0.00007849901,0.000016258897],"category_scores_gemma":[0.00002513054,0.00018195603,0.000022646023,0.0001127554,0.000017335176,0.00044029162,0.000054180888,0.00014208947,0.000001826737],"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":[0.000017644912,0.00001325444,0.0019799469,0.00011740135,0.000069432564,0.000010623572,0.0007712031,0.7564186,0.13195105,0.00039638818,0.00000931402,0.10824516],"study_design_scores_gemma":[0.00014266517,0.000011689081,0.00029388702,0.000049162958,0.000012582262,0.00010073426,0.000013291582,0.99322695,0.0057936856,0.000037239566,0.000075729804,0.00024239071],"about_ca_topic_score_codex":0.000027119027,"about_ca_topic_score_gemma":0.000017549912,"teacher_disagreement_score":0.23680836,"about_ca_system_score_codex":0.000038053055,"about_ca_system_score_gemma":0.0000063912057,"threshold_uncertainty_score":0.74199516},"labels":[],"label_agreement":null},{"id":"W1532227133","doi":"10.1109/ptc.2015.7232253","title":"Local Learning-ARIMA adaptive hybrid architecture for hourly electricity price forecasting","year":2015,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo; Natural Resources Canada","funders":"","keywords":"Autoregressive integrated moving average; Electricity price forecasting; Computer science; Architecture; Electricity; Electricity market; Autoregressive model; Artificial intelligence; Machine learning; Time series; Econometrics; Industrial engineering; Engineering; Economics","score_opus":0.025950580308501645,"score_gpt":0.20963019776600558,"score_spread":0.18367961745750394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1532227133","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":[0.059115104,0.00024788184,0.8943652,0.000027272241,0.00025728554,0.00016662476,0.000007788531,0.0006866316,0.045126196],"genre_scores_gemma":[0.9803265,0.0000032766,0.018527355,0.000055256663,0.0002660764,0.000029567083,0.000024240113,0.000067021465,0.0007007296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988105,0.000025580524,0.00021901791,0.00022611734,0.00017755153,0.0005412253],"domain_scores_gemma":[0.9993212,0.0002105726,0.00004369464,0.00011647963,0.00010402414,0.00020400304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027274754,0.00023184576,0.00022143334,0.00010479869,0.00010207723,0.00004379172,0.00015267805,0.00007865751,0.000015140966],"category_scores_gemma":[0.00018458694,0.00021127303,0.000092178976,0.00021843909,0.000030212803,0.00011773017,0.00003376368,0.00039472544,0.000013280507],"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":[0.00006201347,0.0000147246865,0.00013662671,0.000037597594,0.00005685438,0.000011580608,0.00044199405,0.9055319,0.00027626348,0.0011094484,0.003786066,0.088534966],"study_design_scores_gemma":[0.0006086035,0.0003656798,0.000023952394,0.000033949513,0.00001930694,0.0000840844,0.0001491099,0.94809115,0.013292398,0.0012749821,0.0356938,0.0003629941],"about_ca_topic_score_codex":0.00006771216,"about_ca_topic_score_gemma":0.00005736076,"teacher_disagreement_score":0.92121136,"about_ca_system_score_codex":0.00011727001,"about_ca_system_score_gemma":0.00004760532,"threshold_uncertainty_score":0.86154646},"labels":[],"label_agreement":null},{"id":"W1535194096","doi":"","title":"A Deterministic Bases Piecewise Wind Power Forecasting Models","year":2014,"lang":"en","type":"article","venue":"DergiPark (Istanbul University)","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Renewable energy; Wind power; Wind speed; Turbine; Piecewise; Wind power forecasting; Piecewise linear function; Estimator; Power (physics); Computer science; Econometrics; Meteorology; Environmental science; Mathematical optimization; Simulation; Engineering; Control theory (sociology); Electric power system; Mathematics; Statistics; Electrical engineering; Artificial intelligence; Mechanical engineering","score_opus":0.015590588547133006,"score_gpt":0.16159574741381974,"score_spread":0.14600515886668675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1535194096","genre_codex":"other","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":[0.4687183,0.0000940947,0.058254022,0.000020476316,0.00050301943,0.00010121345,0.000025082061,0.00065361586,0.4716302],"genre_scores_gemma":[0.99635977,0.000016985194,0.0016590145,0.00004821247,0.00008456582,4.7076438e-7,0.000014539459,0.000052812942,0.0017636139],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988143,0.000046977497,0.00019521509,0.0002904297,0.00017395747,0.00047916398],"domain_scores_gemma":[0.99915344,0.00022827208,0.000053905384,0.00031148706,0.000055055585,0.00019785116],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012286694,0.00026686367,0.00025165934,0.0002805824,0.00020726504,0.000059122485,0.00029395992,0.00011229778,0.00011099262],"category_scores_gemma":[0.000057867554,0.00031584268,0.000112241236,0.00041540014,0.00006833449,0.00037852465,0.00007508561,0.00019665374,0.000028423621],"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":[0.000074314936,0.000056754274,0.00042564343,0.00015663517,0.00013160234,0.00052832736,0.001455542,0.94390154,0.0006842647,0.042290214,0.002211607,0.00808358],"study_design_scores_gemma":[0.00076381327,0.00009018541,0.000081279635,0.00015296265,0.00006797933,0.000053821674,0.0003682885,0.79482585,0.00038326965,0.0011070302,0.20147514,0.00063039275],"about_ca_topic_score_codex":0.000016787031,"about_ca_topic_score_gemma":0.000046901943,"teacher_disagreement_score":0.5276415,"about_ca_system_score_codex":0.00011404669,"about_ca_system_score_gemma":0.000027300883,"threshold_uncertainty_score":0.99992937},"labels":[],"label_agreement":null},{"id":"W1545792434","doi":"","title":"Improvement of short-term numerical wind predictions","year":2010,"lang":"en","type":"article","venue":"Espace École de technologie supérieure (École de technologie supérieure)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; École de technologie supérieure; Université de Moncton","keywords":"Wind power; Meteorology; Environmental science; Electricity; Wind speed; Term (time); Electricity generation; Grid; Renewable energy; Power (physics); Engineering; Geography","score_opus":0.009259405993354532,"score_gpt":0.23379763817426974,"score_spread":0.2245382321809152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1545792434","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":[0.9291388,0.00051327626,0.049660165,0.0009824695,0.0012471324,0.0010625946,0.00019818153,0.013543883,0.0036535298],"genre_scores_gemma":[0.99012876,0.00021196915,0.008257891,0.00011633057,0.0002104799,0.00035500844,0.000053324555,0.00022984913,0.00043642052],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9952487,0.000032594227,0.0010563874,0.0010271759,0.0006096987,0.002025451],"domain_scores_gemma":[0.99665123,0.0002572349,0.00022758395,0.0023069142,0.00021748409,0.00033955456],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00061485125,0.0009900182,0.0009893487,0.0009982932,0.00033274107,0.00012271544,0.0022459307,0.0023783918,0.00014682546],"category_scores_gemma":[0.0010885969,0.0010163456,0.0004106011,0.0021143318,0.0008716711,0.00036762995,0.000736844,0.0036484876,0.000042090694],"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":[0.00009169061,0.00058532326,0.080579855,0.00034027544,0.00040751952,0.00039890088,0.0004581873,0.13221455,0.74871963,0.00487518,0.0067145745,0.024614304],"study_design_scores_gemma":[0.00194435,0.0017956547,0.017389085,0.0003508623,0.00033135666,0.0006373034,0.003554887,0.13834149,0.79246294,0.0029206197,0.03741468,0.0028567733],"about_ca_topic_score_codex":0.00006586725,"about_ca_topic_score_gemma":0.00040451856,"teacher_disagreement_score":0.063190766,"about_ca_system_score_codex":0.00029101793,"about_ca_system_score_gemma":0.00027835614,"threshold_uncertainty_score":0.9992287},"labels":[],"label_agreement":null},{"id":"W1554932412","doi":"10.5539/mas.v9n11p1","title":"Imputation of Missing Values in Daily Wind Speed Data Using Hybrid AR-ANN Method","year":2015,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":32,"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":"Missing data; Imputation (statistics); Autoregressive model; Wind speed; Computer science; Artificial neural network; Nonlinear system; Time series; Data mining; Statistics; Pattern recognition (psychology); Artificial intelligence; Mathematics; Machine learning; Meteorology","score_opus":0.08725535616823027,"score_gpt":0.3173799680039748,"score_spread":0.2301246118357445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1554932412","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":[0.41001257,0.00009193318,0.5853351,0.0000060010952,0.00018033743,0.000057834757,0.00000766229,0.000055002132,0.004253555],"genre_scores_gemma":[0.88427126,9.803674e-7,0.11564591,0.000011535866,0.000039397823,2.175895e-7,0.000010057428,0.000014965227,0.0000057032125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987699,0.000015740472,0.0002509119,0.00031385955,0.0003645284,0.0002850604],"domain_scores_gemma":[0.9993683,0.00004820591,0.000057761867,0.0003851562,0.000039534134,0.0001010599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014912399,0.00011393417,0.00017098803,0.00018355408,0.0000633345,0.00006534635,0.00051749806,0.000027849068,0.0000018480013],"category_scores_gemma":[0.000071144095,0.00011684371,0.000012074366,0.00048671552,0.000114745424,0.00043690787,0.00016683937,0.00010629146,0.0000022113566],"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":[0.00000386013,0.0000063143693,0.000074653806,0.0000134013935,0.0000021739222,0.0000025975871,0.0010496713,0.6473925,0.32882845,0.000052541956,0.000013288721,0.022560537],"study_design_scores_gemma":[0.00018975673,0.000004792649,0.00010167032,0.000033563232,0.000005741671,0.000010208825,0.00010287551,0.9375435,0.056774333,0.005085331,0.000025211499,0.0001230258],"about_ca_topic_score_codex":0.000059629063,"about_ca_topic_score_gemma":0.000004347756,"teacher_disagreement_score":0.47425866,"about_ca_system_score_codex":0.000085509586,"about_ca_system_score_gemma":0.00011913738,"threshold_uncertainty_score":0.4764748},"labels":[],"label_agreement":null},{"id":"W1574664227","doi":"10.5772/51306","title":"Towards Developing a Decision Support System for Electricity Load Forecast","year":2012,"lang":"en","type":"book-chapter","venue":"InTech eBooks","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electric power system; Electricity market; Purchasing; Electricity; Computer science; Term (time); Control (management); Operations research; Unit (ring theory); Reliability engineering; Operations management; Power (physics); Engineering; Artificial intelligence","score_opus":0.027987430682799606,"score_gpt":0.23505301094390949,"score_spread":0.20706558026110988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574664227","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00015516262,0.0007078195,0.30797172,0.0000029450177,0.001632417,0.00043416658,0.00007105073,0.00083848636,0.6881862],"genre_scores_gemma":[0.7455037,0.00013399686,0.042964537,0.00012199864,0.0028718277,0.0003870519,0.00017858965,0.0009823347,0.20685594],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99790233,0.000005205286,0.00067258487,0.00036335696,0.0003877627,0.0006687578],"domain_scores_gemma":[0.9989262,0.00014853824,0.00015057746,0.0003800605,0.0002334022,0.00016118797],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00039090693,0.0006110567,0.000644798,0.00027405896,0.000117630014,0.00006625718,0.00037001423,0.00070320163,0.00005548333],"category_scores_gemma":[0.000040133014,0.00059798633,0.00032924346,0.00002622639,0.00003700069,0.00006673461,0.000101635684,0.0005248753,0.00009811243],"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":[0.00007118559,0.000003896472,0.000002453109,0.0012159577,0.0003145769,0.00003821003,0.00034677878,0.000036896752,0.0034773524,0.065022774,0.0014403275,0.9280296],"study_design_scores_gemma":[0.0004241492,0.000110028646,9.271198e-7,0.0024825633,0.00012969831,0.00018489463,0.000014415673,0.00089590775,0.13225381,0.0061836676,0.8562535,0.0010664383],"about_ca_topic_score_codex":0.000013016692,"about_ca_topic_score_gemma":0.000055271383,"teacher_disagreement_score":0.92696315,"about_ca_system_score_codex":0.0010402381,"about_ca_system_score_gemma":0.00024172253,"threshold_uncertainty_score":0.99964714},"labels":[],"label_agreement":null},{"id":"W1574846400","doi":"10.1109/34084poweri.2014.7117662","title":"Short-term load forecasting of Ontario Electricity Market by considering the effect of temperature","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"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":"Term (time); Artificial neural network; Electricity; Scheduling (production processes); Electricity market; Electric power system; Computer science; Reliability (semiconductor); Electrical load; Reliability engineering; Peak load; Power (physics); Operations research; Engineering; Automotive engineering; Artificial intelligence; Operations management; Voltage; Electrical engineering","score_opus":0.006535542110276217,"score_gpt":0.1843637885107121,"score_spread":0.1778282464004359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1574846400","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":[0.9388766,0.00019226594,0.0004811743,0.0000048122524,0.00015948793,0.00009893808,0.0000030514875,0.000075281,0.060108345],"genre_scores_gemma":[0.9990456,0.000004579763,0.0002487722,0.000009237466,0.00004408947,0.0000061337446,0.000003883208,0.00002376048,0.00061393995],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911034,0.00004997415,0.00028907665,0.00012544643,0.00017442777,0.0002507396],"domain_scores_gemma":[0.9991827,0.00049110176,0.000043210373,0.00020535396,0.000035465288,0.0000421693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051393145,0.00018107872,0.00029505693,0.000035176537,0.000053048014,0.000017613327,0.00014833282,0.0000932096,0.000102012265],"category_scores_gemma":[0.00010324609,0.00011959731,0.00009060754,0.00013601857,0.000036439542,0.00006565846,0.00003234193,0.0002577384,6.597864e-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":[0.00018573053,0.00004202543,0.35197687,0.0014657906,0.00058772013,0.000009720433,0.0027693918,0.05048402,0.4979852,0.00042943683,0.020908564,0.07315551],"study_design_scores_gemma":[0.0004990279,0.00030511865,0.003866637,0.00024540402,0.00006437594,0.000036715428,0.000012170844,0.060609095,0.9295559,0.000026188942,0.004455404,0.0003239503],"about_ca_topic_score_codex":0.0010155948,"about_ca_topic_score_gemma":0.0038003407,"teacher_disagreement_score":0.4315707,"about_ca_system_score_codex":0.00009022788,"about_ca_system_score_gemma":0.000026677997,"threshold_uncertainty_score":0.48770365},"labels":[],"label_agreement":null},{"id":"W1583891148","doi":"10.1109/nafips.2001.943625","title":"Fuzzy regression models to represent electricity market data in deregulated power industry","year":2002,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 British Columbia","funders":"University of Tokyo","keywords":"Electricity market; Fuzzy logic; Regression analysis; Econometrics; Electricity; Regression; Data modeling; Supply and demand; Computer science; Power demand; Power (physics); Electric power system; Electric power industry; Demand forecasting; Economics; Microeconomics; Engineering; Statistics; Mathematics; Operations management; Artificial intelligence; Electrical engineering; Machine learning; Power consumption","score_opus":0.04804967602957257,"score_gpt":0.24545576817360937,"score_spread":0.1974060921440368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1583891148","genre_codex":"other","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":[0.4740796,0.0004705125,0.0025619364,0.00018021133,0.000224167,0.00013378961,0.000011391641,0.00040858812,0.5219298],"genre_scores_gemma":[0.9942603,0.000037434365,0.0017412074,0.000090550784,0.00004275435,0.0000048276497,0.000017506834,0.000029571756,0.0037758416],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998911,0.000028575252,0.00024036858,0.00029960307,0.00016905379,0.00035137718],"domain_scores_gemma":[0.99906117,0.00004561157,0.000016551965,0.0007360312,0.000016148728,0.00012446064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018202644,0.0001556562,0.00015494852,0.00015563144,0.000030386966,0.000029434728,0.000349209,0.00023074436,0.0008760373],"category_scores_gemma":[0.000044155116,0.00013548335,0.000018989898,0.0005282255,0.000006927023,0.0002881251,0.00017779725,0.0003984298,0.000026161648],"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":[0.000035321667,0.0001294904,0.004074623,0.000043163425,0.000045102308,0.00010974181,0.00072336895,0.5805544,0.0034542393,0.00083163584,0.3749964,0.035002492],"study_design_scores_gemma":[0.0002279033,0.000015320324,0.0009789014,0.00012206949,0.0000035568028,0.000012207932,0.000031310756,0.99163866,0.0027578997,0.00033002972,0.0036309992,0.0002511513],"about_ca_topic_score_codex":0.000102391954,"about_ca_topic_score_gemma":0.00008081797,"teacher_disagreement_score":0.5201807,"about_ca_system_score_codex":0.000049756196,"about_ca_system_score_gemma":0.0000046762652,"threshold_uncertainty_score":0.9591994},"labels":[],"label_agreement":null},{"id":"W158863919","doi":"10.1007/978-81-322-0987-4_4","title":"Application of Hourly Time Series Models in Day-ahead Wind Power Commitment","year":2013,"lang":"en","type":"book-chapter","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wind power; Installation; Wind speed; Meteorology; Position (finance); Environmental economics; Power (physics); Environmental science; Engineering; Electrical engineering; Business; Economics; Geography; Finance; Physics","score_opus":0.01087952588014152,"score_gpt":0.18644052362434657,"score_spread":0.17556099774420506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W158863919","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00046594974,0.00041072298,0.0054903855,0.00002185246,0.00010486738,0.00023972381,0.000036138314,0.000144151,0.9930862],"genre_scores_gemma":[0.29836643,0.00021854279,0.0045019705,0.000052203304,0.00014173031,0.000044466546,0.0003010443,0.0002335449,0.69614005],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991517,0.0000045459487,0.00036089102,0.00016953718,0.00014589509,0.00016739848],"domain_scores_gemma":[0.99951947,0.000028753719,0.00006688278,0.00030166522,0.000035376925,0.00004783176],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007800563,0.00026173363,0.0003365642,0.00013090215,0.000014047265,0.000015554722,0.00014166078,0.00025361538,0.00082922954],"category_scores_gemma":[0.0000011608132,0.00025391008,0.000068305526,0.000024780249,0.000031727326,0.000183814,0.00003746834,0.00019914332,0.00023035397],"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":[0.000012209873,0.000035945093,0.00003727752,0.00023896655,0.00022615024,0.0000073735428,0.0011341622,0.3215342,0.0013927474,0.64342874,0.009650891,0.022301316],"study_design_scores_gemma":[0.0011004347,0.00030011826,0.00026368105,0.0014337518,0.00011086404,0.000025413636,0.000051284795,0.32270107,0.003648203,0.10469015,0.563056,0.0026190223],"about_ca_topic_score_codex":0.00007258186,"about_ca_topic_score_gemma":0.000048148282,"teacher_disagreement_score":0.5534051,"about_ca_system_score_codex":0.00006047855,"about_ca_system_score_gemma":0.000010325824,"threshold_uncertainty_score":0.9999913},"labels":[],"label_agreement":null},{"id":"W1595816206","doi":"10.3233/ida-2000-43-414","title":"An architectural framework for hybrid intelligent systems: Implementation issues","year":2000,"lang":"en","type":"article","venue":"Intelligent Data Analysis","topic":"Energy Load and Power Forecasting","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 Regina","funders":"","keywords":"Computer science; Artificial neural network; Modularity (biology); Set (abstract data type); Hybrid system; Artificial intelligence; Programming language; Machine learning","score_opus":0.0433581560745468,"score_gpt":0.34678100658930816,"score_spread":0.30342285051476137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1595816206","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":[0.13075723,0.002052886,0.86447364,0.00004112177,0.0005008953,0.00027276544,0.0012983179,0.0003489188,0.00025420496],"genre_scores_gemma":[0.9722294,0.0007848943,0.0165745,0.00005100144,0.00088529865,0.000058750185,0.009207871,0.000055962984,0.00015228818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827415,0.000052298084,0.0005701261,0.0004664513,0.00023282984,0.00040413404],"domain_scores_gemma":[0.9984262,0.00014146291,0.000056876663,0.0011794287,0.00005133565,0.00014471177],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00033728575,0.0002559306,0.00036676897,0.0003055295,0.00011146432,0.00024649155,0.00077565585,0.00006144917,0.0014057839],"category_scores_gemma":[0.00002713661,0.00024324354,0.00017448801,0.0005647637,0.000026196229,0.00035261712,0.000043832286,0.00015022859,0.00007391904],"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":[0.00001592078,0.000027095759,0.0007588876,0.000051900985,0.0014136527,0.000004143536,0.0005171688,0.5997663,0.000043923243,0.0008127226,0.0011842101,0.39540404],"study_design_scores_gemma":[0.000064012,0.00007048928,0.0000837991,0.000046026813,0.0009978071,0.0000074713844,0.00067674246,0.86776924,0.00650287,0.0007148507,0.12265603,0.00041067047],"about_ca_topic_score_codex":0.00069247716,"about_ca_topic_score_gemma":0.00038674273,"teacher_disagreement_score":0.84789914,"about_ca_system_score_codex":0.000062793275,"about_ca_system_score_gemma":0.000010072941,"threshold_uncertainty_score":0.99950707},"labels":[],"label_agreement":null},{"id":"W1597102781","doi":"10.1201/9781482270099","title":"Electric Systems, Dynamics, and Stability with Artificial Intelligence Applications","year":2018,"lang":"en","type":"book","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Stability (learning theory); Electric power system; Transient (computer programming); Computer science; Voltage; Artificial neural network; Artificial intelligence; Control engineering; Control theory (sociology); Engineering; Power (physics); Electrical engineering; Control (management); Machine learning; Physics","score_opus":0.014529705951182548,"score_gpt":0.2026665200789142,"score_spread":0.18813681412773164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1597102781","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00018575083,0.000984891,0.3562441,0.000004209236,0.00015738705,0.0003258774,0.00003944386,0.00039239632,0.64166594],"genre_scores_gemma":[0.43377274,0.0011143937,0.00737343,0.000040950064,0.0036840364,0.00067192083,0.0011660649,0.00058109645,0.55159533],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991985,0.000008562486,0.00024755666,0.00024121524,0.00010942771,0.00019477021],"domain_scores_gemma":[0.9994862,0.00007623563,0.000041981704,0.00026111986,0.000065019354,0.00006942535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011034778,0.00021626112,0.00022003017,0.00008510129,0.00006195556,0.000060623475,0.000110789886,0.00017553181,0.00006220276],"category_scores_gemma":[0.000004394137,0.00018548527,0.000021216776,0.000103046244,0.00006382894,0.00004119052,0.000020196121,0.00022193902,0.00003622964],"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":[0.00001712043,0.000032284446,0.00008611932,0.0017724968,0.00024486106,0.0000052907676,0.00015624899,0.007333646,0.000060062943,0.8950227,0.0050153923,0.090253785],"study_design_scores_gemma":[0.00004073806,0.00017837739,0.0000075946646,0.0003379975,0.00012205731,0.000054481967,0.00010706968,0.8961662,0.0006895598,0.009557972,0.0915986,0.001139341],"about_ca_topic_score_codex":0.000020353446,"about_ca_topic_score_gemma":0.00035208528,"teacher_disagreement_score":0.88883257,"about_ca_system_score_codex":0.00018975513,"about_ca_system_score_gemma":0.00006466822,"threshold_uncertainty_score":0.756387},"labels":[],"label_agreement":null},{"id":"W1613622704","doi":"","title":"Forecasting for Wind Power Plants in Quebec, Canada","year":2010,"lang":"en","type":"article","venue":"EGU General Assembly Conference Abstracts","topic":"Energy Load and Power Forecasting","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":"Wind power; Meteorology; Environmental science; Order (exchange); Geography; Operations research; Business; Engineering; Finance","score_opus":0.018930946693124128,"score_gpt":0.2154493341021345,"score_spread":0.19651838740901037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1613622704","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":[0.98315203,0.000036532303,0.00010650893,0.000115169074,0.0019438256,0.00016217715,0.00003612829,0.00009178715,0.014355859],"genre_scores_gemma":[0.9971428,0.000004434701,0.0012316112,0.00008836926,0.00032581107,0.000020507418,0.000060815677,0.000045378143,0.0010802816],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845976,0.000009603797,0.0004154415,0.00026817148,0.00019737768,0.0006496419],"domain_scores_gemma":[0.9993404,0.00013957027,0.000071904695,0.00020692009,0.00006421526,0.00017698442],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000175665,0.00027206185,0.00025618274,0.00008892999,0.0000782773,0.00011228638,0.0002526978,0.00016580369,0.00006915787],"category_scores_gemma":[0.000081954866,0.00027784205,0.000048063765,0.00009560139,0.000019363302,0.00026073164,0.000026303609,0.00042554585,0.00000546349],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000067128494,0.000094710995,0.0069215884,0.00019781785,0.000119299344,0.00033078418,0.0014039199,0.42669496,0.49004662,0.0029445158,0.018610861,0.052567817],"study_design_scores_gemma":[0.0032930926,0.00012961765,0.1620153,0.000553984,0.000035221296,0.00012396538,0.0005997272,0.3338151,0.33273485,0.001029455,0.16238499,0.0032847004],"about_ca_topic_score_codex":0.32561418,"about_ca_topic_score_gemma":0.98370856,"teacher_disagreement_score":0.65809435,"about_ca_system_score_codex":0.00008900198,"about_ca_system_score_gemma":0.00039358193,"threshold_uncertainty_score":0.9999674},"labels":[],"label_agreement":null},{"id":"W1631788667","doi":"","title":"Fuzzy system applications for short-term electric load forecasting.","year":2001,"lang":"en","type":"article","venue":"Library and Archives Canada (Government of Canada)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Fuzzy logic; Electric power system; Term (time); Electrical load; Computer science; Mathematical optimization; Fuzzy number; Scheduling (production processes); Fuzzy set; Control theory (sociology); Power (physics); Mathematics; Artificial intelligence","score_opus":0.005702016083041645,"score_gpt":0.14503480379676667,"score_spread":0.13933278771372504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1631788667","genre_codex":"other","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":[0.12669206,0.0012111309,0.0068109003,0.00023271171,0.00033145922,0.0004914781,0.00018627313,0.00012050133,0.8639235],"genre_scores_gemma":[0.9970535,0.00008644461,0.0010094136,0.00007299284,0.00013447195,0.00006841537,0.000010567255,0.00003096849,0.0015332042],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987568,0.000008867294,0.00023385398,0.00015478779,0.0005391577,0.0003064845],"domain_scores_gemma":[0.9994951,0.00015991696,0.00004132794,0.00013684698,7.1541507e-7,0.00016611292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00000896617,0.00015400065,0.00017575969,0.000020790223,0.00014139358,0.000017838605,0.0001461819,0.000023905668,0.0000033678702],"category_scores_gemma":[0.0000012143702,0.00015853315,0.00002827716,0.00010649652,0.0000150292335,0.000156438,0.000029358913,0.00007801809,1.608318e-9],"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":[0.00055396167,0.000065885484,0.054896493,0.003830166,0.00054025353,0.00019671845,0.00025639078,0.026263038,0.10750922,0.47186878,0.0037787287,0.33024037],"study_design_scores_gemma":[0.001344547,0.00023194561,0.014061624,0.0007264765,0.00015405337,0.00024159529,0.0020712635,0.41775683,0.19605821,0.0020379985,0.36362514,0.0016903096],"about_ca_topic_score_codex":0.00024872244,"about_ca_topic_score_gemma":0.0064605656,"teacher_disagreement_score":0.87036145,"about_ca_system_score_codex":0.000017973764,"about_ca_system_score_gemma":0.0003533298,"threshold_uncertainty_score":0.6464794},"labels":[],"label_agreement":null},{"id":"W1643103419","doi":"10.1109/ccece.1995.528149","title":"Real-time electricity pricing using a neural network approach","year":2002,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Technical University of Nova Scotia","funders":"","keywords":"Artificial neural network; Backpropagation; Electricity; Computer science; Feedforward neural network; Electric power system; Spot contract; Electricity market; Electricity pricing; Feed forward; Artificial intelligence; Power (physics); Engineering; Control engineering; Economics","score_opus":0.02074232885734734,"score_gpt":0.197014736788108,"score_spread":0.17627240793076066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1643103419","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":[0.6499655,0.00024551645,0.012039458,0.0000045856464,0.00017206903,0.00006669078,4.1731846e-7,0.0008265064,0.33667925],"genre_scores_gemma":[0.97554153,0.000038830785,0.022979612,0.000027916778,0.000435948,0.000002582326,0.0000030145216,0.00004302203,0.00092752633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991288,0.00001739449,0.0001711249,0.00014216741,0.00010101023,0.00043950632],"domain_scores_gemma":[0.9997265,0.000037210266,0.000019577614,0.00013578846,0.000011836714,0.000069107424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000086801956,0.00014599954,0.00015523072,0.000049578695,0.00009655528,0.000037137917,0.00009328919,0.00006497575,0.00020004182],"category_scores_gemma":[0.000007019,0.00013732292,0.00005138428,0.0004060419,0.0000096743815,0.00011612665,0.000020514251,0.00014593279,0.00002742883],"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":[8.899729e-7,0.0000088242705,0.00032133926,0.000014612831,0.000016216833,0.000003551985,0.00009237607,0.9899169,0.005021141,0.00021083657,0.0015454246,0.0028478862],"study_design_scores_gemma":[0.000085593674,0.000010660936,0.00007834689,0.000011256008,0.00000972421,0.000029365781,0.000004081733,0.9987002,0.0005236785,0.000019014733,0.0003542252,0.00017382251],"about_ca_topic_score_codex":0.000057872083,"about_ca_topic_score_gemma":0.0000018005936,"teacher_disagreement_score":0.3357517,"about_ca_system_score_codex":0.000048060116,"about_ca_system_score_gemma":0.0000021647986,"threshold_uncertainty_score":0.55998665},"labels":[],"label_agreement":null},{"id":"W173431335","doi":"10.1007/978-3-319-13572-4_2","title":"Optimization of Wind Direction Distribution Parameters Using Particle Swarm Optimization","year":2014,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Particle swarm optimization; Wind power; Computer science; Wind speed; Wind direction; Mathematical optimization; von Mises distribution; Set (abstract data type); Algorithm; von Mises yield criterion; Mathematics; Meteorology; Finite element method; Engineering; Physics; Structural engineering","score_opus":0.015801263622088446,"score_gpt":0.22808103728382717,"score_spread":0.21227977366173872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W173431335","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":[0.0056232074,0.008003251,0.97798634,8.6129387e-7,0.0012398487,0.00017455914,0.000009547935,0.00008472569,0.0068776454],"genre_scores_gemma":[0.99062693,0.0027122914,0.0057226704,0.0000026177906,0.00018066338,0.0000021390265,0.000108657725,0.00005859589,0.00058544026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988517,0.00002279577,0.00060346787,0.00021486363,0.00013308431,0.00017405568],"domain_scores_gemma":[0.99943525,0.00010034009,0.00025315108,0.0001153704,0.000056036803,0.000039869446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020720843,0.0002181727,0.00034179087,0.000081520426,0.00006175289,0.000036083697,0.00005873356,0.00014893412,0.000003395519],"category_scores_gemma":[0.000018326844,0.00023520729,0.00004785517,0.00006732398,0.0000382568,0.00012627797,0.00002656568,0.00014545415,5.8118775e-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":[0.0000041222715,0.0000031882792,0.00012949176,0.0002953792,0.000019269437,8.0445443e-7,0.00008471551,0.98404014,0.000010535514,0.004501831,0.0000018771093,0.010908639],"study_design_scores_gemma":[0.00008048767,0.000028624097,0.0000022119377,0.0017446481,0.000024361001,0.000009777668,0.0000354341,0.9952574,0.00032974072,0.00006089767,0.002207666,0.00021878512],"about_ca_topic_score_codex":0.000033664466,"about_ca_topic_score_gemma":0.000004525613,"teacher_disagreement_score":0.9850037,"about_ca_system_score_codex":0.00011610083,"about_ca_system_score_gemma":0.0000062314766,"threshold_uncertainty_score":0.9591475},"labels":[],"label_agreement":null},{"id":"W1908791208","doi":"10.1109/ccece.1996.548207","title":"Application of least absolute value parameter estimation technique based on linear programming to short-term load forecasting","year":2002,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Technical University of Nova Scotia","funders":"","keywords":"Overdetermined system; Linear regression; Term (time); Linear programming; Least-squares function approximation; Estimation theory; Computer science; Linear model; Linear least squares; Mathematical optimization; Mathematics; Applied mathematics; Algorithm; Statistics","score_opus":0.02516263296769045,"score_gpt":0.23901054365441868,"score_spread":0.21384791068672823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1908791208","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":[0.03148179,0.000016384776,0.95919853,0.0000321088,0.0000714442,0.00049529626,0.0000036693505,0.00046255792,0.008238201],"genre_scores_gemma":[0.7520377,8.9533546e-7,0.24762212,0.0000293558,0.000049932973,0.00017433907,0.000011743678,0.00003492388,0.000039007526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895984,0.000012745181,0.000334838,0.00020781552,0.00022075234,0.00026403318],"domain_scores_gemma":[0.99946475,0.000095638025,0.00004035997,0.0002587811,0.000056685596,0.00008377459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019442146,0.00018051016,0.00017100749,0.00013811057,0.0000506022,0.000023808834,0.00012100866,0.00009801904,0.000030257426],"category_scores_gemma":[0.000068031026,0.00017694043,0.00006628754,0.0003030107,0.00001610555,0.00009846921,0.000018035962,0.00013464765,0.00002277164],"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":[0.000005208681,0.00003415626,0.00026370984,0.000091708265,0.000008218671,0.0000012596759,0.00009668663,0.7133886,0.0051526977,0.00019119286,0.00004596451,0.2807206],"study_design_scores_gemma":[0.00009156333,0.00009493809,0.00010093221,0.00016168863,0.000010572545,0.0000049571186,0.0000048901834,0.9645308,0.033844996,0.000020188503,0.0009483711,0.00018605776],"about_ca_topic_score_codex":0.000023022554,"about_ca_topic_score_gemma":0.000010660877,"teacher_disagreement_score":0.7205559,"about_ca_system_score_codex":0.00008292322,"about_ca_system_score_gemma":0.000007025989,"threshold_uncertainty_score":0.7215421},"labels":[],"label_agreement":null},{"id":"W1948643200","doi":"10.1109/icmla.2015.164","title":"Predicting Energy Demand Peak Using M5 Model Trees","year":2015,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Decision tree; Tree (set theory); Computer science; Feature selection; Artificial neural network; Predictive modelling; Mean absolute percentage error; Feature (linguistics); Regression analysis; Energy (signal processing); Linear regression; Regression; Machine learning; Artificial intelligence; Data mining; Statistics; Mathematics","score_opus":0.03596141385705448,"score_gpt":0.22183197474539568,"score_spread":0.1858705608883412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1948643200","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":[0.6046852,0.00047723038,0.24912351,0.000007921079,0.00034895304,0.000018109486,0.0000024971923,0.0006119262,0.14472464],"genre_scores_gemma":[0.9908042,0.000011399159,0.00804693,0.000032403488,0.00021257842,0.0000022249972,0.0000044814255,0.00003601472,0.0008497641],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993485,0.000008735398,0.00015988591,0.0001135478,0.0001256343,0.00024366357],"domain_scores_gemma":[0.99967957,0.00001923091,0.000016152997,0.0001229623,0.000027384418,0.00013467624],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103137994,0.00012629703,0.000116591415,0.00005879325,0.00004661073,0.00003353729,0.00008688265,0.0000635944,0.000013966774],"category_scores_gemma":[0.000017323235,0.00011664023,0.00003540433,0.0000961559,0.000012051593,0.00017833804,0.000033155582,0.000068211586,0.000003443081],"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":[0.0000017250009,0.0000045040542,0.0011084038,0.0000060742905,0.000015046731,0.0000028060015,0.00025470363,0.9939549,0.0017015161,0.0013686237,0.00043523792,0.0011464419],"study_design_scores_gemma":[0.00017568101,0.000009413964,0.000017668099,0.000025645646,0.0000104757055,0.00001332446,0.00007819734,0.99407125,0.0039826236,0.00041895482,0.0010498278,0.00014691296],"about_ca_topic_score_codex":0.00015106685,"about_ca_topic_score_gemma":0.00022210386,"teacher_disagreement_score":0.386119,"about_ca_system_score_codex":0.000043629665,"about_ca_system_score_gemma":0.000021114573,"threshold_uncertainty_score":0.47564507},"labels":[],"label_agreement":null},{"id":"W1955850335","doi":"10.1063/1.4919021","title":"Meteorological phenomena associated with wind-power ramps downwind of mountainous terrain","year":2015,"lang":"en","type":"article","venue":"Journal of Renewable and Sustainable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Queen's University; University of Calgary","funders":"Alberta Innovates - Technology Futures","keywords":"Wind power; Environmental science; Maximum sustained wind; Wind speed; Meteorology; Atmospheric instability; Wind profile power law; Atmospheric sciences; Terrain; Wind direction; Wind gradient; Geology; Geography; Engineering","score_opus":0.008973525174484066,"score_gpt":0.1936653914403713,"score_spread":0.1846918662658872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1955850335","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":[0.96225935,0.0043710857,0.0050532673,0.000053504213,0.00018117404,0.000045944813,0.0000027032745,0.00004821549,0.027984729],"genre_scores_gemma":[0.9969806,0.00016998028,0.00035879796,0.000051762785,0.00012353355,0.0000015397134,0.000004221382,0.000030945583,0.002278605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99849814,0.00006981947,0.00046507063,0.00012057345,0.00032800136,0.0005184144],"domain_scores_gemma":[0.9988573,0.00011035961,0.00023890642,0.00012906065,0.00040712903,0.00025724998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082502904,0.00021121746,0.0005003666,0.00023415934,0.000069216476,0.000049653405,0.00016618807,0.00014644272,0.000024006238],"category_scores_gemma":[0.00016435656,0.00015279914,0.0000741523,0.00033914304,0.00008004465,0.00031815705,0.000050419254,0.00017926987,1.3158848e-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":[0.00036600573,0.00013498975,0.0015788833,0.00007601224,0.00050869456,0.000676031,0.0013649985,0.9856154,0.0016451011,0.0033012491,0.0027813567,0.0019512995],"study_design_scores_gemma":[0.027878456,0.024959192,0.0023984255,0.0015101644,0.0011109068,0.0030831525,0.08390482,0.049749814,0.052663703,0.033087164,0.7155148,0.0041394243],"about_ca_topic_score_codex":0.00035757484,"about_ca_topic_score_gemma":0.00006371147,"teacher_disagreement_score":0.9358656,"about_ca_system_score_codex":0.00017014694,"about_ca_system_score_gemma":0.00016052074,"threshold_uncertainty_score":0.6230968},"labels":[],"label_agreement":null},{"id":"W1971421510","doi":"10.1109/epec.2014.13","title":"Aggregate Load Forecast with Payback Model of the Electric Water Heaters for a Direct Load Control Program","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":16,"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 New Brunswick","funders":"","keywords":"Load profile; Electrical load; Aggregate (composite); Controller (irrigation); Automotive engineering; Payback period; Computer science; Kalman filter; Simulation; Environmental science; Engineering; Electricity; Voltage; Electrical engineering; Artificial intelligence","score_opus":0.007460609762723566,"score_gpt":0.18320200048467034,"score_spread":0.17574139072194678,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971421510","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":[0.8449815,0.00017279385,0.099831775,0.00019953077,0.00026931355,0.0013528016,0.000019041237,0.00068862346,0.052484654],"genre_scores_gemma":[0.99644935,0.0000057059337,0.0023135596,0.000076747565,0.000052018186,0.0001608328,0.0000032899648,0.000050689934,0.000887779],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998942,0.000017380233,0.00021639733,0.000171229,0.00019513274,0.00045786408],"domain_scores_gemma":[0.9995064,0.000051587325,0.000034136792,0.00025061014,0.00009718533,0.000060073256],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021880926,0.00020340816,0.00025709212,0.000032789296,0.00006376509,0.000029866245,0.00017153024,0.000064718195,0.00000820771],"category_scores_gemma":[0.000015497562,0.0000983123,0.00012091096,0.00010944038,0.00003333074,0.00007919451,0.0000151489885,0.00009235262,0.0000034903162],"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":[0.00018851078,0.000059431586,0.0006629914,0.0002166084,0.00021869877,4.5183316e-7,0.00049792364,0.9156258,0.03571809,0.0004548227,0.0006681805,0.04568846],"study_design_scores_gemma":[0.0010534133,0.00019327879,0.00001558101,0.000056157718,0.000044953566,0.0000031700765,0.0000040779646,0.84644943,0.14856379,0.0001240206,0.0033197487,0.00017239612],"about_ca_topic_score_codex":0.000034556215,"about_ca_topic_score_gemma":0.00012795224,"teacher_disagreement_score":0.1514679,"about_ca_system_score_codex":0.000060992606,"about_ca_system_score_gemma":0.000029525696,"threshold_uncertainty_score":0.40090594},"labels":[],"label_agreement":null},{"id":"W1971711751","doi":"10.1109/smartgridcomm.2013.6687983","title":"A Maximum-Entropy based fast estimation of power quality for smart microgrid","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Victoria","funders":"","keywords":"Microgrid; Computer science; Reliability (semiconductor); Maximization; Principle of maximum entropy; Entropy (arrow of time); Monte Carlo method; Reliability engineering; Power quality; Power (physics); Mathematical optimization; Artificial intelligence; Mathematics; Statistics; Engineering","score_opus":0.012609596071521708,"score_gpt":0.2329206501352504,"score_spread":0.2203110540637287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971711751","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":[0.56667507,0.00006129029,0.42440686,0.00008465997,0.00031488133,0.00024323793,0.00002094279,0.00023314048,0.0079599],"genre_scores_gemma":[0.94821215,0.0000011743045,0.051498674,0.00005020814,0.000020209733,0.0000344358,0.00003179686,0.00001818975,0.00013314256],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943495,0.00000947584,0.0002348686,0.000085435306,0.00007142087,0.00016385851],"domain_scores_gemma":[0.99965936,0.000084127205,0.00003230023,0.00012992535,0.000051056326,0.00004323617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010227545,0.00009340741,0.00012929639,0.000043371645,0.000022635677,0.00002052348,0.00006346228,0.000047198562,0.000442371],"category_scores_gemma":[0.000029214292,0.00008371151,0.0000689554,0.00006495702,0.000014594613,0.00010668143,0.000007135881,0.0000395248,0.000028130993],"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":[0.000060294828,0.00020995118,0.005742954,0.0011361187,0.00019184129,9.736764e-7,0.0007538462,0.5190792,0.27558538,0.013824473,0.02155726,0.1618577],"study_design_scores_gemma":[0.0007409138,0.00007167586,0.0035276536,0.000051110106,0.000011747457,0.0000010827517,0.000062789004,0.85125256,0.13848458,0.00092271756,0.0046283323,0.00024481685],"about_ca_topic_score_codex":0.00009985347,"about_ca_topic_score_gemma":0.0000110022975,"teacher_disagreement_score":0.38153708,"about_ca_system_score_codex":0.000017125049,"about_ca_system_score_gemma":0.000007779874,"threshold_uncertainty_score":0.48436522},"labels":[],"label_agreement":null},{"id":"W1971889265","doi":"10.1049/iet-gtd.2013.0610","title":"Mid‐term electricity market clearing price forecasting using multiple least squares support vector machines","year":2014,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":20,"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 Saskatchewan","funders":"","keywords":"Clearing; Support vector machine; Term (time); Least squares support vector machine; Electricity market; Electricity; Market clearing; Computer science; Least-squares function approximation; Econometrics; Mathematical optimization; Machine learning; Economics; Engineering; Mathematics; Microeconomics; Statistics; Finance; Electrical engineering","score_opus":0.022682377238834036,"score_gpt":0.22163769207507775,"score_spread":0.1989553148362437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971889265","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":[0.44114998,0.00010144019,0.5570729,0.000028935227,0.00042507533,0.00012711095,0.000055816283,0.0003043282,0.00073441886],"genre_scores_gemma":[0.9954977,0.000036895442,0.0025163,0.00002330109,0.0006505421,0.000015327638,0.0011221875,0.000055963716,0.000081764825],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99823993,0.00011079877,0.0005015682,0.0003532641,0.0003040405,0.0004903817],"domain_scores_gemma":[0.9993288,0.00008369424,0.00009901911,0.00019678082,0.000101838166,0.00018989171],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043197238,0.0003179818,0.00025394457,0.0000909795,0.00045196043,0.00014707808,0.00014088035,0.00018484564,0.0002256434],"category_scores_gemma":[0.0001018232,0.00031909707,0.00013133275,0.00034321882,0.00002449767,0.0003743435,0.000016570215,0.00026014194,0.0000073283936],"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":[0.000074346826,0.000072963885,0.0043366444,0.0002411941,0.000045911347,0.000005548336,0.00028009104,0.24734849,0.555671,0.00015072455,0.002212692,0.18956044],"study_design_scores_gemma":[0.0004979117,0.00005842421,0.002376146,0.00008279988,0.000031687832,0.000027405516,0.0000066156535,0.8839078,0.10395995,0.000020464673,0.008694057,0.00033674884],"about_ca_topic_score_codex":0.000042035354,"about_ca_topic_score_gemma":0.000025571106,"teacher_disagreement_score":0.6365593,"about_ca_system_score_codex":0.00017429747,"about_ca_system_score_gemma":0.000033574237,"threshold_uncertainty_score":0.9999261},"labels":[],"label_agreement":null},{"id":"W1974088618","doi":"10.1300/j073v18n02_03","title":"The Big American Blackout of 2003","year":2005,"lang":"en","type":"article","venue":"Journal of Travel & Tourism Marketing","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"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":"Blackout; Thursday; Tourism; Government (linguistics); Geography; Power (physics); Electric power system; Archaeology","score_opus":0.007177459715677506,"score_gpt":0.20517011818320932,"score_spread":0.1979926584675318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974088618","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":[0.9578929,0.0035001365,0.0037458742,0.00047713463,0.0012668563,0.000057165995,0.0000035275689,0.00003633612,0.033020075],"genre_scores_gemma":[0.9906361,0.0006739802,0.0067488807,0.000036767626,0.0013860863,6.7120595e-7,2.6350207e-7,0.0000333749,0.00048384734],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99861604,0.00009348577,0.00066486973,0.00006415376,0.0002726146,0.00028881332],"domain_scores_gemma":[0.9987994,0.00041546562,0.00039801156,0.00013155174,0.0001617916,0.00009378764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016762305,0.00013789564,0.00027034048,0.00010275821,0.00009137904,0.00003712872,0.00026114035,0.000040466806,0.000020438354],"category_scores_gemma":[0.0003911641,0.00009893309,0.00013161318,0.0002944567,0.00007831776,0.000088559435,0.000019979621,0.00036537508,0.0000022645156],"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":[0.00012011473,0.000032445394,0.0022732373,0.00006125942,0.00021342156,0.000037535185,0.0006907799,0.02585253,0.008766864,0.00006672846,0.0160932,0.9457919],"study_design_scores_gemma":[0.0033448779,0.0006302228,0.17520498,0.0023370518,0.0003650019,0.0011239328,0.004764112,0.11037165,0.06476446,0.00027087884,0.63520914,0.0016137152],"about_ca_topic_score_codex":0.000011590355,"about_ca_topic_score_gemma":0.000019732352,"teacher_disagreement_score":0.94417816,"about_ca_system_score_codex":0.000045577184,"about_ca_system_score_gemma":0.000044891745,"threshold_uncertainty_score":0.4034374},"labels":[],"label_agreement":null},{"id":"W1975392736","doi":"10.1109/pesmg.2013.6672338","title":"Computation of dynamic operating balancing reserve for wind power integration for the time-horizon 1&amp;#x2013;48 hours","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Hydro-Québec","funders":"","keywords":"Unavailability; Time horizon; Wind power; Electric power system; Computer science; Computation; Reliability (semiconductor); Reliability engineering; Power (physics); Mathematical optimization; Engineering; Mathematics","score_opus":0.011014963319634509,"score_gpt":0.237434444075338,"score_spread":0.2264194807557035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1975392736","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":[0.37896344,0.00011555061,0.6180838,0.0001338128,0.00040100704,0.00054310047,0.000011490897,0.00011519504,0.0016326528],"genre_scores_gemma":[0.9586614,0.0000041203552,0.04058121,0.000029005438,0.0000705387,0.0000668502,0.000076376215,0.00003825247,0.0004722656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926317,0.000014169173,0.00028340478,0.00012199858,0.000094672105,0.00022257102],"domain_scores_gemma":[0.9992739,0.00034769275,0.00005447726,0.00012155916,0.00016131872,0.00004107306],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002941309,0.00012901436,0.00014622728,0.00005470669,0.0001140697,0.000069448484,0.000109598215,0.00006904733,0.00006290548],"category_scores_gemma":[0.00017376909,0.00009163269,0.0000757699,0.00009898404,0.000016698748,0.00023875757,0.00001562054,0.00008086398,0.000015762498],"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":[0.000019664825,0.000025360378,0.00024846927,0.00017428171,0.00013391626,6.76029e-8,0.0017199948,0.6770214,0.2380821,0.0013508096,0.01128444,0.06993953],"study_design_scores_gemma":[0.00028118686,0.000084618856,0.0003039485,0.000109449014,0.000015638412,0.0000012546549,0.0003144478,0.99250174,0.005515498,0.00031296915,0.00042419223,0.00013504375],"about_ca_topic_score_codex":0.00011242215,"about_ca_topic_score_gemma":0.00014933989,"teacher_disagreement_score":0.57969797,"about_ca_system_score_codex":0.00004773225,"about_ca_system_score_gemma":0.00001593419,"threshold_uncertainty_score":0.37366727},"labels":[],"label_agreement":null},{"id":"W1977362693","doi":"10.1111/j.1468-0394.2004.00272.x","title":"Short‐term electric power load forecasting using feedforward neural networks","year":2004,"lang":"en","type":"article","venue":"Expert Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":38,"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":"University of Toronto; University of Engineering and Technology, Lahore; National Technical University of Athens; University of Houston; American Society for Engineering Education","keywords":"Computer science; Artificial neural network; Feed forward; Feedforward neural network; Electrical load; Electric power system; Term (time); Electric power; Power (physics); Artificial intelligence; Machine learning; Control engineering; Engineering","score_opus":0.02434402890887483,"score_gpt":0.230294832109868,"score_spread":0.20595080320099318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977362693","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":[0.9138497,0.011694203,0.06314839,0.000008379448,0.0046668793,0.00023725172,0.000002213882,0.00076240755,0.0056305374],"genre_scores_gemma":[0.9982608,0.00003335706,0.0003225371,0.000032132113,0.0011572526,0.000025764211,0.0000070863525,0.00011301772,0.000048054695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981503,0.000027216913,0.00047025984,0.0002881513,0.00030278758,0.00076132384],"domain_scores_gemma":[0.9993888,0.000042479336,0.000051894363,0.00028597531,0.000058783236,0.00017204501],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001874225,0.0003462036,0.0003574025,0.000122853,0.00018775462,0.0001565196,0.00021799936,0.0001841237,0.000011239365],"category_scores_gemma":[0.00002039095,0.00033300172,0.00013452007,0.0003843708,0.000018708533,0.00027078422,0.00003967124,0.00028629182,0.000009041842],"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":[0.0000058768574,0.000010110444,0.00060446997,0.00003096837,0.00004675708,0.00005888224,0.00076299487,0.9908703,0.0053216275,0.00008768617,0.00015131588,0.0020490391],"study_design_scores_gemma":[0.00031890837,0.000043095817,0.00009785819,0.00025663685,0.000010199744,0.00038067196,0.0001251592,0.99613976,0.0010312882,0.0000054036395,0.0011370676,0.0004539395],"about_ca_topic_score_codex":0.00023317026,"about_ca_topic_score_gemma":0.000014007172,"teacher_disagreement_score":0.084411055,"about_ca_system_score_codex":0.00046493526,"about_ca_system_score_gemma":0.000027474383,"threshold_uncertainty_score":0.9999122},"labels":[],"label_agreement":null},{"id":"W1977877725","doi":"10.1109/icmla.2013.130","title":"A Neural Network Approach to Multi-step-ahead, Short-Term Wind Speed Forecasting","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Wind speed; Artificial neural network; Term (time); Computer science; Set (abstract data type); Range (aeronautics); Wind direction; Data set; Meteorology; Machine learning; Artificial intelligence; Engineering; Geography","score_opus":0.044547425062353894,"score_gpt":0.22901533083702214,"score_spread":0.18446790577466823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977877725","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":[0.8627501,0.00013600076,0.036336105,0.00002648904,0.0011128164,0.00048972014,0.00000272889,0.00081689877,0.098329134],"genre_scores_gemma":[0.9468675,0.000002008682,0.050608847,0.00013633945,0.0006880738,0.000014798042,0.000019331972,0.000081638755,0.0015814486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984414,0.00001879105,0.0003366946,0.00029216817,0.00015787405,0.00075308373],"domain_scores_gemma":[0.9993389,0.00005778557,0.000018703562,0.00026787326,0.00004032667,0.00027641392],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012183974,0.00029348643,0.00026219612,0.00007792268,0.00011218976,0.00013650645,0.00023129488,0.00010963462,0.00018268648],"category_scores_gemma":[0.000020119078,0.00026364243,0.00009226077,0.00031044023,0.00001884426,0.00025322553,0.00008961902,0.00023578614,0.00012498342],"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":[0.0000032158534,0.000026270565,0.008051066,0.000049882656,0.00003894519,0.000005439291,0.0003951544,0.9562007,0.0012810897,0.00009518525,0.0067564473,0.027096605],"study_design_scores_gemma":[0.00020483517,0.000027775804,0.0036747928,0.00004301853,0.00001032912,0.00003673184,0.000101493744,0.9940541,0.00022453832,0.000008380485,0.0012429795,0.00037103897],"about_ca_topic_score_codex":0.000101815545,"about_ca_topic_score_gemma":0.000042009036,"teacher_disagreement_score":0.09674768,"about_ca_system_score_codex":0.00003890954,"about_ca_system_score_gemma":0.0000058321884,"threshold_uncertainty_score":0.9999816},"labels":[],"label_agreement":null},{"id":"W1979034248","doi":"10.1109/tec.2009.2015973","title":"Annual Wind Speed Estimation Utilizing Constrained Grey Predictor","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Energy Conversion","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":27,"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":"Wind speed; Estimation; Wind power; Meteorology; Computer science; Environmental science; Control theory (sociology); Engineering; Artificial intelligence; Geography; Electrical engineering; Control (management)","score_opus":0.00977078168490298,"score_gpt":0.2038129884050549,"score_spread":0.19404220672015193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1979034248","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":[0.2413783,0.00008356795,0.7437338,0.00013964613,0.0034983396,0.00011180619,0.00014430564,0.0013262454,0.0095839845],"genre_scores_gemma":[0.9985141,0.000077931254,0.0007230988,0.00014024449,0.00008386343,9.608659e-7,0.000023842993,0.000027472093,0.00040844933],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990106,0.000025257514,0.00024169103,0.00022274577,0.00021120065,0.00028849998],"domain_scores_gemma":[0.99953556,0.000058844173,0.00003526327,0.00018520324,0.00004864882,0.00013647553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007037622,0.0002260996,0.00017465252,0.00021252628,0.00017024676,0.000025114128,0.00011983314,0.00015625329,0.00017698073],"category_scores_gemma":[0.0000034094019,0.00024304652,0.000104679864,0.0002444073,0.000043215437,0.0003635073,6.108066e-7,0.00019687146,0.000029033901],"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":[0.000046692545,0.000054390774,0.0000032476676,0.000016252849,0.000039785507,0.000013574208,0.00027301034,0.9311352,0.01035176,0.00031395134,0.00055337953,0.057198785],"study_design_scores_gemma":[0.0010918583,0.00025183338,0.00008792322,0.00014233915,0.000053984546,0.000031698106,0.00016518313,0.84041923,0.15390778,0.00016121106,0.0032975366,0.00038944036],"about_ca_topic_score_codex":0.00002725928,"about_ca_topic_score_gemma":0.000007029788,"teacher_disagreement_score":0.7571358,"about_ca_system_score_codex":0.00009247992,"about_ca_system_score_gemma":0.000024603927,"threshold_uncertainty_score":0.9911149},"labels":[],"label_agreement":null},{"id":"W1983080429","doi":"10.4018/jghpc.2011010103","title":"Performance Analysis of Sequential and Parallel Neural Network Algorithm for Stock Price Forecasting","year":2011,"lang":"en","type":"article","venue":"International Journal of Grid and High Performance Computing","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Artificial neural network; Computer science; Backpropagation; Stock market; Process (computing); Artificial intelligence; Stock market prediction; Machine learning; Algorithm","score_opus":0.026713443804112447,"score_gpt":0.2302361378127717,"score_spread":0.20352269400865924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983080429","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":[0.9466463,0.00034717625,0.051007368,0.0000064119504,0.0017232469,0.00004293278,0.000010799474,0.000016012244,0.00019980417],"genre_scores_gemma":[0.95626813,0.00026933933,0.042146,0.000015921398,0.0012719348,9.724306e-7,0.000008676373,0.000013901471,0.0000051060524],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895155,0.000010496498,0.00053281675,0.00010046015,0.00019463766,0.00021005663],"domain_scores_gemma":[0.9992519,0.00008848296,0.00030669727,0.00004897053,0.00024096992,0.00006295248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034072858,0.00013455555,0.00029529544,0.00025367472,0.00009918409,0.000033719007,0.0001830095,0.000045178702,0.0000082135075],"category_scores_gemma":[0.00001065208,0.00012275732,0.00008875637,0.00017765097,0.00004419635,0.00031515615,0.00006292341,0.00016117237,8.604773e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006647779,0.00001573081,0.060852077,0.00006791387,0.00093126827,0.000006382569,0.0007858023,0.6212079,0.00008332005,0.000065160864,0.00004095998,0.31587702],"study_design_scores_gemma":[0.0004645997,0.00015006197,0.06683127,0.00016975107,0.00015240887,0.00011061349,0.00002742266,0.9315575,0.0002784124,0.000011657995,0.00012811173,0.000118223295],"about_ca_topic_score_codex":0.000009719993,"about_ca_topic_score_gemma":0.000002558977,"teacher_disagreement_score":0.3157588,"about_ca_system_score_codex":0.000023902308,"about_ca_system_score_gemma":0.000013452857,"threshold_uncertainty_score":0.50058985},"labels":[],"label_agreement":null},{"id":"W1983664186","doi":"10.1016/j.asoc.2011.07.001","title":"Short-term load forecasting using bayesian neural networks learned by Hybrid Monte Carlo algorithm","year":2011,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":120,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial neural network; Algorithm; Overfitting; Computer science; Mean squared error; Monte Carlo method; Bayesian probability; Feedforward neural network; Support vector machine; Artificial intelligence; Machine learning; Mathematics; Statistics","score_opus":0.029069031088898214,"score_gpt":0.21430568652268484,"score_spread":0.18523665543378662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1983664186","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":[0.41899547,0.00048155003,0.5743665,0.0000016047388,0.00068907125,0.00015928024,0.0000054461448,0.000827461,0.0044736345],"genre_scores_gemma":[0.9671782,0.00000664824,0.032028817,0.000053759704,0.00052233564,0.0000068255395,0.000019732348,0.00016664092,0.000017010714],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99753016,0.000029169882,0.000601357,0.000511258,0.00028206833,0.0010459645],"domain_scores_gemma":[0.9991537,0.00012920833,0.000108741566,0.00032601328,0.000053521566,0.00022883526],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003366958,0.0004946104,0.00045089278,0.00008954418,0.00039047425,0.00011265339,0.0003575767,0.0001546243,0.000018040651],"category_scores_gemma":[0.000014427216,0.00056052866,0.00013583148,0.00027425686,0.00006466285,0.00015747974,0.00018683517,0.00063171884,0.0000041151707],"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":[0.0000072263506,0.000015124927,0.00089621946,0.000031514937,0.00006280101,0.00004441153,0.0007514546,0.5848009,0.000647021,0.000021127795,0.0000923417,0.41262984],"study_design_scores_gemma":[0.000291139,0.000020005606,0.00008848197,0.00008597563,0.000039851177,0.00009530009,0.00010091867,0.9971863,0.0013456066,0.00004555979,0.00010343342,0.0005974538],"about_ca_topic_score_codex":0.00010096865,"about_ca_topic_score_gemma":0.000009403532,"teacher_disagreement_score":0.5481828,"about_ca_system_score_codex":0.0001504661,"about_ca_system_score_gemma":0.000018223724,"threshold_uncertainty_score":0.99968463},"labels":[],"label_agreement":null},{"id":"W1984016160","doi":"10.1109/ifsa-nafips.2013.6608579","title":"Predicting solar power output using complex fuzzy logic","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":27,"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":"Photovoltaic system; Computer science; Renewable energy; Fuzzy logic; Adaptive neuro fuzzy inference system; Electricity generation; Power (physics); Solar power; Grid-connected photovoltaic power system; Maximum power point tracking; Electric power system; Grid; Function (biology); Control engineering; Artificial intelligence; Fuzzy control system; Engineering; Electrical engineering; Inverter; Mathematics","score_opus":0.0359102840150187,"score_gpt":0.22507290777261835,"score_spread":0.18916262375759965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984016160","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":[0.7369757,0.00010864953,0.015062566,0.000025300795,0.00050835934,0.00009235948,0.0000023566533,0.00078079785,0.2464439],"genre_scores_gemma":[0.9877509,0.0000031874185,0.0114694135,0.00014344988,0.00013771778,0.000004582086,0.000005945828,0.000037969505,0.00044686184],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999183,0.00001111337,0.0002069522,0.00013548636,0.00011305195,0.0003504102],"domain_scores_gemma":[0.99966186,0.000032540393,0.000021697402,0.00015733507,0.000034516605,0.00009202342],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000071964656,0.00015513664,0.00014169555,0.000060604598,0.00009208294,0.00007484762,0.00011556472,0.00007420943,0.0010069788],"category_scores_gemma":[0.000017061318,0.0001373776,0.00005308251,0.000109510045,0.000020103782,0.0002288243,0.00004422031,0.0001407111,0.00020238494],"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":[0.00000513602,0.00007825553,0.06924087,0.00021270354,0.00028855982,0.00003549132,0.0022775296,0.7554727,0.12841582,0.008628716,0.015418441,0.019925734],"study_design_scores_gemma":[0.00025546152,0.00003330045,0.007487189,0.000054844535,0.000013856143,0.00003824752,0.0002704469,0.98312885,0.0020073648,0.00079033745,0.0054903743,0.00042971838],"about_ca_topic_score_codex":0.00015210058,"about_ca_topic_score_gemma":0.000009866254,"teacher_disagreement_score":0.25077516,"about_ca_system_score_codex":0.000034409182,"about_ca_system_score_gemma":0.0000057515044,"threshold_uncertainty_score":0.99990624},"labels":[],"label_agreement":null},{"id":"W1987941067","doi":"10.1155/2015/384528","title":"Midterm Electricity Market Clearing Price Forecasting Using Two-Stage Multiple Support Vector Machine","year":2015,"lang":"en","type":"article","venue":"Journal of Energy","topic":"Energy Load and Power Forecasting","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":"University of Saskatchewan","funders":"","keywords":"Support vector machine; Electricity market; Electricity; Electricity price forecasting; Computer science; Scheduling (production processes); Market clearing; Clearing; Operations research; Artificial intelligence; Engineering; Economics; Operations management; Finance; Microeconomics","score_opus":0.03809069388526573,"score_gpt":0.23858027206298857,"score_spread":0.20048957817772284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987941067","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":[0.9314445,0.00082721625,0.039429203,0.00001444074,0.0015565802,0.000033954213,0.000008124186,0.00011039361,0.02657559],"genre_scores_gemma":[0.99103343,0.000034418623,0.007537967,0.000048183723,0.00080008624,8.3778673e-7,0.000003664675,0.00007099848,0.00047038594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983551,0.000056785873,0.0006314494,0.00013382627,0.0003538923,0.0004689492],"domain_scores_gemma":[0.9989186,0.00014761361,0.0002998986,0.00015275671,0.00017121709,0.00030994753],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066131435,0.00023630989,0.0003656407,0.00026276405,0.000078964295,0.000070561655,0.00024922163,0.00008753637,0.00010606158],"category_scores_gemma":[0.00025898262,0.00022070447,0.00015311032,0.00032537,0.000018794204,0.0003989965,0.0000600553,0.00036149903,0.0000010897303],"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":[0.000212746,0.00007753983,0.015827442,0.000087452034,0.00024784933,0.0006041064,0.00039743044,0.94318885,0.02505532,0.00028418645,0.0019353583,0.012081709],"study_design_scores_gemma":[0.0014003686,0.00018472855,0.0004987402,0.00012948686,0.000044197375,0.00086245826,0.000038750004,0.9509843,0.018092202,0.000056085817,0.027361553,0.00034714554],"about_ca_topic_score_codex":0.0001495061,"about_ca_topic_score_gemma":0.00008919591,"teacher_disagreement_score":0.059588958,"about_ca_system_score_codex":0.00025177473,"about_ca_system_score_gemma":0.000093530434,"threshold_uncertainty_score":0.9000067},"labels":[],"label_agreement":null},{"id":"W1989876608","doi":"10.1109/icmla.2013.124","title":"Price Forecasting in the Spanish Day-Ahead Electricity Market Using Preconditioned Wind Power Information","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Calgary","funders":"","keywords":"Wind power; Electricity market; Renewable energy; Electricity; Electricity price forecasting; Residual; Electricity generation; Stand-alone power system; Environmental economics; Economics; Electricity retailing; Econometrics; Computer science; Power (physics); Distributed generation; Engineering; Electrical engineering","score_opus":0.012489600508060436,"score_gpt":0.19135512741984342,"score_spread":0.17886552691178298,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989876608","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":[0.7815932,0.000028448445,0.010754515,0.000031415206,0.0001930689,0.00025390496,0.00000324478,0.00014207506,0.20700015],"genre_scores_gemma":[0.9979058,0.000004181248,0.0017027586,0.00019315392,0.000059455364,0.000015497655,0.00001768266,0.000015098642,0.000086365195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990212,0.000045528435,0.00033016692,0.0000888101,0.00016269089,0.00035161644],"domain_scores_gemma":[0.9995358,0.00017518092,0.000054181797,0.00014779689,0.000046127596,0.0000409239],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040662437,0.0001485974,0.00011906397,0.00013830858,0.00010294305,0.00017168034,0.00015858801,0.00008190882,0.0009366711],"category_scores_gemma":[0.00008910056,0.00011638125,0.000038217342,0.00041828453,0.00001286636,0.0014769948,0.000020519798,0.00021868464,0.00004446579],"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":[0.00005178177,0.00026483607,0.044556428,0.0007184069,0.00031110639,0.00002918857,0.030747395,0.7129776,0.01282033,0.0069324435,0.07628269,0.11430779],"study_design_scores_gemma":[0.0003449281,0.000033325097,0.028823856,0.00007084174,0.000008685035,0.000054867745,0.00050246133,0.9642868,0.001276056,0.00051698525,0.003763542,0.00031763202],"about_ca_topic_score_codex":0.00016547885,"about_ca_topic_score_gemma":0.000039335944,"teacher_disagreement_score":0.25130922,"about_ca_system_score_codex":0.000075963864,"about_ca_system_score_gemma":0.000015157136,"threshold_uncertainty_score":0.99997663},"labels":[],"label_agreement":null},{"id":"W1990193139","doi":"10.1016/j.ijepes.2014.11.027","title":"Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network","year":2014,"lang":"en","type":"article","venue":"International Journal of Electrical Power & Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":136,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial neural network; Computer science; Wavelet transform; Fuzzy logic; Maxima and minima; Term (time); Artificial intelligence; Selection (genetic algorithm); Genetic algorithm; Neuro-fuzzy; Adaptive neuro fuzzy inference system; Wavelet; Algorithm; Pattern recognition (psychology); Machine learning; Fuzzy control system; Mathematics","score_opus":0.014345342472828974,"score_gpt":0.23103903098153136,"score_spread":0.21669368850870238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990193139","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":[0.7784115,0.0067227236,0.19503935,0.00015165716,0.009151607,0.000117947406,0.000013714703,0.00016532646,0.010226148],"genre_scores_gemma":[0.99701005,0.00019418956,0.00092627615,0.00011338619,0.001599209,0.0000034336056,0.000011849627,0.000051122228,0.000090467925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977028,0.00010010092,0.0008922359,0.00018819148,0.0006318436,0.00048485544],"domain_scores_gemma":[0.9989406,0.00012542306,0.00017527751,0.00011106586,0.00041736013,0.00023027872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049234804,0.00030351628,0.00048803707,0.00024161044,0.00006815914,0.00017508728,0.00039088604,0.00017161241,0.000014192378],"category_scores_gemma":[0.000057545356,0.00024506534,0.00019207757,0.00026664327,0.00004356883,0.0002840572,0.000026579857,0.00037729362,9.1630176e-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":[0.0003435298,0.00009289094,0.0022026694,0.000027366792,0.0010769917,0.0002775824,0.00022347184,0.8559902,0.013501776,0.02866794,0.0022809152,0.0953147],"study_design_scores_gemma":[0.0013498476,0.0003675646,0.00040182256,0.0003038958,0.000078380195,0.0026824863,0.000019529842,0.94592834,0.0011186718,0.0012036138,0.045997348,0.000548515],"about_ca_topic_score_codex":0.000094812174,"about_ca_topic_score_gemma":0.000023954306,"teacher_disagreement_score":0.21859854,"about_ca_system_score_codex":0.00034261405,"about_ca_system_score_gemma":0.000054731514,"threshold_uncertainty_score":0.99934745},"labels":[],"label_agreement":null},{"id":"W1992778383","doi":"10.1016/j.ijepes.2006.02.014","title":"An efficient approach for short term load forecasting using artificial neural networks","year":2006,"lang":"en","type":"article","venue":"International Journal of Electrical Power & Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":171,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec","funders":"","keywords":"Artificial neural network; Term (time); Computer science; Wind speed; Meteorology; Artificial intelligence; Geography","score_opus":0.02098137224467834,"score_gpt":0.24269702520674014,"score_spread":0.2217156529620618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992778383","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":[0.25017655,0.0010078975,0.7438281,0.000005724981,0.0035198133,0.00007040862,0.0000068078675,0.00006429055,0.0013204293],"genre_scores_gemma":[0.99394184,0.000004591263,0.001924546,0.00001843108,0.0039946805,0.000008357116,0.000027558553,0.000061020106,0.000018965795],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976123,0.000056054472,0.000989341,0.00020636868,0.0006729909,0.0004629681],"domain_scores_gemma":[0.9988153,0.00012935678,0.00023570328,0.000121659694,0.00055438536,0.00014358475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044855732,0.00025476553,0.00035940687,0.00028550794,0.00009093145,0.00022720061,0.00046263318,0.00015425077,0.0000038631597],"category_scores_gemma":[0.000038112903,0.00023020752,0.00024317508,0.00023173,0.000028447477,0.00021609323,0.000020433272,0.00027040928,1.5816369e-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":[0.000085901134,0.00012050941,0.00033464655,0.0000059974013,0.00011725784,0.000046079105,0.000022610544,0.9856944,0.004611629,0.0056448407,0.00014946633,0.0031666476],"study_design_scores_gemma":[0.00030146725,0.0001580403,0.00005199614,0.000052521136,0.000032395877,0.00070314854,0.000015711526,0.9968705,0.00090715504,0.00006788552,0.0005981193,0.00024102531],"about_ca_topic_score_codex":0.000096160395,"about_ca_topic_score_gemma":0.000007796207,"teacher_disagreement_score":0.7437653,"about_ca_system_score_codex":0.0004372913,"about_ca_system_score_gemma":0.000055702945,"threshold_uncertainty_score":0.938759},"labels":[],"label_agreement":null},{"id":"W1994716963","doi":"10.5383/ijtee.01.01.007","title":"Wind Data Collection and Analyses at Masdar City for Wind Turbine Assessment","year":2010,"lang":"en","type":"article","venue":"International Journal of Thermal and Environmental Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"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":"Masdar Institute of Science and Technology","keywords":"Turbine; Wind power; Marine engineering; Environmental science; Engineering; Automotive engineering; Meteorology; Aerospace engineering; Electrical engineering; Geography","score_opus":0.019742816325013565,"score_gpt":0.25348459021799324,"score_spread":0.23374177389297968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994716963","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":[0.9954682,0.00030029367,0.003081643,0.00004862257,0.00088890985,0.000033291526,0.000051904502,0.000012615136,0.00011454232],"genre_scores_gemma":[0.9950263,0.00016423968,0.0043504206,0.000011413393,0.00034383763,6.0909923e-7,0.000037554164,0.000016102047,0.000049520408],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994763,0.0000031774919,0.00019110831,0.00008707775,0.00014681635,0.00009548135],"domain_scores_gemma":[0.9997569,0.000047055437,0.000052431707,0.00006983328,0.000007542114,0.00006628866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012351762,0.00010304524,0.00011062781,0.00007662036,0.000039744275,0.000035345907,0.00015324233,0.000041778236,0.000054610584],"category_scores_gemma":[0.0000109676,0.000092552764,0.000033157834,0.00001871143,0.00002200913,0.00024917885,0.00010086166,0.00016152747,2.7410982e-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":[0.000036499754,0.000023893725,0.009004682,0.000017207054,0.00029622114,0.000013402913,0.00008895974,0.15481655,0.82677615,0.00002265074,0.00006876175,0.008835013],"study_design_scores_gemma":[0.0014344275,0.00011088617,0.0998086,0.00007463512,0.0000917544,0.00063836854,0.000052896827,0.8522559,0.030684661,0.000035377365,0.014498406,0.00031409872],"about_ca_topic_score_codex":0.000004258573,"about_ca_topic_score_gemma":0.0000034378586,"teacher_disagreement_score":0.7960915,"about_ca_system_score_codex":0.000053897453,"about_ca_system_score_gemma":0.0000036914817,"threshold_uncertainty_score":0.3774192},"labels":[],"label_agreement":null},{"id":"W1995062145","doi":"10.1109/smc.2013.203","title":"Wrapper Feature Selection Significantly Improves Nonlinear Prediction of Electricity Spot Prices","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Grantová Agentura České Republiky","keywords":"Feature selection; Selection (genetic algorithm); Computer science; Feature (linguistics); Set (abstract data type); Artificial neural network; Artificial intelligence; Nonlinear system; Machine learning; Spot contract; Electricity; Pattern recognition (psychology); Mathematical optimization; Data mining; Engineering; Mathematics; Futures contract","score_opus":0.005821046090764404,"score_gpt":0.1786429540056222,"score_spread":0.1728219079148578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995062145","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":[0.970305,0.00014556423,0.011838782,0.000027790968,0.00025799515,0.00016033802,0.0000068468407,0.0004670522,0.01679061],"genre_scores_gemma":[0.99433464,0.000043002914,0.004577195,0.0000147233395,0.00019004667,0.000013682011,0.00001466294,0.000020695072,0.0007913786],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994171,0.00000752502,0.0001579699,0.00011741026,0.00010930362,0.00019064173],"domain_scores_gemma":[0.9997462,0.000026955506,0.000033596018,0.00007816077,0.0000699712,0.00004509238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000058201058,0.00011512996,0.00011820796,0.0000891509,0.00003819398,0.00002342954,0.000064842614,0.00010744905,0.000121413],"category_scores_gemma":[0.000013146821,0.00009661807,0.000043744036,0.000304054,0.0000120748,0.0002344836,0.0000074468085,0.00015566425,0.000015793743],"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":[0.0000059692566,0.000032800395,0.0021488483,0.00008900498,0.000057980298,2.2976145e-7,0.00013429075,0.00972421,0.9650289,0.00015785075,0.0048712324,0.017748697],"study_design_scores_gemma":[0.00016828065,0.00012367622,0.01494146,0.000021798138,0.000015347954,0.0000059792865,0.00004548757,0.4591593,0.5224563,0.000049679722,0.00286198,0.0001506821],"about_ca_topic_score_codex":0.00014990832,"about_ca_topic_score_gemma":0.000025368838,"teacher_disagreement_score":0.4494351,"about_ca_system_score_codex":0.000024266317,"about_ca_system_score_gemma":0.0000106546095,"threshold_uncertainty_score":0.39399704},"labels":[],"label_agreement":null},{"id":"W1996559929","doi":"10.1109/ccece.2012.6334953","title":"Statistical analysis of environment Canada's wind speed data","year":2012,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of New Brunswick","funders":"","keywords":"Weibull distribution; Wind speed; Wind power; Meteorology; Time series; Histogram; Gaussian; Series (stratigraphy); Environmental science; Statistics; Computer science; Mathematics; Engineering; Geology; Geography; Physics","score_opus":0.021241346835453714,"score_gpt":0.2140425450481814,"score_spread":0.19280119821272768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996559929","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":[0.9001476,0.0005060645,0.04317707,0.000029710325,0.00056822714,0.000050793842,0.0009935487,0.00006826738,0.054458704],"genre_scores_gemma":[0.9972222,0.000009170845,0.0022747233,0.0000122776955,0.000035786994,5.369132e-8,0.0002714089,0.0000066103994,0.00016780138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995432,0.0000057727984,0.00011993691,0.000060560196,0.0001163891,0.00015416277],"domain_scores_gemma":[0.99959064,0.000052273805,0.000010231892,0.00026727055,0.0000020339858,0.00007755008],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007337998,0.00005449491,0.000115740324,0.0000363395,0.000009533836,0.000002761055,0.00010552832,0.00001618483,0.0012754634],"category_scores_gemma":[0.000008824717,0.000048756057,0.000011559809,0.00009499087,0.000010090992,0.000051242474,0.00004313134,0.000034485594,0.000004126579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000038743383,0.000050615086,0.19530179,0.00004128485,0.0022276079,0.0000064903147,0.00018289367,0.7659538,0.001779892,0.0039104265,0.020989416,0.009551943],"study_design_scores_gemma":[0.00013350091,0.000008211241,0.22283043,0.000005580984,0.00084664114,0.0000014943747,0.00008218296,0.67876923,0.0022100396,0.0000063992507,0.09483512,0.00027118067],"about_ca_topic_score_codex":0.0669977,"about_ca_topic_score_gemma":0.074300356,"teacher_disagreement_score":0.097074546,"about_ca_system_score_codex":0.000030164858,"about_ca_system_score_gemma":0.000010512315,"threshold_uncertainty_score":0.9996375},"labels":[],"label_agreement":null},{"id":"W1996619742","doi":"10.2202/1553-779x.2266","title":"A New Method for Next-Day Price Forecasting for PJM Electricity Market","year":2010,"lang":"en","type":"article","venue":"International Journal of Emerging Electric Power Systems","topic":"Energy Load and Power Forecasting","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":"University of Calgary","funders":"","keywords":"Electricity price forecasting; Electricity market; Bidding; Electricity; Econometrics; Order (exchange); Electricity price; Market price; Investment (military); Economics; Computer science; Microeconomics; Engineering; Finance","score_opus":0.01630360500275095,"score_gpt":0.2763741646777082,"score_spread":0.26007055967495724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996619742","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":[0.014623702,0.001263869,0.9664397,0.00014011982,0.013174618,0.00030123832,0.000014609901,0.0000972629,0.0039448855],"genre_scores_gemma":[0.9365995,0.000048609283,0.05929598,0.000057185796,0.002850543,0.000035775367,0.0000069248576,0.00009664742,0.0010088324],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997843,0.000047509366,0.0009153842,0.00019775117,0.0004843465,0.0005119577],"domain_scores_gemma":[0.99732965,0.0010995367,0.00050648925,0.00013584098,0.0007378271,0.00019068262],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018871485,0.00026011435,0.00040287303,0.0006065779,0.00009249823,0.00021377532,0.00066107017,0.00014792965,0.000047558286],"category_scores_gemma":[0.0009684138,0.00024450358,0.00032462893,0.00036089437,0.000006203361,0.00042376644,0.00002296944,0.00048232588,0.0000013314882],"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":[0.00084588927,0.0001769883,0.0008174191,0.00040393168,0.0037543862,0.00010258472,0.0019172955,0.10053977,0.38240772,0.013828737,0.2513287,0.24387656],"study_design_scores_gemma":[0.001305806,0.0002185099,0.00005368729,0.0001809523,0.0000649951,0.00083951,0.00003518764,0.71982855,0.007818744,0.00058701355,0.2687207,0.00034634364],"about_ca_topic_score_codex":0.00003841276,"about_ca_topic_score_gemma":0.000010933136,"teacher_disagreement_score":0.9219758,"about_ca_system_score_codex":0.00016521425,"about_ca_system_score_gemma":0.00017179374,"threshold_uncertainty_score":0.99705666},"labels":[],"label_agreement":null},{"id":"W2000322714","doi":"10.1057/mel.2015.2","title":"A new approach for Baltic Dry Index forecasting based on empirical mode decomposition and neural networks","year":2015,"lang":"en","type":"article","venue":"Maritime Economics & Logistics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Hilbert–Huang transform; Artificial neural network; Mode (computer interface); Decomposition; Index (typography); Term (time); Computer science; Artificial intelligence; Pattern recognition (psychology); Mathematics; Statistics; Energy (signal processing)","score_opus":0.051879624242221103,"score_gpt":0.280765309876521,"score_spread":0.2288856856342999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000322714","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":[0.013834848,0.00006909902,0.97774947,0.000044413715,0.00042694274,0.00018018999,0.00003523536,0.00015017403,0.0075096125],"genre_scores_gemma":[0.91970557,0.0000063351063,0.07915652,0.00025637526,0.00052171445,0.00002184156,0.00022076872,0.000059860784,0.000051038776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898607,0.000019203106,0.00030430045,0.00027113542,0.000046805,0.00037249658],"domain_scores_gemma":[0.99918395,0.00029839686,0.000052975596,0.0001758306,0.000029610359,0.00025926332],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020024063,0.00022637576,0.00026139084,0.000085387095,0.00008022639,0.000121559155,0.00011846483,0.0001774806,0.0000066332004],"category_scores_gemma":[0.000081350976,0.00026014005,0.00005970911,0.000057096655,0.000037504044,0.0000889764,0.000040645096,0.00021850223,0.0000016424635],"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":[0.000059967773,0.000016744689,0.0035693995,0.000033595054,0.000015756159,0.000002266773,0.000033468285,0.9811738,7.630007e-7,0.0006720309,0.0010942678,0.013327946],"study_design_scores_gemma":[0.0008653759,0.00009439457,0.00015793774,0.000016904647,0.000027320355,0.000010638775,0.0000135563905,0.9964816,0.0000073218466,0.0015220015,0.0005250914,0.00027786914],"about_ca_topic_score_codex":0.00004561338,"about_ca_topic_score_gemma":0.000041001957,"teacher_disagreement_score":0.9058707,"about_ca_system_score_codex":0.00014597506,"about_ca_system_score_gemma":0.0000419966,"threshold_uncertainty_score":0.9999851},"labels":[],"label_agreement":null},{"id":"W2001370835","doi":"10.1260/030952408786411967","title":"Extrapolation of Wind Profiles Using Indirect Measures of Stability","year":2008,"lang":"en","type":"article","venue":"Wind Engineering","topic":"Energy Load and Power Forecasting","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":"WSP (Canada)","funders":"","keywords":"Extrapolation; Wind profile power law; Wind power; Meteorology; Environmental science; Wind speed; Tower; Stability (learning theory); Wind shear; Log wind profile; Atmospheric instability; Wind gradient; Engineering; Mathematics; Statistics; Computer science; Geography; Structural engineering","score_opus":0.04916101540135306,"score_gpt":0.20488138106984344,"score_spread":0.15572036566849037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001370835","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":[0.9904355,0.0005725843,0.007409955,7.070656e-7,0.00022717271,0.000069539434,0.000010836781,0.00013305302,0.0011406534],"genre_scores_gemma":[0.99500763,0.000020731268,0.0048561557,5.067574e-7,0.000073316034,9.0509144e-7,0.000004606984,0.000032274846,0.000003873814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920726,0.000009889037,0.00031600482,0.00010404293,0.00017255393,0.00019023071],"domain_scores_gemma":[0.99966383,0.000047917285,0.00004596754,0.00015518206,0.000042229847,0.000044891014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014543842,0.00014048113,0.0002380552,0.00014100446,0.000028520686,0.0000033553124,0.00008056971,0.00007895131,0.000020079684],"category_scores_gemma":[0.000048553466,0.00015092212,0.00006614654,0.0002558895,0.000030208243,0.00014204628,0.000013862542,0.000114335526,5.0011386e-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":[0.0000029263324,0.000008617602,0.009983645,0.00014286276,0.000032471668,0.0000014953047,0.0008117737,0.706129,0.28235382,0.000043062406,0.0000028574418,0.00048746218],"study_design_scores_gemma":[0.00019894927,0.000023261753,0.02399321,0.00018846823,0.000019072642,0.00002062821,0.00003952157,0.3667638,0.60829335,0.000008907007,0.0002216357,0.00022918773],"about_ca_topic_score_codex":0.000023321267,"about_ca_topic_score_gemma":0.0000015619346,"teacher_disagreement_score":0.33936518,"about_ca_system_score_codex":0.000038197428,"about_ca_system_score_gemma":0.000019218425,"threshold_uncertainty_score":0.6154426},"labels":[],"label_agreement":null},{"id":"W2006629977","doi":"10.1109/infocom.2014.6848231","title":"Blowing hard is not all we want: Quantity vs quality of wind power in the smart grid","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Wind power; Renewable energy; Smart grid; Computer science; Distributed generation; Grid; Intermittent energy source; Electricity; Turbine; Greenhouse gas; Environmental science; Electricity generation; Environmental economics; Power (physics); Engineering; Electrical engineering; Aerospace engineering","score_opus":0.03947091520311945,"score_gpt":0.25926076386442626,"score_spread":0.2197898486613068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006629977","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":[0.9658752,0.000064605774,0.0008246403,0.00065565616,0.0005106249,0.000054719956,0.00000931517,0.00008541069,0.03191981],"genre_scores_gemma":[0.99876106,0.0000162006,0.00054379884,0.00044416354,0.000079702026,0.0000016517463,0.0000040670993,0.000017129656,0.00013221489],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9989585,0.000081280734,0.00035463786,0.00014361326,0.00021285853,0.0002490573],"domain_scores_gemma":[0.9993729,0.00022643745,0.00003536677,0.00030901795,0.000019268093,0.000037035257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009283848,0.00013667336,0.00022991661,0.000055104072,0.00003328865,0.000026215279,0.00022483579,0.000075828066,0.000178535],"category_scores_gemma":[0.000056854184,0.00009886159,0.00009464531,0.000134933,0.00002630728,0.0001145147,0.000032758584,0.00019108452,0.00002892206],"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":[0.00028252328,0.0007397128,0.4316902,0.0022149628,0.000834189,0.000047847778,0.088952266,0.08331525,0.17361552,0.14692512,0.050773114,0.020609308],"study_design_scores_gemma":[0.0036422254,0.00046266342,0.39613327,0.00075073924,0.00010007029,0.00003971973,0.002060809,0.06563136,0.21611753,0.0022348098,0.3105294,0.0022973772],"about_ca_topic_score_codex":0.00051942846,"about_ca_topic_score_gemma":0.00044291225,"teacher_disagreement_score":0.2597563,"about_ca_system_score_codex":0.000014425556,"about_ca_system_score_gemma":0.0000060330103,"threshold_uncertainty_score":0.40314585},"labels":[],"label_agreement":null},{"id":"W2006672684","doi":"10.1109/tpwrs.2012.2205714","title":"Probabilistic Load Flow Modeling Comparing Maximum Entropy and Gram-Charlier Probability Density Function Reconstructions","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":94,"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 Victoria","funders":"","keywords":"Probabilistic logic; Probability density function; Monte Carlo method; Principle of maximum entropy; Grid; Mathematical optimization; Computer science; Entropy (arrow of time); Flow (mathematics); Algorithm; Applied mathematics; Mathematics; Statistics; Physics","score_opus":0.022213052834848902,"score_gpt":0.19743919047109978,"score_spread":0.1752261376362509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006672684","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":[0.3898454,0.00030107683,0.6028214,0.0000062330246,0.005296979,0.00029255336,0.000015825193,0.0004228401,0.000997704],"genre_scores_gemma":[0.9987568,0.000024256939,0.0008919959,0.000008185178,0.00008492139,0.00010442679,0.0000044668395,0.00005238082,0.000072516625],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833304,0.00007776607,0.00047929026,0.00032144118,0.00026353844,0.0005249404],"domain_scores_gemma":[0.9991713,0.00006547537,0.000051529038,0.00035393637,0.000108536755,0.00024921182],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038331657,0.00031740527,0.00035283537,0.00013205645,0.00034116057,0.00010306935,0.00008270312,0.00017737078,0.000041239437],"category_scores_gemma":[0.000008775522,0.0003216769,0.000112354035,0.00021189339,0.00006495332,0.00046081096,0.0000021619776,0.00042571555,0.00004431818],"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":[0.000037778438,0.000089254965,0.0005945249,0.00020386344,0.0001138702,8.4358265e-7,0.0006754836,0.9958877,0.0005947202,0.00031747797,0.000035528676,0.0014489804],"study_design_scores_gemma":[0.00047343684,0.00006581515,0.00014071196,0.00019489283,0.00012120564,0.00017218679,0.00023257812,0.99643266,0.0006882004,0.00030639063,0.00071410957,0.00045780116],"about_ca_topic_score_codex":0.00012209022,"about_ca_topic_score_gemma":0.000100706486,"teacher_disagreement_score":0.60891145,"about_ca_system_score_codex":0.00032451816,"about_ca_system_score_gemma":0.000028556115,"threshold_uncertainty_score":0.9999235},"labels":[],"label_agreement":null},{"id":"W2008431679","doi":"10.1109/ccece.2014.6901040","title":"Wind energy forecast error estimation using black &amp;amp; scholes mathematical model","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University","funders":"","keywords":"Renewable energy; Wind power; Volatility (finance); Computer science; Black–Scholes model; Econometrics; Mathematical optimization; Economics; Engineering; Mathematics; Electrical engineering","score_opus":0.04396167830653583,"score_gpt":0.26076515752985285,"score_spread":0.21680347922331702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008431679","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":[0.27236584,0.000014167399,0.6891476,0.000015805235,0.0000691324,0.000025808846,0.0000020104176,0.0002584588,0.038101204],"genre_scores_gemma":[0.8003091,0.0000020761368,0.19796889,0.000052045118,0.0001052799,0.000002363322,0.000022181917,0.00005146169,0.0014866126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903506,0.000016730593,0.00028761209,0.0001705182,0.00017552503,0.00031455318],"domain_scores_gemma":[0.9994916,0.00006353929,0.000033686294,0.00026410678,0.000032651344,0.00011443788],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016069009,0.00020529838,0.00020153986,0.000096256554,0.00007930958,0.000076066295,0.00012373416,0.00011884585,0.00018085526],"category_scores_gemma":[0.00006307695,0.00018786103,0.00006973663,0.00012282966,0.000044689914,0.0002615942,0.0000364392,0.00011748291,0.00015724833],"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":[0.0000019761883,0.00001201589,0.000015094737,0.000052802232,0.000014863714,2.2175016e-7,0.00018205411,0.97119635,0.0022400597,0.020584827,0.00047238343,0.0052273516],"study_design_scores_gemma":[0.00015101167,0.000004485703,0.0000045556067,0.00008286371,0.000017798091,0.00001642397,0.0000097071215,0.98334277,0.0019672005,0.012155107,0.002012618,0.00023547001],"about_ca_topic_score_codex":0.000015795948,"about_ca_topic_score_gemma":0.00006994912,"teacher_disagreement_score":0.52794325,"about_ca_system_score_codex":0.00004472435,"about_ca_system_score_gemma":0.000011088977,"threshold_uncertainty_score":0.7660751},"labels":[],"label_agreement":null},{"id":"W2011600182","doi":"10.1109/icece.2014.7026882","title":"Data logging and energy consumption analysis of two houses in St. John's, Newfoundland","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Data logger; Microcontroller; Energy consumption; Power consumption; Logging; Computer science; Computer hardware; Consumption (sociology); Power (physics); Embedded system; Real-time computing; Electrical engineering; Engineering; Operating system; Geography; Forestry","score_opus":0.02877236774400859,"score_gpt":0.2537457044814323,"score_spread":0.2249733367374237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011600182","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":[0.9426466,0.00044670716,0.04918354,0.000008892988,0.00008849462,0.0000102329395,0.000031713098,0.00010468999,0.007479122],"genre_scores_gemma":[0.9985116,0.00023090724,0.0010460464,0.000020546342,0.000026461938,7.527977e-7,0.000107096064,0.000009718109,0.000046855763],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951434,0.000019077936,0.00016742878,0.00012804654,0.000058721238,0.00011238961],"domain_scores_gemma":[0.9995819,0.00011404501,0.000022684157,0.00024444974,0.0000064701667,0.00003048915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016489919,0.000073612326,0.00018043134,0.00023930125,0.000014338423,0.000018085373,0.00010027066,0.000028536397,0.000059264286],"category_scores_gemma":[0.000015730957,0.0000684824,0.000016227053,0.00018561621,0.000021168587,0.00012596876,0.000054417582,0.000039763585,5.644613e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000052771466,0.000015870042,0.5873781,0.00007324112,0.00038830316,0.000002420267,0.0001335237,0.3685845,0.0016681944,0.008193845,0.00020523748,0.033351492],"study_design_scores_gemma":[0.00026479823,0.000007229737,0.03179996,0.000025418854,0.00012424123,0.000001355081,0.000007848355,0.9596836,0.00016738188,0.00006331434,0.007742595,0.00011227439],"about_ca_topic_score_codex":0.0013943395,"about_ca_topic_score_gemma":0.028069478,"teacher_disagreement_score":0.5910991,"about_ca_system_score_codex":0.000010074155,"about_ca_system_score_gemma":0.0000030352687,"threshold_uncertainty_score":0.98966575},"labels":[],"label_agreement":null},{"id":"W2011699084","doi":"10.1109/icsssm.2014.6943390","title":"Forecasting day-ahead electricity prices using data mining and neural network techniques","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Electricity price forecasting; Electricity; Electricity market; Computer science; Data set; Training set; Set (abstract data type); Econometrics; Artificial intelligence; Data mining; Engineering; Economics","score_opus":0.04786390194559933,"score_gpt":0.24835588520848306,"score_spread":0.20049198326288373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2011699084","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":[0.7917918,0.000739008,0.18093045,0.00001696002,0.00034521273,0.00010904608,0.000003990914,0.0010458827,0.02501766],"genre_scores_gemma":[0.877655,0.000020507621,0.1215595,0.000059903137,0.00061917043,0.000002172741,0.0000191881,0.00003859205,0.00002592416],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989404,0.000028845861,0.00023428166,0.00025980896,0.000104029874,0.0004326379],"domain_scores_gemma":[0.9993192,0.00022990136,0.000046079214,0.00030935818,0.000018230006,0.00007723022],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005367941,0.00018257594,0.00019415803,0.00006489615,0.000163472,0.0000959697,0.0002443031,0.000078686186,0.000008687727],"category_scores_gemma":[0.00009178781,0.00017015808,0.000018854476,0.0002495162,0.000024309393,0.00037823466,0.0001831508,0.00013784425,3.2051378e-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":[0.000007979141,0.000012646585,0.021190599,0.00017780886,0.00007020168,0.000009900388,0.00031946943,0.10458816,0.0049913228,0.00061466946,0.0027387978,0.8652784],"study_design_scores_gemma":[0.0000621201,0.00002500194,0.00013492435,0.00007668323,0.00001691846,0.00003700509,0.000012492808,0.9936494,0.0018216437,0.00006932485,0.0038762956,0.00021819373],"about_ca_topic_score_codex":0.00005203629,"about_ca_topic_score_gemma":0.0000878969,"teacher_disagreement_score":0.8890612,"about_ca_system_score_codex":0.00001730364,"about_ca_system_score_gemma":0.0000061647015,"threshold_uncertainty_score":0.69388455},"labels":[],"label_agreement":null},{"id":"W2015195567","doi":"10.1016/j.rser.2013.06.022","title":"A comparative study of optimal hybrid methods for wind power prediction in wind farm of Alberta, Canada","year":2013,"lang":"en","type":"article","venue":"Renewable and Sustainable Energy Reviews","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Wind power; Artificial neural network; Particle swarm optimization; Imperialist competitive algorithm; Wind power forecasting; Time series; Hybrid power; Genetic algorithm; Electric power system; Engineering; Power (physics); Computer science; Artificial intelligence; Machine learning; Multi-swarm optimization","score_opus":0.014670747619125575,"score_gpt":0.2619024139922946,"score_spread":0.247231666373169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2015195567","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":[0.960635,0.02013375,0.00784299,0.0000102207705,0.00018029581,0.0011253746,0.0000077940995,0.000018926694,0.010045646],"genre_scores_gemma":[0.99261904,0.0008102801,0.0021681977,0.000013566128,0.000026542135,0.00012582303,0.000016138418,0.00002393672,0.004196492],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99851185,0.0001283969,0.00066692114,0.00020974402,0.000090990114,0.0003920731],"domain_scores_gemma":[0.9992385,0.00020410109,0.00013662087,0.00020747946,0.00013222496,0.00008107852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004717938,0.00021261863,0.0007773277,0.00012557741,0.000057303787,0.000016484673,0.00011143608,0.000049055947,0.00004053978],"category_scores_gemma":[0.000066848675,0.00018642745,0.000056937235,0.00028619546,0.00002371861,0.00016052008,0.0000440594,0.000069655514,1.0979572e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000498515,0.00025316948,0.002427899,0.0016106443,0.00022593727,0.000008204039,0.0037844663,0.96613413,0.0016174281,0.00078032276,0.0041947607,0.018913213],"study_design_scores_gemma":[0.0029428026,0.0012794512,0.0009990725,0.0006066,0.00016681082,0.000018985735,0.023732832,0.15336296,0.020175848,0.00049085263,0.7954132,0.000810589],"about_ca_topic_score_codex":0.6805295,"about_ca_topic_score_gemma":0.24770167,"teacher_disagreement_score":0.81277114,"about_ca_system_score_codex":0.00010004406,"about_ca_system_score_gemma":0.00011707506,"threshold_uncertainty_score":0.76602584},"labels":[],"label_agreement":null},{"id":"W2020760721","doi":"10.1109/poweri.2012.6479588","title":"Soft computing applications in wind speed and power prediction for wind energy","year":2012,"lang":"en","type":"article","venue":"2012 IEEE Fifth Power India Conference","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"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":"Soft computing; Computer science; Wind speed; Artificial neural network; Wind power; Adaptive neuro fuzzy inference system; Power (physics); Fuzzy logic; Construct (python library); Electric power system; Fuzzy inference system; Data mining; Artificial intelligence; Fuzzy control system; Engineering; Meteorology","score_opus":0.017600371856304097,"score_gpt":0.23085067500083314,"score_spread":0.21325030314452906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020760721","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":[0.9241237,0.0011012527,0.061093677,0.000023128594,0.002095505,0.00039796915,0.00012535785,0.00027820675,0.010761203],"genre_scores_gemma":[0.9987219,0.000027821701,0.00064313196,0.00005103675,0.0002578285,0.000010429202,0.00005711204,0.000044956007,0.00018578442],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986978,0.000023251085,0.00032857418,0.00023495329,0.00013223202,0.0005831782],"domain_scores_gemma":[0.99930143,0.00017752529,0.00006975956,0.0002101243,0.00005461376,0.00018651527],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023251359,0.0002457903,0.00023615926,0.00016379208,0.000112745314,0.000057143647,0.00014899095,0.00018815634,0.00009190938],"category_scores_gemma":[0.00001888513,0.00025640306,0.000045570876,0.00018308198,0.000056488112,0.00044167123,0.000033079654,0.00020045669,0.00001188804],"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":[0.00014773838,0.00084473286,0.7358675,0.00080495613,0.00057389296,0.000011711206,0.056787312,0.049243588,0.035071105,0.061483394,0.01065524,0.04850884],"study_design_scores_gemma":[0.003609199,0.00031648236,0.38506612,0.00065278565,0.00012679995,0.00007861985,0.0013117362,0.10205849,0.008465429,0.0014194854,0.49438742,0.002507427],"about_ca_topic_score_codex":0.000026415426,"about_ca_topic_score_gemma":0.000018795046,"teacher_disagreement_score":0.48373216,"about_ca_system_score_codex":0.000045760335,"about_ca_system_score_gemma":0.000031062857,"threshold_uncertainty_score":0.9999888},"labels":[],"label_agreement":null},{"id":"W2024032348","doi":"10.1016/j.procs.2012.09.080","title":"Forecasting Power Output of Solar Photovoltaic System Using Wavelet Transform and Artificial Intelligence Techniques","year":2012,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":208,"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 New Brunswick","funders":"","keywords":"Computer science; Photovoltaic system; Grid-connected photovoltaic power system; Electric power system; Wavelet transform; Solar power; Electricity generation; Maximum power point tracking; Renewable energy; Power (physics); Reliability engineering; Wavelet; Artificial intelligence; Electrical engineering; Engineering","score_opus":0.03443551176320986,"score_gpt":0.23106682404862014,"score_spread":0.19663131228541028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024032348","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":[0.4210137,0.0001704648,0.57730764,0.0000021695787,0.00052978814,0.00011882473,0.0000023429714,0.00019977834,0.00065525185],"genre_scores_gemma":[0.869432,0.0000048379434,0.13035738,0.000008635997,0.00017386272,0.0000055029805,4.29044e-7,0.000016469407,9.031978e-7],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869263,0.0000072796906,0.0003342517,0.00021011908,0.00026054698,0.00049516046],"domain_scores_gemma":[0.99952126,0.000046026507,0.00006010242,0.00013296369,0.00009401402,0.00014565153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006608875,0.0001681086,0.00019426685,0.00020449619,0.0001502481,0.0000699695,0.00024845815,0.00005722473,0.0000016597834],"category_scores_gemma":[0.000020508734,0.00015687791,0.0000349563,0.0005234782,0.00020017444,0.0006878328,0.000082452294,0.00013018106,0.0000010289316],"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":[0.000013690508,0.000070239985,0.0031235751,0.0014628312,0.000030629293,0.000008375442,0.013041369,0.009105115,0.12667754,0.0044532623,0.000015747617,0.8419976],"study_design_scores_gemma":[0.000013402154,0.000030575142,0.000049440852,0.00019369824,0.000005615949,0.000085217405,0.00007702793,0.7035614,0.29571676,0.00007571899,0.000045340763,0.0001457811],"about_ca_topic_score_codex":0.000012019598,"about_ca_topic_score_gemma":0.0000017693663,"teacher_disagreement_score":0.84185183,"about_ca_system_score_codex":0.00006557062,"about_ca_system_score_gemma":0.00004233215,"threshold_uncertainty_score":0.63972956},"labels":[],"label_agreement":null},{"id":"W2025295529","doi":"10.4028/www.scientific.net/amm.368-370.2043","title":"Study on the Relationship Between City and District Average Price by GAOT in Taipei","year":2013,"lang":"en","type":"article","venue":"Applied Mechanics and Materials","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Unit price; Unit (ring theory); Real estate; Econometrics; Quarter (Canadian coin); Correlation coefficient; Economics; Statistics; Agricultural economics; Business; Mathematics; Geography; Microeconomics; Finance","score_opus":0.0271753102614289,"score_gpt":0.21474840696163,"score_spread":0.18757309670020111,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2025295529","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":[0.996582,0.000023221854,0.0017567306,0.00002924207,0.00008028652,0.0003491191,0.000023161561,0.000044215725,0.0011120732],"genre_scores_gemma":[0.9997118,0.000015405378,0.0000718462,0.000025988864,0.000037280068,0.00008803599,0.000013870632,0.000015365587,0.000020393793],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.99948543,0.000021768426,0.0001649838,0.00012288365,0.00006473415,0.00014020102],"domain_scores_gemma":[0.9996042,0.00021964421,0.00002744898,0.00010725258,0.000004392663,0.000037018108],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002724101,0.00010801515,0.00013745905,0.000027060762,0.00006953207,0.00008399068,0.000055803623,0.00005287503,0.000029152947],"category_scores_gemma":[0.000022807306,0.000079793754,0.0000057242005,0.000067113455,0.000004233655,0.000037951173,0.000038203845,0.00007798563,0.000007763453],"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":[0.000026631244,0.00014665182,0.015065189,0.00017496332,0.00010522986,0.000006107189,0.0037617849,0.00014268397,0.17799526,0.7968953,0.002047853,0.0036322963],"study_design_scores_gemma":[0.0044007604,0.0005650276,0.5220502,0.00025497554,0.00013622658,0.000009477664,0.0030315942,0.000991175,0.17148201,0.29144457,0.0034525413,0.0021814497],"about_ca_topic_score_codex":0.000037158057,"about_ca_topic_score_gemma":0.0000036289941,"teacher_disagreement_score":0.506985,"about_ca_system_score_codex":0.000011320023,"about_ca_system_score_gemma":0.0000018999416,"threshold_uncertainty_score":0.32538947},"labels":[],"label_agreement":null},{"id":"W2026040110","doi":"10.1016/j.apenergy.2014.08.022","title":"Performance prediction of a hybrid microgeneration system using Adaptive Neuro-Fuzzy Inference System (ANFIS) technique","year":2014,"lang":"en","type":"article","venue":"Applied Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Adaptive neuro fuzzy inference system; TRNSYS; Hybrid system; Neuro-fuzzy; MATLAB; Inference system; Computer science; Engineering; Fuzzy control system; Simulation; Control engineering; Automotive engineering; Fuzzy logic; Machine learning; Artificial intelligence; Mathematics; Statistics","score_opus":0.010766794935298307,"score_gpt":0.17510475375924917,"score_spread":0.16433795882395086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2026040110","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":[0.5380538,0.00003611833,0.43608144,5.7230545e-7,0.00034958788,0.000107503765,0.000014650456,0.00057460903,0.024781747],"genre_scores_gemma":[0.99566364,0.000013487163,0.0039155646,0.000007029421,0.00022041662,0.00009467716,0.000029310513,0.000048507565,0.0000073506394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990037,0.000029924217,0.00037147402,0.00020967502,0.00015522871,0.00022998043],"domain_scores_gemma":[0.9994953,0.00004638069,0.0001072747,0.00024224875,0.000052363343,0.00005641558],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015696281,0.00020283152,0.0002452216,0.0001279041,0.00009036757,0.000020077945,0.00012810525,0.000082054255,0.0000013664088],"category_scores_gemma":[0.000004622657,0.00021213837,0.00004334215,0.00018861413,0.000026260714,0.00011434331,0.000031973228,0.00010220014,0.0000024454052],"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":[0.000009816083,0.0000063005878,0.000055751327,0.00027684696,0.000021791415,0.0000010385631,0.000032480046,0.5940124,0.36185735,0.04123895,0.000018891356,0.0024684162],"study_design_scores_gemma":[0.00010349834,0.000031960168,0.000017921253,0.0001722858,0.000016159283,0.000031598294,0.00002503516,0.5783966,0.42075542,0.000009725234,0.00033173795,0.00010807294],"about_ca_topic_score_codex":0.000086636086,"about_ca_topic_score_gemma":0.000005237083,"teacher_disagreement_score":0.45760986,"about_ca_system_score_codex":0.0001301675,"about_ca_system_score_gemma":0.000018017638,"threshold_uncertainty_score":0.8650752},"labels":[],"label_agreement":null},{"id":"W2027309736","doi":"10.1016/j.renene.2015.03.048","title":"Regarding the influence of the Van der Hoven spectrum on wind energy applications in the meteorological mesoscale and microscale","year":2015,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Microscale chemistry; Mesoscale meteorology; Spectral density; Wind power; Kinetic energy; Turbulence; Energy (signal processing); Physics; Meteorology; Turbulence kinetic energy; Frequency domain; Computational physics; Environmental science; Statistical physics; Mathematics; Statistics; Classical mechanics; Electrical engineering; Engineering; Mathematical analysis","score_opus":0.010813745877033347,"score_gpt":0.2013110226937498,"score_spread":0.19049727681671647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027309736","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":[0.96368724,0.0019896636,0.0027163487,0.0004487024,0.00013716063,0.00010174798,0.000007894032,0.00006618785,0.030845031],"genre_scores_gemma":[0.9989035,0.00011780154,0.00007698771,0.00026617866,0.000100033234,0.00003453661,0.0000035846658,0.00001863608,0.00047873962],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990492,0.00011024441,0.00021304975,0.00017685452,0.00019078742,0.0002598757],"domain_scores_gemma":[0.99920326,0.00024532087,0.000053273136,0.0004267956,0.000019017805,0.000052302574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029541308,0.00015479614,0.00016639277,0.000046073663,0.00012232726,0.000035671623,0.00042187402,0.00009155393,0.000004884507],"category_scores_gemma":[0.000039858493,0.000077881494,0.000055427317,0.0003729669,0.00011637325,0.00006449799,0.000085013206,0.00013214594,6.654293e-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":[0.0000061636174,0.000017605047,0.001072687,0.0000056931012,0.000017324239,0.000001045264,0.0002468776,0.9847958,0.0049454807,0.007963218,0.00047323303,0.0004548842],"study_design_scores_gemma":[0.0015510775,0.00027457083,0.018888533,0.00027452657,0.00013081267,0.00010955921,0.0012507148,0.045220025,0.25798553,0.047272157,0.62612236,0.00092014676],"about_ca_topic_score_codex":0.0023659428,"about_ca_topic_score_gemma":0.0021539878,"teacher_disagreement_score":0.9395758,"about_ca_system_score_codex":0.000037237132,"about_ca_system_score_gemma":0.00002095023,"threshold_uncertainty_score":0.35766137},"labels":[],"label_agreement":null},{"id":"W2027327378","doi":"10.1109/icmlc.2010.5580713","title":"Artificial neural network for load forecasting in smart grid","year":2010,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":77,"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":"City, University of London","keywords":"Smart grid; Artificial neural network; Computer science; Electric power system; Grid; Key (lock); Power grid; Electric power transmission; Power system simulation; Power (physics); Load balancing (electrical power); Representation (politics); Industrial engineering; Operations research; Artificial intelligence; Engineering; Electrical engineering; Computer security","score_opus":0.023555881242050686,"score_gpt":0.2181455192291404,"score_spread":0.19458963798708973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027327378","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":[0.9674837,0.000046713,0.0035433415,0.000048036276,0.0047200248,0.0001421029,0.000005827738,0.00031190598,0.023698354],"genre_scores_gemma":[0.98892623,0.000001183553,0.008924099,0.00005504428,0.001860361,0.00003063668,0.00001400654,0.000036271573,0.00015217095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909085,0.0000051961633,0.00025013543,0.00013742823,0.00008050478,0.00043585963],"domain_scores_gemma":[0.999644,0.00013509452,0.00001808779,0.00011717662,0.00002402606,0.00006163001],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025014323,0.00013182925,0.00013683063,0.000040965577,0.00006269195,0.00003759375,0.00009900486,0.00009156986,0.00007674311],"category_scores_gemma":[0.000067217,0.00012762929,0.000058543956,0.00015895195,0.000015512282,0.0001050214,0.00001882814,0.0002541577,0.000008786559],"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":[0.000037391063,0.000024326508,0.017018914,0.000075933516,0.000021855454,0.000015657086,0.0002990895,0.82462364,0.010657631,0.007183914,0.0089839315,0.13105772],"study_design_scores_gemma":[0.00016438116,0.000024168481,0.0007622427,0.000021292695,0.0000046992454,0.000012568468,0.000014239603,0.97294736,0.0033246581,0.0010835924,0.021415098,0.00022571383],"about_ca_topic_score_codex":0.000055608667,"about_ca_topic_score_gemma":0.0050829006,"teacher_disagreement_score":0.14832371,"about_ca_system_score_codex":0.000019273959,"about_ca_system_score_gemma":0.000013332347,"threshold_uncertainty_score":0.52045715},"labels":[],"label_agreement":null},{"id":"W2028376765","doi":"10.1109/ccece.2013.6567687","title":"On error measures in wind forecasting evaluations","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"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 Calgary","funders":"","keywords":"Wind power; Wind power forecasting; Computer science; Wind speed; Grid; Probabilistic forecasting; Scale (ratio); Forecast error; Variety (cybernetics); Electricity; Focus (optics); Electric power system; Meteorology; Reliability engineering; Power (physics); Econometrics; Engineering; Artificial intelligence; Mathematics; Electrical engineering","score_opus":0.05044530542831856,"score_gpt":0.2552628143625727,"score_spread":0.20481750893425416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028376765","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":[0.7913881,0.000049408216,0.0013180416,0.000050102313,0.00020336152,0.00009741683,6.638658e-7,0.00017008667,0.20672281],"genre_scores_gemma":[0.99828315,0.000001755146,0.0011053052,0.000054880606,0.000049065424,0.000018297324,0.0000030765068,0.000018740488,0.00046572855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943763,0.000012698756,0.00014774642,0.000087461216,0.0001213546,0.00019310902],"domain_scores_gemma":[0.9997365,0.000092565046,0.000009560621,0.0000962743,0.00002363898,0.000041488078],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012590898,0.00008858002,0.00007767578,0.00009875797,0.00003319925,0.000028305043,0.000061814535,0.000040882907,0.0006182352],"category_scores_gemma":[0.00008729334,0.00007808099,0.000023827266,0.00014355645,0.000007663492,0.00012312003,0.000008601358,0.00010602742,0.00017899556],"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":[0.0000014523213,0.000018211318,0.0024002965,0.000015827869,0.000014563506,0.0000024352219,0.00049958186,0.9223002,0.0018471669,0.0028736615,0.0031218163,0.06690475],"study_design_scores_gemma":[0.0002999303,0.000031111682,0.0071727466,0.00009853484,0.000004178544,0.0000042942606,0.000108522196,0.9861647,0.0021208334,0.0028440428,0.00093686156,0.0002142725],"about_ca_topic_score_codex":0.000091380934,"about_ca_topic_score_gemma":0.00018473083,"teacher_disagreement_score":0.20689504,"about_ca_system_score_codex":0.000034561388,"about_ca_system_score_gemma":0.000006827721,"threshold_uncertainty_score":0.6769242},"labels":[],"label_agreement":null},{"id":"W2030637305","doi":"10.1049/iet-gtd.2014.0599","title":"Prediction interval estimations for electricity demands and prices: a multi‐objective approach","year":2015,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Energy Load and Power Forecasting","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":"","keywords":"Interval (graph theory); Electricity; Interval data; Interval arithmetic; Computer science; Econometrics; Electricity market; Mathematical optimization; Mathematics; Data mining; Engineering; Electrical engineering; Measure (data warehouse)","score_opus":0.04839696948713152,"score_gpt":0.24872875753429638,"score_spread":0.20033178804716487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2030637305","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":[0.07053956,0.0004692921,0.9277196,0.000036933016,0.0002154264,0.000311248,0.00020547179,0.00026364953,0.00023883625],"genre_scores_gemma":[0.9716371,0.000053777894,0.02499519,0.0000094050165,0.0001964089,0.0001483101,0.0029073802,0.000019087252,0.00003333788],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916285,0.000029986213,0.0002665688,0.00021445694,0.00014026207,0.00018587668],"domain_scores_gemma":[0.99955994,0.000025107558,0.000041464387,0.00008140644,0.0001443025,0.00014780588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002528093,0.0001548547,0.0001356857,0.000056881196,0.0001833027,0.00006853889,0.000047518042,0.00013017213,0.000003505311],"category_scores_gemma":[0.000047719623,0.00014758015,0.000052102914,0.00019989512,0.000020018988,0.00024431295,0.0000051574893,0.000106302665,0.000001109462],"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":[0.0001613962,0.0003004654,0.00085974537,0.0003356386,0.00014821181,9.747225e-7,0.0037511121,0.80810994,0.06310288,0.004341168,0.010167352,0.10872111],"study_design_scores_gemma":[0.00091326336,0.00011304858,0.0005015345,0.00002535055,0.000042552747,0.00000955067,0.000062135936,0.97179735,0.02169801,0.000155601,0.0045325058,0.00014908212],"about_ca_topic_score_codex":0.0000075200605,"about_ca_topic_score_gemma":0.0000048178,"teacher_disagreement_score":0.9027244,"about_ca_system_score_codex":0.00012929735,"about_ca_system_score_gemma":0.000036154026,"threshold_uncertainty_score":0.6018144},"labels":[],"label_agreement":null},{"id":"W2031294076","doi":"10.1109/pes.2010.5589334","title":"Electricity market price forecasting in a price-responsive smart grid environment","year":2010,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Calgary","funders":"","keywords":"Smart grid; Demand side; Electricity market; Electricity; Grid; Demand response; Demand forecasting; Industrial organization; Environmental economics; Microeconomics; Electricity price; Market price; Computer science; Economics; Business; Marketing; Engineering; Electrical engineering","score_opus":0.00809896889081782,"score_gpt":0.1803434340170819,"score_spread":0.17224446512626407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031294076","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":[0.81288254,0.000073621595,0.003258013,0.00003821876,0.0005718594,0.00015663747,0.000004401549,0.0002291408,0.18278554],"genre_scores_gemma":[0.9920222,0.000034478457,0.0062922654,0.000062685256,0.00020047114,0.00003282626,0.000005926404,0.000042344953,0.0013067618],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987786,0.000028217984,0.00028547048,0.00022807308,0.0001682275,0.00051144586],"domain_scores_gemma":[0.9993662,0.000257317,0.000036742575,0.00021583783,0.000012253823,0.00011164947],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004031509,0.00020609138,0.00018115279,0.00017158335,0.0000578395,0.00003171873,0.0001618954,0.00012269654,0.0010110843],"category_scores_gemma":[0.00013864136,0.00019921912,0.000053728898,0.00027270525,0.000022148834,0.00014793957,0.000054408054,0.0005169332,0.000044489705],"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":[0.0005248888,0.0007078122,0.14635558,0.0006300151,0.0004024135,0.00073416857,0.0048431177,0.15816574,0.4762552,0.007574661,0.03661782,0.16718858],"study_design_scores_gemma":[0.0011346089,0.00012538742,0.045099426,0.00008421509,0.000019654111,0.00015576134,0.00008875449,0.72848386,0.04955413,0.00037821016,0.17376466,0.0011113565],"about_ca_topic_score_codex":0.000052933396,"about_ca_topic_score_gemma":0.0001815093,"teacher_disagreement_score":0.5703181,"about_ca_system_score_codex":0.000078060715,"about_ca_system_score_gemma":0.000018683999,"threshold_uncertainty_score":0.9999021},"labels":[],"label_agreement":null},{"id":"W2033348701","doi":"10.1108/fs-09-2013-0045","title":"Forecasting inflation in G-7 countries: an application of artificial neural network","year":2015,"lang":"en","type":"article","venue":"foresight","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":19,"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":"Economics; Artificial neural network; Econometrics; Inflation (cosmology); Backpropagation; Consumer price index (South Africa); Benchmark (surveying); Index (typography); Price index; Monetary policy; Macroeconomics; Computer science; Artificial intelligence; Geography","score_opus":0.028034957239443593,"score_gpt":0.23042615374342787,"score_spread":0.20239119650398427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033348701","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":[0.97878325,0.00016489995,0.017369252,0.000008182751,0.00029132995,0.000112410125,0.0000033366796,0.000107172076,0.0031601929],"genre_scores_gemma":[0.99845344,0.0000019329439,0.0010711276,0.000009757313,0.00037457843,0.000015593689,0.000045767327,0.000019970534,0.000007844582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929637,0.000017447634,0.00027744705,0.000096207776,0.000121060344,0.0001914735],"domain_scores_gemma":[0.99968886,0.000038164875,0.000051200674,0.00012060496,0.00004274307,0.000058419977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022494124,0.000089628295,0.00011973415,0.00006858442,0.000024811292,0.000015268406,0.0000799835,0.00006776081,0.000005833833],"category_scores_gemma":[0.00002258423,0.00009133696,0.000018858313,0.00023545304,0.000018684012,0.0002307174,0.000011381925,0.00008006577,0.000004080399],"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":[0.0000204212,0.0000063548573,0.020700488,0.000020759111,0.00000340058,0.0000014111082,0.00071715825,0.96610314,0.00016430898,0.00339132,0.00015174627,0.008719503],"study_design_scores_gemma":[0.00014680807,0.000039655963,0.0016555705,0.000025259298,0.0000039418233,0.0000022153474,0.00004258845,0.99237573,0.0009767509,0.002244722,0.0023858745,0.00010087698],"about_ca_topic_score_codex":0.000052985946,"about_ca_topic_score_gemma":0.00070531986,"teacher_disagreement_score":0.026272608,"about_ca_system_score_codex":0.0000382947,"about_ca_system_score_gemma":0.000013951006,"threshold_uncertainty_score":0.37246132},"labels":[],"label_agreement":null},{"id":"W2034374099","doi":"10.1080/15325000802599353","title":"Day-ahead Price Forecasting in Ontario Electricity Market Using Variable-segmented Support Vector Machine-based Model","year":2009,"lang":"en","type":"article","venue":"Electric Power Components and Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Independent Electricity System Operator","keywords":"Support vector machine; Autoregressive model; Artificial neural network; Volatility (finance); Autoregressive integrated moving average; Time series; Electricity market; Moving-average model; Computer science; Heuristic; Econometrics; Electricity; Engineering; Artificial intelligence; Machine learning; Mathematics","score_opus":0.025927991343338716,"score_gpt":0.20630569994901882,"score_spread":0.1803777086056801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2034374099","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":[0.8887613,0.0009493826,0.09514443,0.0000086685395,0.0005460938,0.0004225759,0.000012638481,0.0002444819,0.0139104435],"genre_scores_gemma":[0.99831754,0.000009831808,0.0011189962,0.00006664095,0.00004969868,0.000012623523,0.000036750564,0.000049157283,0.00033876867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757916,0.00010086688,0.0006767542,0.0004119733,0.00034918552,0.000882086],"domain_scores_gemma":[0.9992663,0.00011609346,0.00013004667,0.0002477826,0.000050370945,0.00018939264],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006914124,0.00041386316,0.0005457019,0.00043450572,0.00015423173,0.00013110564,0.00020428043,0.00017131683,0.00004130256],"category_scores_gemma":[0.00003132083,0.0004150416,0.00006905255,0.0006909464,0.000010363932,0.00021319282,0.000021980411,0.00053748826,0.0000017836367],"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":[0.00019705902,0.00024993386,0.021517828,0.00016774696,0.00012860112,0.0001248736,0.0006060228,0.8834878,0.09122774,0.00042848074,0.00063092564,0.001232989],"study_design_scores_gemma":[0.0010371368,0.00013183171,0.006113694,0.00018846977,0.000022206004,0.00007136633,0.0000038934486,0.99094194,0.0003051149,0.000041451323,0.00069733587,0.00044557973],"about_ca_topic_score_codex":0.0064786067,"about_ca_topic_score_gemma":0.0010576764,"teacher_disagreement_score":0.10955624,"about_ca_system_score_codex":0.0006661419,"about_ca_system_score_gemma":0.00015535495,"threshold_uncertainty_score":0.9998301},"labels":[],"label_agreement":null},{"id":"W2035103704","doi":"10.2316/journal.201.2004.1.201-1171","title":"An Advanced Model for Short-Term Forecasting of Mean Wind Speed and Wind Electric Power","year":2004,"lang":"en","type":"article","venue":"Control and Intelligent Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"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":"Term (time); Wind speed; Wind power; Meteorology; Environmental science; Wind power forecasting; Power (physics); Computer science; Electric power system; Engineering; Electrical engineering; Physics","score_opus":0.02033662579337544,"score_gpt":0.2300696917380349,"score_spread":0.20973306594465946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035103704","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":[0.913116,0.004497721,0.08101965,0.0000049415407,0.00037482328,0.000274856,0.000024001187,0.000074079835,0.00061397534],"genre_scores_gemma":[0.9993884,0.00012592565,0.00024152047,0.000010245459,0.00010448956,0.000005942958,0.000008550815,0.000041466676,0.00007348722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99888945,0.000012729894,0.0004506516,0.00022318483,0.00010738184,0.00031661196],"domain_scores_gemma":[0.99951965,0.00006381111,0.000065573986,0.0001478124,0.000070366885,0.00013279947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021296274,0.000212429,0.00037411545,0.00010921223,0.00007199942,0.00004869808,0.00008879849,0.00009427942,0.0000013176171],"category_scores_gemma":[0.000021081863,0.0001920483,0.000059004335,0.00007832386,0.000026678568,0.00018285631,0.000008814836,0.0000922324,4.0555247e-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":[0.000059208327,0.000020951184,0.0004288142,0.00024954512,0.000073583105,0.0000020136063,0.0011109025,0.9365877,0.05079286,0.0007988872,0.000004134311,0.0098714],"study_design_scores_gemma":[0.0008622261,0.00022721969,0.000052070493,0.00024924424,0.000038433045,0.000026864891,0.00017543658,0.99355185,0.004346565,0.00012364589,0.00012059332,0.00022586722],"about_ca_topic_score_codex":0.000014393868,"about_ca_topic_score_gemma":0.0000094992965,"teacher_disagreement_score":0.08627241,"about_ca_system_score_codex":0.000036621386,"about_ca_system_score_gemma":0.000015019242,"threshold_uncertainty_score":0.78315026},"labels":[],"label_agreement":null},{"id":"W2035233482","doi":"10.1109/epec.2013.6802978","title":"Hybrid SVM &amp;amp; ARMAX based mid-term electricity market clearing price forecasting","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Saskatchewan","funders":"","keywords":"Support vector machine; Electricity price forecasting; Electricity market; Electricity; Computer science; Term (time); Market clearing; Autoregressive model; Scheduling (production processes); Autoregressive–moving-average model; Operations research; Artificial intelligence; Mathematical optimization; Econometrics; Engineering; Economics; Microeconomics; Mathematics","score_opus":0.021346903958837243,"score_gpt":0.20843568209678284,"score_spread":0.1870887781379456,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2035233482","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":[0.7448361,0.000109912275,0.05595533,0.000035443198,0.0004060593,0.00019123826,0.0000038358435,0.0009620249,0.1975001],"genre_scores_gemma":[0.97978437,0.000015150051,0.016167182,0.00017615713,0.00024437037,0.000040109786,0.000027710877,0.000093816896,0.0034511518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982233,0.000037982132,0.0004014972,0.00031166698,0.00024196488,0.0007835961],"domain_scores_gemma":[0.99903536,0.00025721773,0.00006395348,0.0003564223,0.00007606488,0.0002109741],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00028590765,0.00032591887,0.00027063984,0.00018324204,0.00016053962,0.00017374942,0.0002697455,0.00008858682,0.003530437],"category_scores_gemma":[0.00013362052,0.00031749372,0.00012625029,0.0003116698,0.000023898152,0.00036828214,0.00006454556,0.00036889786,0.00043129188],"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":[0.00010953541,0.0003236122,0.04956721,0.0017104769,0.0005846375,0.00009271412,0.0008191186,0.27465543,0.16912164,0.00049558334,0.2133858,0.28913423],"study_design_scores_gemma":[0.0008078469,0.000031888907,0.0052067135,0.0001902927,0.000037595808,0.0000932087,0.000014181336,0.89559126,0.041426085,0.00023391286,0.055269904,0.0010971301],"about_ca_topic_score_codex":0.00019194733,"about_ca_topic_score_gemma":0.000074501324,"teacher_disagreement_score":0.6209358,"about_ca_system_score_codex":0.000111992595,"about_ca_system_score_gemma":0.000023087203,"threshold_uncertainty_score":0.9999277},"labels":[],"label_agreement":null},{"id":"W2037194002","doi":"10.1109/epe.2014.6839490","title":"Analysis of wind speed and power time series preceding wind ramp events","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Wind power; Wind speed; Meteorology; Wind power forecasting; Renewable energy; Time series; Probabilistic logic; Environmental science; Parametric statistics; Series (stratigraphy); Computer science; Power (physics); Electric power system; Engineering; Statistics; Mathematics; Geography; Geology","score_opus":0.0057495452332528875,"score_gpt":0.1939522811287281,"score_spread":0.1882027358954752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2037194002","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":[0.9381286,0.000040583793,0.00049175083,0.000012422013,0.00012258916,0.00002577645,0.000003492073,0.0000839071,0.061090875],"genre_scores_gemma":[0.9969092,0.000007982005,0.0005968497,0.000011580414,0.000029007335,9.8175676e-8,0.000009547539,0.0000151013355,0.0024206198],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947083,0.000012228045,0.00016845687,0.00010801084,0.0000931386,0.00014730655],"domain_scores_gemma":[0.99973106,0.000050790422,0.000026443417,0.00011899282,0.000019152654,0.000053560805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013729329,0.00010427765,0.0002261626,0.0001279995,0.000044584947,0.000026746142,0.00005857716,0.000052163585,0.00034171317],"category_scores_gemma":[0.000026818341,0.00009429831,0.000057472673,0.00029182233,0.000018589257,0.00013686142,0.00002634758,0.000051080915,0.00000893048],"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":[0.000036209854,0.00004649494,0.16070823,0.0001602395,0.00363363,0.0000027319363,0.0022559327,0.712641,0.11097111,0.0014023316,0.0011646579,0.0069774413],"study_design_scores_gemma":[0.0011000502,0.00024192246,0.11093663,0.00019832229,0.0012484733,0.000013275192,0.00025415732,0.8006072,0.067134,0.00036901762,0.01682275,0.0010742038],"about_ca_topic_score_codex":0.000010545666,"about_ca_topic_score_gemma":0.000011366309,"teacher_disagreement_score":0.08796622,"about_ca_system_score_codex":0.000007851807,"about_ca_system_score_gemma":0.0000021859416,"threshold_uncertainty_score":0.38453734},"labels":[],"label_agreement":null},{"id":"W2040414337","doi":"10.1016/j.epsr.2015.03.027","title":"Improved short-term load forecasting using bagged neural networks","year":2015,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":173,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Computer science; Term (time); Range (aeronautics); Set (abstract data type); Artificial intelligence; Machine learning; Data set; Process (computing); Electrical load; Training set; Sampling (signal processing); Data mining; Engineering; Voltage","score_opus":0.10097262599695728,"score_gpt":0.3113299849015657,"score_spread":0.21035735890460844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2040414337","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":[0.91440016,0.01619167,0.04376848,0.000011558049,0.003834314,0.0007585983,0.0000046958226,0.00073553936,0.020295005],"genre_scores_gemma":[0.9985239,0.000025628642,0.00012147267,0.0000056179942,0.00070272136,0.00005445302,0.0000084704,0.00013150595,0.00042623072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.995783,0.00032517538,0.0005878999,0.00044674947,0.0010666952,0.001790531],"domain_scores_gemma":[0.9980736,0.00029035483,0.00004879022,0.0004902746,0.0005690086,0.0005280023],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0029757286,0.00034138834,0.00042617222,0.0004754463,0.00028245797,0.0003498377,0.0005085447,0.00026757136,0.00001269805],"category_scores_gemma":[0.0002851367,0.0003294659,0.000102417216,0.0017823435,0.000049939536,0.00032420337,0.00013817775,0.0012327321,0.000014199582],"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":[0.0001429518,0.00008045237,0.004338809,0.0002898443,0.00028990075,0.0004073489,0.0016548764,0.9224864,0.041958854,0.00015968109,0.0070203138,0.02117052],"study_design_scores_gemma":[0.00036338938,0.00017548892,0.000036410576,0.000115110255,0.000009156276,0.0002401637,0.000121693236,0.9961034,0.0008021267,0.0000070735764,0.0016729084,0.00035305467],"about_ca_topic_score_codex":0.00040585242,"about_ca_topic_score_gemma":0.000040937553,"teacher_disagreement_score":0.08412376,"about_ca_system_score_codex":0.0009751271,"about_ca_system_score_gemma":0.00023804248,"threshold_uncertainty_score":0.9999157},"labels":[],"label_agreement":null},{"id":"W2045085958","doi":"10.1109/pes.2006.1709373","title":"One day ahead prediction of wind speed using annual trends","year":2006,"lang":"en","type":"article","venue":"2006 IEEE Power Engineering Society General Meeting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":62,"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":"Wind speed; Wind power; Computer science; Interval (graph theory); Time series; Meteorology; Data set; Set (abstract data type); Wind power forecasting; Prediction interval; Power (physics); Electric power system; Machine learning; Artificial intelligence; Engineering; Mathematics","score_opus":0.010279437064976858,"score_gpt":0.19916512096832706,"score_spread":0.1888856839033502,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045085958","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":[0.9762598,0.00084370404,0.015572507,0.0000112308635,0.0024999387,0.00007981995,0.0001921923,0.0007222573,0.0038185061],"genre_scores_gemma":[0.9760329,0.000020752905,0.021890463,0.00001315437,0.0014076177,0.0000018212583,0.000064573906,0.0001412182,0.0004274875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979795,0.000025028696,0.00064869487,0.00031110906,0.00036841427,0.0006672105],"domain_scores_gemma":[0.9993879,0.0000655151,0.000105714316,0.00025543928,0.000083676605,0.00010176455],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044205599,0.00037524346,0.00040238834,0.00015346245,0.00010441615,0.000039083214,0.00017574606,0.00024810238,0.000027846565],"category_scores_gemma":[0.00001852714,0.00046127645,0.0003099284,0.00060084346,0.00004115922,0.00026247505,0.000032425367,0.00033064175,0.0000030260462],"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":[0.0000014286603,0.000021581938,0.0006039254,0.00006425738,0.0000703578,0.0000013821228,0.00034329869,0.6819482,0.31511408,0.000041130214,0.0014882562,0.0003021363],"study_design_scores_gemma":[0.0004917426,0.000039579354,0.002859718,0.0002818599,0.00006877844,0.000012037387,0.000060995775,0.90317315,0.08997571,0.000009626006,0.0025369143,0.00048991543],"about_ca_topic_score_codex":0.00019109376,"about_ca_topic_score_gemma":0.0000051437614,"teacher_disagreement_score":0.22513837,"about_ca_system_score_codex":0.00016779639,"about_ca_system_score_gemma":0.00001842648,"threshold_uncertainty_score":0.9997839},"labels":[],"label_agreement":null},{"id":"W2046820006","doi":"10.1109/epec.2013.6802948","title":"A hybrid genetic radial basis function network with fuzzy corrector for short term load forecasting","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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","funders":"","keywords":"Computer science; Radial basis function; Term (time); Electric power system; Artificial neural network; Genetic algorithm; Adaptive neuro fuzzy inference system; Fuzzy logic; Scheduling (production processes); Mathematical optimization; Radial basis function network; Artificial intelligence; Fuzzy control system; Power (physics); Machine learning; Mathematics","score_opus":0.012768681815632744,"score_gpt":0.18052076330370379,"score_spread":0.16775208148807105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046820006","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":[0.7869443,0.00026708498,0.18386531,0.00001310059,0.0017781648,0.00053929206,0.0000069166767,0.00061192125,0.025973858],"genre_scores_gemma":[0.98118734,0.000009208611,0.016479595,0.0000668322,0.0013495799,0.00022799721,0.000025710877,0.00008197597,0.0005717857],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883205,0.0000098725495,0.00024529092,0.00023391537,0.00014886408,0.0005300146],"domain_scores_gemma":[0.9994707,0.000115599505,0.000026347705,0.00017619129,0.000079607016,0.00013151634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009128984,0.00023851302,0.00020676323,0.000046478606,0.00012859853,0.000091454305,0.00009934239,0.00006055668,0.00022865251],"category_scores_gemma":[0.000017305374,0.00019819358,0.000078775556,0.00012499266,0.000021392661,0.0001814377,0.000018093795,0.00011542349,0.000028624578],"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":[0.000095807656,0.000023214729,0.021379618,0.00015168458,0.00022904821,0.000010101833,0.0001458912,0.5560565,0.0012914756,0.00013633653,0.033101447,0.3873789],"study_design_scores_gemma":[0.00084824744,0.00044934454,0.010094737,0.00017899391,0.00012152597,0.00012474737,0.00003964577,0.9734056,0.002163978,0.00036090222,0.01143915,0.00077311887],"about_ca_topic_score_codex":0.00007132156,"about_ca_topic_score_gemma":0.00014454524,"teacher_disagreement_score":0.41734913,"about_ca_system_score_codex":0.00007945254,"about_ca_system_score_gemma":0.000026725646,"threshold_uncertainty_score":0.80820996},"labels":[],"label_agreement":null},{"id":"W2048321766","doi":"10.1109/ccece.2006.277728","title":"Prediction of Ontario Hourly Load Demands and Neural Network Modeling Techniques","year":2006,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"McMaster University","funders":"","keywords":"Artificial neural network; Computer science; Levenberg–Marquardt algorithm; Energy (signal processing); Machine learning; Artificial intelligence; Statistics","score_opus":0.012009580353500535,"score_gpt":0.17646412991660035,"score_spread":0.16445454956309982,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048321766","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":[0.89401084,0.00034287648,0.060563885,0.0000041027893,0.00012833173,0.00003859284,0.0000019488778,0.0003737456,0.044535697],"genre_scores_gemma":[0.9929821,0.000011186999,0.0064625307,0.0000061331034,0.00017251643,0.0000031514467,0.000007051376,0.000012144469,0.00034319473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957484,0.0000030987362,0.00015725153,0.00006945574,0.00006888188,0.00012648563],"domain_scores_gemma":[0.9998805,0.000007956528,0.000012709555,0.00005973223,0.000019382464,0.000019709865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006929605,0.00007597316,0.000089970395,0.000022770208,0.000028042416,0.000011934913,0.000028739525,0.000052599546,0.000012779646],"category_scores_gemma":[0.0000011636309,0.000069979345,0.000021280934,0.000050734416,0.000008324906,0.00009195727,0.000011406335,0.00007736198,1.7461073e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000028250222,0.0000033251088,0.014208971,0.000014797286,0.0000069561006,7.4158686e-7,0.00008949053,0.98135144,0.00072302826,0.0004014451,0.00045137256,0.002745601],"study_design_scores_gemma":[0.00007948889,0.000027909555,0.0013646266,0.00004151078,0.000009684727,0.0000073087685,0.0000039504953,0.99461496,0.0022622873,0.0005385908,0.0009749398,0.00007477014],"about_ca_topic_score_codex":0.011009469,"about_ca_topic_score_gemma":0.018490057,"teacher_disagreement_score":0.09897127,"about_ca_system_score_codex":0.00005345706,"about_ca_system_score_gemma":0.00001305736,"threshold_uncertainty_score":0.9994199},"labels":[],"label_agreement":null},{"id":"W2048340369","doi":"10.1002/etep.463","title":"A new hybrid iterative method for short-term wind speed forecasting","year":2010,"lang":"en","type":"article","venue":"European Transactions on Electrical Power","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":43,"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 Calgary","funders":"","keywords":"Wind power forecasting; Wind power; Wind speed; Term (time); Computer science; Electric power system; Power (physics); Meteorology; Engineering; Electrical engineering; Geography","score_opus":0.01784632786349705,"score_gpt":0.24581229370592897,"score_spread":0.22796596584243192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2048340369","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":[0.026215311,0.000042576758,0.9342698,0.00004722237,0.0010250687,0.00024730223,0.00002715028,0.00044959632,0.037675988],"genre_scores_gemma":[0.9607208,0.000005091951,0.036576092,0.00010592504,0.0003582942,0.0000031296947,0.000013966368,0.00013941243,0.0020773413],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986195,0.00006562457,0.00033152918,0.00032790095,0.00015628894,0.0004991606],"domain_scores_gemma":[0.99913824,0.000305774,0.000028554143,0.00023556827,0.000048380138,0.00024350635],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027015238,0.00028690606,0.00023676612,0.0001920829,0.00019798802,0.000092772294,0.00019657504,0.000060549075,0.00035622227],"category_scores_gemma":[0.000038792954,0.0002770053,0.00021505814,0.00030252314,0.00001768448,0.00015685457,0.000002715371,0.0007851608,0.00005511583],"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":[0.00025112132,0.00024985344,0.000050569917,0.000048124337,0.00041580116,0.00014308639,0.0013604868,0.119748995,0.1747622,0.0009820176,0.010170179,0.6918176],"study_design_scores_gemma":[0.00228138,0.0012505169,0.00052032206,0.00012642509,0.00020538895,0.0004483131,0.000025446929,0.6620619,0.17680645,0.0004208405,0.15409255,0.0017604141],"about_ca_topic_score_codex":0.0000030496917,"about_ca_topic_score_gemma":0.000007249315,"teacher_disagreement_score":0.93450546,"about_ca_system_score_codex":0.000039498394,"about_ca_system_score_gemma":0.000023886168,"threshold_uncertainty_score":0.99996823},"labels":[],"label_agreement":null},{"id":"W2049171089","doi":"10.1109/iraniancee.2014.6999606","title":"A hybrid model for wind power prediction composed of ANN and imperialist competitive algorithm (ICA)","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"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":"Imperialist competitive algorithm; Wind power; Artificial neural network; Wind power forecasting; Electric power system; Computer science; Wind speed; Power (physics); Algorithm; Engineering; Artificial intelligence; Particle swarm optimization; Meteorology; Electrical engineering; Physics","score_opus":0.008550755367792501,"score_gpt":0.1963092738759098,"score_spread":0.18775851850811728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049171089","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":[0.25648645,0.000052355903,0.72333777,0.000019013154,0.00040032848,0.00015575282,0.00027088056,0.00018586057,0.019091617],"genre_scores_gemma":[0.9804261,0.0000072510597,0.019270286,0.000021857026,0.000081048565,0.0000052781575,0.000046353776,0.000020747902,0.0001210794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995208,0.000008118623,0.00016476684,0.00010843041,0.00006502298,0.00013290584],"domain_scores_gemma":[0.9997409,0.000058304544,0.000023179411,0.000083835956,0.000043709082,0.0000500825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009958507,0.00009921958,0.0001568808,0.000038800907,0.00003753548,0.000015233641,0.00004302198,0.0000361144,0.000014062888],"category_scores_gemma":[0.000011607203,0.000093895134,0.00003603377,0.000026632839,0.00003629172,0.00007759778,0.000014508632,0.000044212906,5.850901e-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":[0.00015732748,0.00016242558,0.0009000615,0.0005149554,0.00039184748,0.0000025982197,0.0033192558,0.7604966,0.11076534,0.073739044,0.0050903223,0.044460196],"study_design_scores_gemma":[0.0005582114,0.00007956896,0.00016197214,0.000028824164,0.000011606728,0.000004834862,0.00002710716,0.98722684,0.009418503,0.00038254485,0.002002637,0.000097346325],"about_ca_topic_score_codex":0.000010305365,"about_ca_topic_score_gemma":0.0000046383407,"teacher_disagreement_score":0.72393966,"about_ca_system_score_codex":0.000011086472,"about_ca_system_score_gemma":0.0000050961585,"threshold_uncertainty_score":0.38289326},"labels":[],"label_agreement":null},{"id":"W2050849598","doi":"10.1134/s0040601512110080","title":"Changing pattern and amount of the residential and commercial energy consumption in response to economic and climatic factors","year":2012,"lang":"en","type":"article","venue":"Thermal Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"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":"Energy consumption; Consumption (sociology); Economic sector; Business; Natural resource economics; Energy sector; Climate change; Energy conservation; Environmental protection; Agricultural economics; Economy; Environmental science; Geography; Economics; Engineering; Ecology","score_opus":0.0105542547105938,"score_gpt":0.19965328724942918,"score_spread":0.1890990325388354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050849598","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":[0.99860543,0.00038874644,0.0006685788,0.0000111744885,0.00022320889,0.00004225333,0.0000050100643,0.000030252191,0.000025336582],"genre_scores_gemma":[0.9998272,0.000028503244,0.000047199872,0.000009578431,0.000058133395,0.0000043521545,7.7552176e-7,0.000020488222,0.0000037899706],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99952644,0.000021428865,0.00014109956,0.00006369981,0.000037998216,0.00020933815],"domain_scores_gemma":[0.99975723,0.00009592133,0.000017197248,0.00007112205,0.0000019161896,0.000056619057],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020188013,0.000103003964,0.00012287781,0.0001222791,0.000027155986,0.000014282336,0.000040752984,0.000038895145,0.0000068957684],"category_scores_gemma":[0.000012072508,0.000090782945,0.000012941216,0.000041943757,0.00001633964,0.000108186076,0.000065948145,0.00006889806,4.1850828e-7],"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":[0.000109690074,0.000015173575,0.40588117,0.0005106453,0.000073380514,0.000002767871,0.023015063,0.3216814,0.22357635,0.00064958335,0.0000061799437,0.024478598],"study_design_scores_gemma":[0.00030470637,0.000016549368,0.9073226,0.00019768835,0.000013035412,0.00001030431,0.00012262964,0.07781276,0.01390738,0.0000021302849,0.00009732689,0.00019292261],"about_ca_topic_score_codex":0.000066889595,"about_ca_topic_score_gemma":0.000042605916,"teacher_disagreement_score":0.50144136,"about_ca_system_score_codex":0.00003012132,"about_ca_system_score_gemma":0.000001944552,"threshold_uncertainty_score":0.37020212},"labels":[],"label_agreement":null},{"id":"W2052741429","doi":"10.1109/icsssm.2011.5959472","title":"Electricity load forecasting based on weather variables and seasonalities: A neural network approach","year":2011,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Nonlinear autoregressive exogenous model; Artificial neural network; Benchmark (surveying); Feedforward neural network; Linear regression; Autoregressive model; Recurrent neural network; Electricity; Mean absolute percentage error; Computer science; Econometrics; Regression analysis; Feed forward; Weather forecasting; Time series; Statistics; Meteorology; Mathematics; Artificial intelligence; Engineering; Geography","score_opus":0.030421095946729912,"score_gpt":0.18404406606172014,"score_spread":0.1536229701149902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052741429","genre_codex":"other","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":[0.18969762,0.00040404062,0.056889847,0.0000067392025,0.0002394521,0.00013167669,0.0000044323933,0.0006347806,0.7519914],"genre_scores_gemma":[0.98185563,0.0000053636504,0.017286956,0.00016048615,0.00019976238,0.000015697826,0.000004802502,0.00003989746,0.00043140247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990934,0.000026226302,0.0001486001,0.00018680152,0.00013704166,0.00040790468],"domain_scores_gemma":[0.99961555,0.00011759213,0.000021405509,0.00013266488,0.00002278197,0.00008998316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022178261,0.00018934092,0.00015961975,0.0000360116,0.000090283545,0.0000337646,0.000086258115,0.000077375764,0.00014777585],"category_scores_gemma":[0.000025504993,0.0001624144,0.000041841806,0.00019345991,0.000024005481,0.00008685354,0.000017842971,0.00016470972,0.00000266683],"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":[0.00009314622,0.00008825102,0.01594098,0.00022479624,0.000102266815,0.000018290608,0.0014664846,0.944322,0.00009493058,0.016099809,0.0025635688,0.018985497],"study_design_scores_gemma":[0.00022294803,0.000057165704,0.0004514532,0.000039405008,0.000012576361,0.000011328852,0.000032906093,0.99757814,0.00021845214,0.000357191,0.0008082387,0.00021019432],"about_ca_topic_score_codex":0.00008776885,"about_ca_topic_score_gemma":0.000014225508,"teacher_disagreement_score":0.792158,"about_ca_system_score_codex":0.000035507008,"about_ca_system_score_gemma":0.00001654267,"threshold_uncertainty_score":0.6623067},"labels":[],"label_agreement":null},{"id":"W2060261774","doi":"10.1049/iet-gtd.2009.0297","title":"Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network","year":2010,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":141,"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":"Volatility (finance); Electricity price forecasting; Electricity; Electricity market; Artificial neural network; Computer science; Electricity price; Feature selection; Econometrics; SIGNAL (programming language); Economics; Machine learning; Engineering","score_opus":0.011527775071848238,"score_gpt":0.2028623232543782,"score_spread":0.19133454818252996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060261774","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":[0.30112246,0.0006023193,0.69668305,0.000103124126,0.0006485924,0.00014179003,0.00011947085,0.00028206207,0.00029715724],"genre_scores_gemma":[0.9919168,0.00017914057,0.0043036845,0.000044921515,0.0007574495,0.000024509736,0.0026679651,0.000035817964,0.00006974079],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859023,0.00005374975,0.00037900466,0.00031522708,0.00022189002,0.00043989503],"domain_scores_gemma":[0.9994476,0.000056357447,0.00006402952,0.0001537964,0.0000735393,0.00020470694],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034925598,0.00025453628,0.00019160185,0.000034294302,0.00039386092,0.00011978202,0.00009513392,0.00016606935,0.00003623165],"category_scores_gemma":[0.000031337848,0.00025335664,0.000062730775,0.00025953882,0.000030917974,0.00028234546,0.000011070184,0.00045207812,0.0000024038475],"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":[0.000015417529,0.00004537152,0.00007041898,0.000039111623,0.000024717943,0.0000061866813,0.00008916012,0.16858214,0.26596734,0.00038460124,0.02735821,0.53741735],"study_design_scores_gemma":[0.00034994807,0.000044026936,0.00011474323,0.000019626796,0.000019447287,0.000035086727,0.0000020710809,0.89205325,0.080351144,0.00012132253,0.026626354,0.00026297287],"about_ca_topic_score_codex":0.000019487385,"about_ca_topic_score_gemma":0.000006621376,"teacher_disagreement_score":0.7234711,"about_ca_system_score_codex":0.000044603054,"about_ca_system_score_gemma":0.000020208425,"threshold_uncertainty_score":0.9999919},"labels":[],"label_agreement":null},{"id":"W2060606400","doi":"10.1109/naps.2010.5619586","title":"A review of wind power and wind speed forecasting methods with different time horizons","year":2010,"lang":"en","type":"review","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":873,"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 Calgary","funders":"","keywords":"Intermittency; Wind power; Wind power forecasting; Wind speed; Renewable energy; Meteorology; Computer science; Artificial neural network; Electric power system; Power (physics); Environmental science; Engineering; Artificial intelligence; Electrical engineering; Geography","score_opus":0.03647778405806498,"score_gpt":0.30083237764870274,"score_spread":0.26435459359063773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060606400","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000035621302,0.9722255,0.00047332345,0.000003825032,0.0003270146,0.0005293333,0.000029090063,0.00014101212,0.026235307],"genre_scores_gemma":[0.0000039307647,0.98688847,0.01207908,0.00001911489,0.00012034965,0.0000047373596,0.000054798587,0.00015571208,0.00067382876],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99808145,0.00012619306,0.00083205424,0.00037365453,0.00018154373,0.0004051203],"domain_scores_gemma":[0.99854213,0.00048395243,0.0002722109,0.00046406162,0.000058611142,0.0001790256],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052016985,0.00070655096,0.0025649848,0.00017858879,0.000048526083,0.00003422254,0.00024428198,0.0003470802,0.00044334715],"category_scores_gemma":[0.000107254054,0.00044687363,0.0003312247,0.00032980792,0.00007762778,0.000083486615,0.00010211425,0.00077370176,0.000008851859],"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":[0.0000010145128,0.000011108978,0.0000013594095,0.103139825,0.00022791346,0.0000063186735,0.00002375365,0.000009833866,0.000011066882,0.00005429536,0.00032950615,0.896184],"study_design_scores_gemma":[0.00009704138,0.0000859055,6.3300797e-7,0.17488979,0.00076261087,0.00021147954,0.0000021009535,0.00034730442,0.000017179742,0.000006979926,0.8230867,0.00049228943],"about_ca_topic_score_codex":0.0000043361188,"about_ca_topic_score_gemma":0.000003462419,"teacher_disagreement_score":0.8956917,"about_ca_system_score_codex":0.000032696993,"about_ca_system_score_gemma":0.000050719354,"threshold_uncertainty_score":0.9997983},"labels":[],"label_agreement":null},{"id":"W2061507872","doi":"10.1016/j.ijepes.2014.05.037","title":"Mid-term electricity market clearing price forecasting utilizing hybrid support vector machine and auto-regressive moving average with external input","year":2014,"lang":"en","type":"article","venue":"International Journal of Electrical Power & Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Support vector machine; Electricity market; Electricity; Term (time); Electricity price forecasting; Least squares support vector machine; Computer science; Market clearing; Scheduling (production processes); Engineering; Artificial intelligence; Economics; Operations management","score_opus":0.0075330672519699935,"score_gpt":0.20408174127548565,"score_spread":0.19654867402351564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061507872","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":[0.7310112,0.0029093912,0.24477242,0.00006014613,0.0031482202,0.00011233405,0.000012930202,0.00019327048,0.017780043],"genre_scores_gemma":[0.99789727,0.00012977657,0.000597117,0.000077792334,0.00094522996,0.000005831977,0.0000060722778,0.00008333716,0.00025758633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971236,0.00014354773,0.0009396361,0.00030629776,0.00090104534,0.00058590237],"domain_scores_gemma":[0.9981156,0.00045220638,0.0005833501,0.0001604775,0.00039614073,0.00029223107],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006974332,0.00039136325,0.0005515992,0.0005376453,0.00012343196,0.00029119587,0.00053658884,0.00011692031,0.00005098035],"category_scores_gemma":[0.00024595734,0.00032651867,0.00015162758,0.00027707798,0.000044215667,0.00051627826,0.00008717867,0.0006572791,0.0000015078409],"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":[0.0039790412,0.00081766007,0.09540489,0.0005675427,0.0068223546,0.009800676,0.0021322046,0.44667974,0.15403947,0.020732235,0.004515426,0.25450876],"study_design_scores_gemma":[0.0023351982,0.0009116328,0.0040080845,0.0012788848,0.00008729163,0.011637745,0.000023384006,0.9534976,0.013764227,0.00015769989,0.011471815,0.00082644995],"about_ca_topic_score_codex":0.00010027053,"about_ca_topic_score_gemma":0.000011965144,"teacher_disagreement_score":0.5068178,"about_ca_system_score_codex":0.00033230658,"about_ca_system_score_gemma":0.000075120784,"threshold_uncertainty_score":0.9999187},"labels":[],"label_agreement":null},{"id":"W2062147311","doi":"10.4018/ijamc.2014070102","title":"A Fuzzy Model with Thermodynamic Based Consequents and a Niching Swarm-Based Supervisor to Capture the Uncertainties of Damavand Power System","year":2014,"lang":"en","type":"article","venue":"International Journal of Applied Metaheuristic Computing","topic":"Energy Load and Power Forecasting","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 Waterloo","funders":"","keywords":"Computer science; Particle swarm optimization; Fuzzy logic; Metaheuristic; Robustness (evolution); Artificial intelligence; Supervisor; Mathematical optimization; Machine learning; Mathematics","score_opus":0.006589619167793012,"score_gpt":0.2005663898448511,"score_spread":0.19397677067705807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062147311","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":[0.6056889,0.000072290735,0.3904982,0.00007772747,0.0002462894,0.000073134484,0.000007060629,0.000027027527,0.0033093414],"genre_scores_gemma":[0.9862852,0.0000010322684,0.013445374,0.00014249596,0.00008895809,0.0000019874813,0.0000025790666,0.000028980112,0.0000033701251],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987704,0.000035081874,0.00048309777,0.00012291856,0.00042315188,0.00016533531],"domain_scores_gemma":[0.9988764,0.0004496908,0.00023868369,0.00012301837,0.00023490329,0.00007733827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060127233,0.00017966217,0.00030669954,0.00018136436,0.000062978615,0.000069349044,0.00033681915,0.000040098977,0.000001942281],"category_scores_gemma":[0.000056696266,0.00011985457,0.0000681241,0.00009578437,0.000054346816,0.000037451737,0.000040655927,0.00023530592,4.861817e-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":[0.0001387761,0.000013245411,0.00014676963,0.000070176015,0.0001795995,0.000008890278,0.00069682853,0.9886491,0.0033709116,0.0053311284,0.000015898038,0.0013787047],"study_design_scores_gemma":[0.0008393705,0.000050122526,0.00013313681,0.00052436045,0.00006797184,0.000050446895,0.00041649822,0.9964818,0.0009797425,0.0002548949,0.00006843891,0.0001332485],"about_ca_topic_score_codex":0.000018524495,"about_ca_topic_score_gemma":0.000009693591,"teacher_disagreement_score":0.38059628,"about_ca_system_score_codex":0.000089770794,"about_ca_system_score_gemma":0.000063632804,"threshold_uncertainty_score":0.48875275},"labels":[],"label_agreement":null},{"id":"W2063626393","doi":"10.1016/j.jweia.2007.02.026","title":"Erratum to “The r largest order statistics model for extreme wind speed estimation” [J. Wind Eng. Ind. Aerodyn. 95(3), 165–182]","year":2007,"lang":"en","type":"erratum","venue":"Journal of Wind Engineering and Industrial Aerodynamics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wind speed; Statistics; Meteorology; Estimation; Order (exchange); Mathematics; Environmental science; Geography; Engineering; Economics","score_opus":0.030706670386294863,"score_gpt":0.23367201893732714,"score_spread":0.20296534855103227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063626393","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0364921,0.004857977,0.80793184,0.0007391782,0.1431444,0.00164871,0.0025660698,0.00040835992,0.0022113738],"genre_scores_gemma":[0.26383054,0.009693208,0.40186867,0.0013734484,0.15062416,0.000038834067,0.007916231,0.0054291347,0.15922578],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961766,0.000037495407,0.0016488739,0.00040262114,0.00075606816,0.0009783173],"domain_scores_gemma":[0.9975104,0.00038987788,0.0005369383,0.00043484775,0.0005783726,0.00054958183],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0014470514,0.0009172387,0.001171193,0.0006146112,0.00023956338,0.0003256609,0.0006312012,0.0014961315,0.00001397989],"category_scores_gemma":[0.0012638461,0.0008097716,0.00023021667,0.00073563115,0.00005758166,0.000337536,0.00010632996,0.0037057379,0.000004309287],"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":[0.000051334515,0.000024453784,0.000011381022,0.00015429879,0.00025072548,0.000021689017,0.00023423057,0.70361865,0.00006184621,0.000105915766,0.29172772,0.0037377444],"study_design_scores_gemma":[0.0011731779,0.00022581225,0.000053955377,0.0008439627,0.00029390803,0.000118454715,0.00004769635,0.85803825,0.00000959967,0.000055303124,0.13837965,0.0007602361],"about_ca_topic_score_codex":0.000019990504,"about_ca_topic_score_gemma":0.00006369766,"teacher_disagreement_score":0.40606314,"about_ca_system_score_codex":0.0003883121,"about_ca_system_score_gemma":0.00047445382,"threshold_uncertainty_score":0.99980015},"labels":[],"label_agreement":null},{"id":"W2066269320","doi":"10.1016/j.energy.2012.10.019","title":"Electricity demand estimation using an adaptive neuro-fuzzy network: A case study from the Ontario province – Canada","year":2012,"lang":"en","type":"article","venue":"Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":111,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Adaptive neuro fuzzy inference system; Electricity; Electricity demand; Neuro-fuzzy; Gross domestic product; Econometrics; Fuzzy logic; Electricity market; Estimation; Artificial neural network; Asset (computer security); Renewable energy; Computer science; Electricity generation; Economics; Engineering; Fuzzy control system; Artificial intelligence; Power (physics); Economic growth","score_opus":0.020816444497474444,"score_gpt":0.20535019457178524,"score_spread":0.1845337500743108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066269320","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":[0.97580826,0.00030902302,0.022524705,0.0000047362246,0.00055298256,0.00007618199,0.000004132928,0.00007883493,0.0006411352],"genre_scores_gemma":[0.9982129,0.0000012092852,0.0010840303,0.000109245644,0.00050290715,0.0000092902,0.000011995873,0.000028004957,0.00004042831],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990755,0.00009246398,0.00016483566,0.00013997313,0.00014540863,0.00038182308],"domain_scores_gemma":[0.9994524,0.0001541021,0.000043457825,0.00020332573,0.000017072436,0.00012960171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013681028,0.00016040514,0.00012795566,0.000016318227,0.0002452524,0.00003553471,0.00010676327,0.000038611724,0.000017083461],"category_scores_gemma":[0.000016658223,0.00013113797,0.000019809702,0.0001402473,0.000009348545,0.00023625925,0.00002991993,0.00016352667,3.1908135e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000007280242,0.00003295413,0.009367889,0.0000010557216,0.000039944836,0.00021678669,0.0012338606,0.98504984,0.000036376026,0.00017552258,0.0002906534,0.0035478184],"study_design_scores_gemma":[0.0002563735,0.00009092501,0.0050485493,0.000014436653,0.00007521965,0.00032329786,0.00037312845,0.98908186,0.00034758405,0.000078437886,0.003990906,0.00031925752],"about_ca_topic_score_codex":0.98905396,"about_ca_topic_score_gemma":0.9983643,"teacher_disagreement_score":0.02240462,"about_ca_system_score_codex":0.0003577761,"about_ca_system_score_gemma":0.00021286072,"threshold_uncertainty_score":0.5347651},"labels":[],"label_agreement":null},{"id":"W2070960910","doi":"10.1016/j.apenergy.2013.08.025","title":"Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach","year":2013,"lang":"en","type":"article","venue":"Applied Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":307,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Kalman filter; Wind speed; Control theory (sociology); Autoregressive model; Support vector machine; State vector; State-space representation; Extended Kalman filter; State space; Computer science; Wind power; Machine learning; Engineering; Artificial intelligence; Algorithm; Mathematics; Statistics; Meteorology; Control (management)","score_opus":0.019745248983319193,"score_gpt":0.2147096499951622,"score_spread":0.194964401011843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070960910","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":[0.95639634,0.000033339988,0.02921606,0.0000075394646,0.00060647033,0.00017610732,0.000023301476,0.00071875646,0.01282206],"genre_scores_gemma":[0.99603313,0.000011459055,0.002572088,0.000050215887,0.00030864045,0.00001644272,0.00063907023,0.00011987058,0.0002490549],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998456,0.000027985849,0.0003320616,0.000393918,0.00027010063,0.0005199237],"domain_scores_gemma":[0.99923664,0.000022625542,0.000052688996,0.00041244406,0.000040448365,0.00023515346],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000090177244,0.00035084976,0.0002653446,0.00015769931,0.00012574397,0.000087398126,0.00018599904,0.00017828653,0.00024402475],"category_scores_gemma":[0.0000027493286,0.00032496316,0.00006607388,0.00024110507,0.000041318974,0.0002872498,0.00003759172,0.00019418666,0.000014586361],"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":[0.000026827733,0.00011654034,0.00093275734,0.00006373405,0.000050723942,0.0000067930146,0.00024509602,0.75663215,0.23451194,0.00024757755,0.0014773041,0.0056885737],"study_design_scores_gemma":[0.00065053825,0.000069960704,0.002956049,0.00007113055,0.00003495152,0.000011425406,0.00007537707,0.88052696,0.11240284,0.00005276357,0.002597336,0.000550653],"about_ca_topic_score_codex":0.00012868327,"about_ca_topic_score_gemma":0.000013901072,"teacher_disagreement_score":0.12389485,"about_ca_system_score_codex":0.00010620675,"about_ca_system_score_gemma":0.000023316974,"threshold_uncertainty_score":0.99992025},"labels":[],"label_agreement":null},{"id":"W2071085029","doi":"10.1109/ccece.2012.6334847","title":"A new strategy for wind speed forecasting using hybrid intelligent models","year":2012,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Wind speed; Wind power; Wind power forecasting; Computer science; Artificial neural network; Fuzzy logic; Wavelet transform; Mean squared error; Power (physics); Electric power system; Data mining; Real-time computing; Meteorology; Wavelet; Artificial intelligence; Engineering; Mathematics; Statistics","score_opus":0.12012866498680375,"score_gpt":0.26961325868860686,"score_spread":0.14948459370180311,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071085029","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":[0.26884848,0.0005231793,0.7065634,0.0000039291235,0.00087427313,0.00015990346,0.00000744704,0.0002511286,0.022768244],"genre_scores_gemma":[0.95694864,0.0000066982934,0.04147574,0.000021115504,0.0007766598,9.788324e-7,0.00001229842,0.000060866823,0.00069700455],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895805,0.0000060832995,0.00025291723,0.000122052086,0.00009656973,0.00056432385],"domain_scores_gemma":[0.9995398,0.00006641266,0.000028946451,0.00012621522,0.000024452638,0.00021417376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001522833,0.00018822908,0.00016850232,0.00007277597,0.00006843854,0.000049097835,0.00009743272,0.000051346648,0.00010668136],"category_scores_gemma":[0.0000134034735,0.00017982416,0.00008823251,0.000094011615,0.000007798208,0.0004450865,0.000025354077,0.00009682676,0.0000068914665],"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":[0.000005193308,0.0000075134794,0.00009012346,0.00004081851,0.00003415573,9.655857e-7,0.00022600825,0.9606398,0.0014536199,0.002319755,0.0007751121,0.034406967],"study_design_scores_gemma":[0.00016796561,0.000019902258,0.0000028008328,0.000043390344,0.000021421849,0.000039404884,0.00013359044,0.97316056,0.0225364,0.0014187053,0.0022174832,0.00023838127],"about_ca_topic_score_codex":0.00008032968,"about_ca_topic_score_gemma":0.00000827177,"teacher_disagreement_score":0.68810016,"about_ca_system_score_codex":0.00006074206,"about_ca_system_score_gemma":0.00002345246,"threshold_uncertainty_score":0.73330164},"labels":[],"label_agreement":null},{"id":"W2071932886","doi":"10.1109/ccece.2013.6567685","title":"A comparison between SVM and LSSVM in mid-term electricity market clearing price forecasting","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":15,"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 Saskatchewan","funders":"","keywords":"Electricity market; Support vector machine; Electricity; Bidding; Term (time); Computer science; Scheduling (production processes); Clearing; Least squares support vector machine; Market clearing; Operations research; Business; Economics; Operations management; Artificial intelligence; Microeconomics; Engineering; Finance","score_opus":0.024961660437923375,"score_gpt":0.2317348619396968,"score_spread":0.20677320150177342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2071932886","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":[0.91839266,0.00018377585,0.002206548,0.000015999836,0.00007302139,0.0001270867,8.304553e-7,0.00022835401,0.07877174],"genre_scores_gemma":[0.9971505,0.000017522436,0.0023952683,0.000017675278,0.00010040897,0.000014998721,0.0000038899584,0.000033324643,0.00026646015],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988896,0.000024738965,0.0003336011,0.00019486307,0.00010729689,0.00044987307],"domain_scores_gemma":[0.99950707,0.00021342574,0.000036600723,0.00011735611,0.000019057243,0.00010648445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020674462,0.00017913112,0.00027255964,0.00015292133,0.000060159087,0.000092469585,0.00011177406,0.00009592931,0.000183141],"category_scores_gemma":[0.00004533274,0.00017576542,0.000030104306,0.00029332534,0.000016701264,0.0002619489,0.0000629602,0.00028577494,0.000014562669],"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":[0.0000040657956,0.000015383992,0.9061059,0.00013432073,0.000033419772,0.0000049400714,0.0004965634,0.002880627,0.002156024,0.000060281214,0.0008358623,0.087272614],"study_design_scores_gemma":[0.0004476149,0.000042302712,0.46251488,0.00013521232,0.000012095868,0.000011905327,0.00008655138,0.5298451,0.0056085708,0.00014634061,0.0007532141,0.00039620273],"about_ca_topic_score_codex":0.0002886227,"about_ca_topic_score_gemma":0.000110278634,"teacher_disagreement_score":0.5269645,"about_ca_system_score_codex":0.000055526383,"about_ca_system_score_gemma":0.000005909949,"threshold_uncertainty_score":0.71675056},"labels":[],"label_agreement":null},{"id":"W2074011736","doi":"10.1109/epec.2014.45","title":"Performance Evaluation of New and Advanced Neural Networks for Short Term Load Forecasting","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Term (time); Artificial neural network; Computer science; Artificial intelligence; Machine learning","score_opus":0.03170682173921677,"score_gpt":0.24778202518466896,"score_spread":0.2160752034454522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074011736","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":[0.94735515,0.00022229628,0.043330986,0.0000045314355,0.0002677336,0.00013687141,5.151671e-7,0.00007491321,0.008607022],"genre_scores_gemma":[0.995783,0.000014833647,0.003907165,0.000009542016,0.00017485686,0.000013251737,0.000006881983,0.000018416586,0.00007209494],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994196,0.0000074692875,0.00017145144,0.00009959459,0.00013243197,0.00016940353],"domain_scores_gemma":[0.9997073,0.00006450289,0.000021239523,0.000086591645,0.000071818155,0.00004854826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037255397,0.00009501751,0.00011729197,0.000025420897,0.00003781729,0.000011138146,0.00004780883,0.000041718096,0.000010769239],"category_scores_gemma":[0.000049882936,0.00008739455,0.00002588721,0.000056479774,0.000010621739,0.00014591863,0.000013174598,0.000050089326,1.5294353e-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":[0.000004614164,9.2859426e-7,0.0023700153,0.000023043447,0.000004595375,1.2023072e-8,0.000049133676,0.51391345,0.0003278231,0.000023984394,0.000033351444,0.48324907],"study_design_scores_gemma":[0.00038983647,0.000060770322,0.0030298668,0.000048868373,0.000024217297,0.0000032346761,0.000008992178,0.9942137,0.0018471148,0.000027419424,0.000243631,0.00010234204],"about_ca_topic_score_codex":0.0000038393127,"about_ca_topic_score_gemma":0.000025598316,"teacher_disagreement_score":0.48314673,"about_ca_system_score_codex":0.000021442278,"about_ca_system_score_gemma":0.000009935968,"threshold_uncertainty_score":0.3563846},"labels":[],"label_agreement":null},{"id":"W2074911163","doi":"10.1109/tsg.2014.2377178","title":"Fuzzy Prediction Interval Models for Forecasting Renewable Resources and Loads in Microgrids","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":193,"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":"Microgrid; Renewable energy; Interval (graph theory); Prediction interval; Wind power; Computer science; Fuzzy logic; Probabilistic forecasting; Representation (politics); Mathematical optimization; Energy management system; Electric power system; Electricity generation; Reliability engineering; Data mining; Energy management; Power (physics); Engineering; Energy (signal processing); Machine learning; Artificial intelligence; Mathematics; Statistics","score_opus":0.019622419662299814,"score_gpt":0.2025635714247892,"score_spread":0.18294115176248937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074911163","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":[0.35817912,0.00011122437,0.6355689,0.000030926945,0.0016389973,0.00016423482,0.00006906714,0.00026455,0.0039729904],"genre_scores_gemma":[0.99668884,0.000054528427,0.0026443054,0.000031903997,0.00026653724,0.00008528206,0.000009005128,0.000045786128,0.00017381948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907535,0.000024554804,0.00028598486,0.00022567723,0.0000918174,0.0002966043],"domain_scores_gemma":[0.9995902,0.00014369829,0.000026019123,0.00014032876,0.000023996005,0.00007572687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025252262,0.00017753494,0.00019064956,0.00020393908,0.00013162017,0.00004747635,0.000077114026,0.000111384565,0.0000053064286],"category_scores_gemma":[0.0000051694096,0.00018698398,0.00007429151,0.00015751098,0.000030999014,0.0002324085,0.0000012391596,0.00019237144,0.0000018797214],"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":[0.000044643835,0.000026767857,0.00008298566,0.00008465789,0.000023774353,6.97065e-7,0.00055785326,0.97039825,0.0019938087,0.00001734188,0.00028492504,0.026484324],"study_design_scores_gemma":[0.00073392625,0.00018270151,0.00005210954,0.00022212832,0.00002663415,0.0000212646,0.00007367843,0.97313416,0.016904334,0.0005246955,0.007913858,0.00021052908],"about_ca_topic_score_codex":0.00013720011,"about_ca_topic_score_gemma":0.0005965059,"teacher_disagreement_score":0.6385097,"about_ca_system_score_codex":0.0000500291,"about_ca_system_score_gemma":0.0000060012367,"threshold_uncertainty_score":0.76249856},"labels":[],"label_agreement":null},{"id":"W2076371332","doi":"10.1016/j.enpol.2007.04.006","title":"Electricity market price volatility: The case of Ontario","year":2007,"lang":"en","type":"article","venue":"Energy Policy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":119,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo; University of Calgary","funders":"","keywords":"Volatility (finance); Electricity; Electricity market; Economics; Electricity price; Volatility swap; Volatility smile; Financial economics; Econometrics; Implied volatility; Engineering","score_opus":0.007850362691639337,"score_gpt":0.2157781559530746,"score_spread":0.20792779326143526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076371332","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":[0.6694437,0.00019809564,0.0051763174,0.00003231382,0.00015237274,0.000023861036,0.0000031446573,0.00010266311,0.32486752],"genre_scores_gemma":[0.99453557,0.000010861829,0.00027874936,0.00010848465,0.0003009496,0.0000024293847,0.000002154716,0.000020196569,0.0047406256],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99923205,0.000019197792,0.00023095666,0.00009926216,0.00008019908,0.00033833258],"domain_scores_gemma":[0.9994625,0.00015052398,0.000039145845,0.0002476957,0.000026352687,0.00007379051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003215558,0.0001252888,0.00012771357,0.00011973086,0.00007262109,0.000011327408,0.00013214632,0.00007056319,0.00012760978],"category_scores_gemma":[0.000050652445,0.000097764,0.00006293236,0.00042408204,0.000031772877,0.000058224578,0.000029006884,0.00015339859,0.000001384909],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002072083,0.00026128496,0.014060237,0.00023260745,0.00076109095,0.0019444623,0.017557023,0.048084278,0.008208475,0.37656215,0.02497201,0.50714916],"study_design_scores_gemma":[0.0006388419,0.00011279968,0.015023092,0.00005112027,0.00004973855,0.0015943678,0.000105652616,0.068389125,0.0567833,0.0033751281,0.85320354,0.00067326764],"about_ca_topic_score_codex":0.14557299,"about_ca_topic_score_gemma":0.17672434,"teacher_disagreement_score":0.8282316,"about_ca_system_score_codex":0.00024356997,"about_ca_system_score_gemma":0.00011126211,"threshold_uncertainty_score":0.8601167},"labels":[],"label_agreement":null},{"id":"W2076566755","doi":"10.1504/ejie.2009.025049","title":"Long-term electricity demand forecasting for power system planning using economic, demographic and climatic variables","year":2009,"lang":"en","type":"article","venue":"European J of Industrial Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Demand forecasting; Electricity demand; Econometrics; Term (time); Electricity; Autoregressive model; Computer science; Electricity generation; Operations research; Economics; Power (physics); Engineering","score_opus":0.03379258355219278,"score_gpt":0.21176415000979742,"score_spread":0.17797156645760465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076566755","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":[0.92225474,0.00063278025,0.07466683,0.000002823379,0.00055556436,0.000226321,0.000013697792,0.00032708983,0.0013201828],"genre_scores_gemma":[0.9961112,0.000009047504,0.003333789,0.0000045014217,0.00045221517,0.0000025927975,0.000009188096,0.00007516435,0.0000023085067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986452,0.000034219498,0.0005705645,0.00022349638,0.0000856746,0.0004408395],"domain_scores_gemma":[0.99937505,0.00018595801,0.00012491905,0.00015134858,0.000021195227,0.00014155835],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006481105,0.00028186364,0.00038596842,0.00028317707,0.000102552476,0.00006886382,0.00015239487,0.00010698518,0.0000033070287],"category_scores_gemma":[0.00011055845,0.00030936868,0.00009209367,0.00017887485,0.00001578453,0.00018551617,0.000028391158,0.00024489168,5.4582915e-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":[0.000024899377,0.0000067967753,0.002752935,0.00024839476,0.00009209489,0.000024255192,0.0001699628,0.9836594,0.007238646,0.00047008065,0.000021188673,0.005291356],"study_design_scores_gemma":[0.0013124084,0.00017027237,0.0012065759,0.0015001949,0.00009363636,0.00014156046,0.00004608221,0.99105704,0.0038948103,0.000008242646,0.000080876416,0.0004882932],"about_ca_topic_score_codex":0.0000032924142,"about_ca_topic_score_gemma":4.1644049e-7,"teacher_disagreement_score":0.07385648,"about_ca_system_score_codex":0.000094760806,"about_ca_system_score_gemma":0.00001863034,"threshold_uncertainty_score":0.99993587},"labels":[],"label_agreement":null},{"id":"W2076688526","doi":"10.1260/0309-524x.37.4.347","title":"Development of a Wind to Power Model for Wind Farm Power Production Forecasting","year":2013,"lang":"en","type":"article","venue":"Wind Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"TransCanada (Canada); Université de Moncton; Environment and Climate Change Canada; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wind power; Wind power forecasting; Wind speed; Power (physics); Artificial neural network; Flexibility (engineering); Meteorology; Environmental science; Computer science; Marine engineering; Engineering; Electric power system; Statistics; Mathematics; Electrical engineering; Artificial intelligence; Geography","score_opus":0.01897914874069992,"score_gpt":0.19637481943626164,"score_spread":0.17739567069556172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076688526","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":[0.8836732,0.00008173012,0.113399364,0.000023336073,0.0007904694,0.00044626207,0.0000063153375,0.00027318925,0.0013061368],"genre_scores_gemma":[0.8952424,0.0000010290925,0.10424824,0.000013679329,0.000120430646,0.000037120917,0.000009085402,0.00010022031,0.00022777125],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850947,0.000002864896,0.0004762061,0.00027857933,0.00018978579,0.0005430757],"domain_scores_gemma":[0.99943715,0.00003456094,0.000045356926,0.00022624356,0.000099250334,0.00015744362],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018745712,0.00029847087,0.0002795237,0.00023026484,0.000075071046,0.00003965396,0.00016272365,0.00011136737,0.000030113903],"category_scores_gemma":[0.00009147957,0.00031759578,0.000080845595,0.00026121837,0.00000924467,0.00022464067,0.00004960491,0.00015410331,0.000015990614],"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":[0.0000043706386,0.000012560263,0.000039999777,0.00013302114,0.000052525036,4.97487e-7,0.0035658553,0.90016145,0.090899095,0.0000892231,0.00014536809,0.004896052],"study_design_scores_gemma":[0.00026004322,0.000041133368,0.00046419798,0.00029456057,0.000013228827,0.000011094478,0.00015684108,0.9533718,0.039642483,0.000026258183,0.0052147144,0.00050360785],"about_ca_topic_score_codex":0.0000038672238,"about_ca_topic_score_gemma":0.000005129488,"teacher_disagreement_score":0.0532104,"about_ca_system_score_codex":0.00010375612,"about_ca_system_score_gemma":0.00003035553,"threshold_uncertainty_score":0.99992764},"labels":[],"label_agreement":null},{"id":"W2077031811","doi":"10.1016/j.epsr.2011.08.007","title":"Short-term wind power forecasting using ridgelet neural network","year":2011,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":128,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Wind power; Wind power forecasting; Artificial neural network; Electric power system; Crossover; Computer science; Term (time); Power (physics); Recurrent neural network; Operator (biology); Artificial intelligence; Engineering; Electrical engineering","score_opus":0.11781786071080466,"score_gpt":0.2968851132288222,"score_spread":0.17906725251801756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2077031811","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":[0.9268336,0.0062837517,0.0020356153,0.0000033850572,0.002500998,0.00047711414,0.000006286323,0.00044879867,0.061410423],"genre_scores_gemma":[0.99858195,0.000035903344,0.00021567965,0.0000072791245,0.0005735396,0.000028817807,0.000007457808,0.00016306681,0.00038629462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99532425,0.00033062312,0.000664934,0.00051399955,0.0009144074,0.0022517846],"domain_scores_gemma":[0.9984695,0.00027490064,0.000057676636,0.00058309693,0.00025702477,0.00035782997],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019989805,0.00039936765,0.00047073534,0.00057780446,0.00048009836,0.00021221224,0.00062136544,0.00027321646,0.00014179572],"category_scores_gemma":[0.000110743604,0.00038936193,0.00014078396,0.00199886,0.00007138525,0.00035449245,0.00015142364,0.0012284716,0.00006384811],"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":[0.000493083,0.00048735956,0.11609118,0.0014305852,0.001801108,0.0038481988,0.013896105,0.71680033,0.059246294,0.0056410534,0.054169178,0.02609551],"study_design_scores_gemma":[0.0005412081,0.00051016296,0.00600591,0.00059772446,0.00003214586,0.0009707269,0.00022374088,0.97866833,0.0027639924,0.00009747322,0.008352729,0.0012358391],"about_ca_topic_score_codex":0.00017635188,"about_ca_topic_score_gemma":0.000011237434,"teacher_disagreement_score":0.261868,"about_ca_system_score_codex":0.00031383615,"about_ca_system_score_gemma":0.00008112693,"threshold_uncertainty_score":0.9998558},"labels":[],"label_agreement":null},{"id":"W2079991364","doi":"10.1109/smartgridcomm.2012.6485977","title":"On hourly home peak load prediction","year":2012,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Peak load; Mean squared error; Occupancy; Peak demand; Electricity; Electrical load; Demand response; Work (physics); Load management; Computer science; Reduction (mathematics); Load balancing (electrical power); Root mean square; Environmental science; Statistics; Grid; Automotive engineering; Mathematics; Engineering; Voltage; Electrical engineering","score_opus":0.009366161075519287,"score_gpt":0.1868666573188987,"score_spread":0.1775004962433794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079991364","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":[0.5553673,0.000228018,0.0029339266,0.000009441639,0.00122125,0.000024279396,0.00000546863,0.0006376562,0.4395727],"genre_scores_gemma":[0.9980753,0.000012683918,0.00021679683,0.000037787304,0.00037436915,0.0000033680133,0.0000059913446,0.000017202032,0.0012564675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995895,0.0000041020658,0.00007141231,0.000046304496,0.00009600682,0.00019266011],"domain_scores_gemma":[0.9998116,0.00002086971,0.0000055634923,0.000086127315,0.000008019147,0.0000678369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000070177855,0.000069696005,0.000050523533,0.000028817745,0.000024904315,0.000010534446,0.000036478265,0.000041110266,0.0002555278],"category_scores_gemma":[0.0000074372742,0.00006041157,0.000023815206,0.000056663088,0.0000047552435,0.0001361006,0.000006158316,0.00007542939,0.00023452778],"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":[0.000059310238,0.0003394253,0.054890256,0.00026572877,0.00031814814,0.000012596708,0.0059346408,0.31527567,0.028728494,0.194782,0.26500532,0.1343884],"study_design_scores_gemma":[0.0027066548,0.00062621717,0.20515957,0.0004064087,0.00010268716,0.00012051243,0.0003774826,0.18287118,0.06435454,0.0031903284,0.5378123,0.0022721344],"about_ca_topic_score_codex":0.000008825556,"about_ca_topic_score_gemma":0.000003986207,"teacher_disagreement_score":0.44270805,"about_ca_system_score_codex":0.000041710307,"about_ca_system_score_gemma":0.0000027659428,"threshold_uncertainty_score":0.30144584},"labels":[],"label_agreement":null},{"id":"W2083490075","doi":"10.5539/mas.v7n7p10","title":"A Forecasting Model for Thailand’s Unemployment Rate","year":2013,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"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":"Mahasarakham University","keywords":"Unemployment rate; Unemployment; Box–Jenkins; Econometrics; Statistics; Economics; Computer science; Mathematics; Time series; Macroeconomics; Autoregressive integrated moving average","score_opus":0.033294162989192064,"score_gpt":0.22073624721513285,"score_spread":0.18744208422594077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2083490075","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":[0.19076043,0.000031700813,0.7873606,0.000027749886,0.00015499785,0.00033600285,0.0000047390463,0.00027219884,0.021051552],"genre_scores_gemma":[0.98254186,0.0000025499091,0.016629864,0.000094441806,0.00005470313,0.00029369298,0.0000030750184,0.000030902615,0.0003489227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878883,0.000002158273,0.00018227461,0.00028770228,0.00019130782,0.00054769666],"domain_scores_gemma":[0.99953645,0.000046816338,0.000028387209,0.00021008938,0.000046724454,0.0001315059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033256915,0.00015673945,0.00013052662,0.00008926771,0.00024185122,0.0001437611,0.00032184937,0.00003888384,0.000012478896],"category_scores_gemma":[0.000016397522,0.00014065269,0.000035597513,0.00022371576,0.00009891216,0.00023905475,0.00006083242,0.00008551532,0.000021798667],"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":[0.000002332289,0.0000059642944,0.000022335056,0.000021340513,0.0000037236182,1.9028452e-7,0.0006927613,0.7943218,0.18431397,0.00153002,0.00025642768,0.018829195],"study_design_scores_gemma":[0.00018964842,0.000008034349,0.000013902446,0.00001361427,0.0000035051378,0.0000014089859,0.000020173638,0.976049,0.013373211,0.010004663,0.00013656908,0.00018624433],"about_ca_topic_score_codex":0.000006417288,"about_ca_topic_score_gemma":0.0000073950428,"teacher_disagreement_score":0.7917814,"about_ca_system_score_codex":0.00006362898,"about_ca_system_score_gemma":0.00003897942,"threshold_uncertainty_score":0.573565},"labels":[],"label_agreement":null},{"id":"W2084703439","doi":"10.1109/pedg.2013.6785652","title":"Improved fast short-term wind power prediction model based on superposition of predicted error","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"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 New Brunswick","funders":"","keywords":"Wind power; Computer science; Artificial neural network; Mean squared prediction error; Predictive modelling; Support vector machine; Backpropagation; Stability (learning theory); Superposition principle; Microgrid; Power (physics); Term (time); Control theory (sociology); Algorithm; Machine learning; Artificial intelligence; Engineering; Mathematics","score_opus":0.00859107185199937,"score_gpt":0.19453026250193156,"score_spread":0.1859391906499322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2084703439","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":[0.90224516,0.000008521982,0.06996923,0.000018573268,0.00026892536,0.00022412841,0.00007098096,0.00043098858,0.026763484],"genre_scores_gemma":[0.99795043,0.0000019830336,0.001596904,0.000034992227,0.00004755238,0.000023338242,0.000115135656,0.0000364294,0.000193224],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999216,0.000010347467,0.00026053246,0.00015313727,0.00015157754,0.00020838041],"domain_scores_gemma":[0.9996135,0.000022415563,0.000018234881,0.00020140519,0.00006376538,0.0000806612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000050829414,0.00015839915,0.00013877178,0.00010878436,0.00003443591,0.000021462516,0.00008025276,0.00012057668,0.00038439804],"category_scores_gemma":[0.0000072173575,0.00014222224,0.00006218795,0.00010430346,0.000020885629,0.00023574475,0.000010985534,0.00012480335,0.000007304017],"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":[0.000016603955,0.000054188647,0.0017061193,0.000042607335,0.000025778456,4.7426587e-7,0.00016748585,0.77718955,0.2186883,0.00006778751,0.00048062,0.0015604702],"study_design_scores_gemma":[0.00027230065,0.00014184922,0.0058137816,0.000067415145,0.000012682773,0.0000011514826,0.000022220782,0.9634118,0.030107252,0.000015054478,0.000011938258,0.0001225549],"about_ca_topic_score_codex":0.000024455798,"about_ca_topic_score_gemma":0.000007550011,"teacher_disagreement_score":0.18858105,"about_ca_system_score_codex":0.000039873994,"about_ca_system_score_gemma":0.000012856564,"threshold_uncertainty_score":0.5799655},"labels":[],"label_agreement":null},{"id":"W2085016202","doi":"10.1109/tsg.2011.2177870","title":"Data Mining for Electricity Price Classification and the Application to Demand-Side Management","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Electricity market; Electricity; Demand side; Electricity price forecasting; Electricity price; Computer science; Economics; Demand forecasting; Microeconomics; Operations research; Econometrics; Industrial organization; Operations management; Engineering","score_opus":0.032489654435140715,"score_gpt":0.2538068228235201,"score_spread":0.22131716838837937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085016202","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":[0.018591838,0.00010056977,0.9781098,0.00015932144,0.00057827734,0.00043809117,0.000045247747,0.00012578968,0.0018511104],"genre_scores_gemma":[0.9920749,0.00009429317,0.0070672734,0.00010779269,0.00018082466,0.00032500728,0.000025540601,0.000020533498,0.00010384875],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938685,0.000017917608,0.00015069683,0.00015342807,0.000085645726,0.00020548588],"domain_scores_gemma":[0.9994109,0.00015569561,0.000022136717,0.00033291828,0.000014336531,0.000064014406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003757185,0.000097633565,0.00008874353,0.00007164438,0.00017864992,0.000026092343,0.0001477625,0.00003608368,0.000002693914],"category_scores_gemma":[0.0000035662279,0.00008028761,0.000024614106,0.00018843611,0.000015699203,0.00016286287,0.0000020203272,0.000079097685,0.000010904171],"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":[0.0004596127,0.00027033934,0.0003850125,0.0004555456,0.0005600668,5.111428e-7,0.0027827222,0.24673423,0.010197829,0.0068570226,0.0093839355,0.72191316],"study_design_scores_gemma":[0.0014584666,0.000051803625,0.0035167085,0.000058117334,0.00025114877,0.00001353901,0.00025663755,0.7935218,0.01399386,0.00007207555,0.18638213,0.00042366027],"about_ca_topic_score_codex":0.0000077228715,"about_ca_topic_score_gemma":0.00002840314,"teacher_disagreement_score":0.973483,"about_ca_system_score_codex":0.000033077187,"about_ca_system_score_gemma":0.000003034375,"threshold_uncertainty_score":0.32740337},"labels":[],"label_agreement":null},{"id":"W2085831049","doi":"10.1016/j.energy.2014.09.072","title":"Green power in Ontario: A dynamic model-based analysis","year":2014,"lang":"en","type":"article","venue":"Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"","keywords":"Power (physics); Power analysis; Environmental science; Physics","score_opus":0.005264482654191401,"score_gpt":0.18059217539386072,"score_spread":0.1753276927396693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2085831049","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":[0.6391047,0.000062042855,0.3096136,0.000021444263,0.00015111068,0.00001226186,0.0000021348264,0.00018491088,0.05084781],"genre_scores_gemma":[0.99686474,0.0000012748096,0.0013884095,0.00010227302,0.000011345403,0.0000082889,0.000031528023,0.000023626444,0.0015684967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993514,0.000015962147,0.00016151684,0.00014740176,0.000094834664,0.00022892251],"domain_scores_gemma":[0.9996757,0.000027983904,0.000016137792,0.00021253857,0.000009955085,0.00005767226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008122571,0.00012735686,0.00018611124,0.0002743295,0.000020319772,0.00001379625,0.0001133722,0.00007129489,0.00012919489],"category_scores_gemma":[0.0000045851534,0.00013073556,0.00009630785,0.0003666793,0.000010356653,0.000050565042,0.000013118089,0.0001088907,0.000005151437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002504387,0.000008330582,0.0032168687,0.0000026846808,0.000050409944,0.0000027532808,0.00011527954,0.9932908,0.00022341256,0.0012928103,0.000021381975,0.0017727936],"study_design_scores_gemma":[0.00017399332,0.000011102461,0.003922227,0.00001011956,0.000030230201,3.3209366e-7,0.0000025159952,0.99018174,0.00016788376,0.00035310382,0.0049869106,0.00015985752],"about_ca_topic_score_codex":0.036647435,"about_ca_topic_score_gemma":0.6955717,"teacher_disagreement_score":0.6589243,"about_ca_system_score_codex":0.00012883783,"about_ca_system_score_gemma":0.000020536745,"threshold_uncertainty_score":0.96976763},"labels":[],"label_agreement":null},{"id":"W2086639994","doi":"10.1016/j.rser.2012.05.042","title":"A new strategy for predicting short-term wind speed using soft computing models","year":2012,"lang":"en","type":"article","venue":"Renewable and Sustainable Energy Reviews","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":91,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Adaptive neuro fuzzy inference system; Wind speed; Soft computing; Artificial neural network; Wind power; Backpropagation; Computer science; Term (time); Electric power system; Power (physics); Fuzzy logic; Engineering; Data mining; Machine learning; Simulation; Artificial intelligence; Fuzzy control system; Meteorology","score_opus":0.04527516353835944,"score_gpt":0.26700092336231085,"score_spread":0.2217257598239514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086639994","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":[0.08317814,0.17564182,0.7245622,0.000008276914,0.00067361625,0.0005683096,0.000005345439,0.00037384394,0.014988449],"genre_scores_gemma":[0.9764053,0.0060131187,0.008872098,0.000057601275,0.0017041746,0.000008469736,0.000043463115,0.00013682239,0.0067589213],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976577,0.00004800215,0.00059355854,0.00028620576,0.00012777148,0.0012867318],"domain_scores_gemma":[0.99913454,0.00009452253,0.00009038677,0.00025267468,0.000065648936,0.0003622561],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00075974164,0.00037665057,0.00061300193,0.00013061006,0.00035390144,0.00014092374,0.00015112176,0.00016003166,0.00001782403],"category_scores_gemma":[0.00004127149,0.00035469382,0.0001487128,0.00029727013,0.00002092613,0.000810151,0.000095531126,0.00012372922,5.3467306e-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":[0.000007629646,0.00001380081,0.00074289716,0.00091658137,0.000050923314,0.000005376223,0.00035646535,0.9442838,0.0009842921,0.0026901036,0.0011159108,0.04883226],"study_design_scores_gemma":[0.00037943386,0.000050694613,0.000011521724,0.00046556143,0.00011366519,0.00005862323,0.000580921,0.8537425,0.001118237,0.0011495091,0.14176174,0.0005676189],"about_ca_topic_score_codex":0.0008868755,"about_ca_topic_score_gemma":0.000033451637,"teacher_disagreement_score":0.89322716,"about_ca_system_score_codex":0.00013531245,"about_ca_system_score_gemma":0.00007899081,"threshold_uncertainty_score":0.9998905},"labels":[],"label_agreement":null},{"id":"W2090882695","doi":"10.1175/jcli-d-12-00424.1","title":"The Gaussian Statistical Predictability of Wind Speeds","year":2013,"lang":"en","type":"article","venue":"Journal of Climate","topic":"Energy Load and Power Forecasting","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":"University of Victoria","funders":"","keywords":"Predictability; Wind speed; Scatterometer; Gaussian; Meteorology; Wind direction; Wind profile power law; Log wind profile; Environmental science; Mathematics; Wind gradient; Statistics; Physics","score_opus":0.008074134600797138,"score_gpt":0.2266456246468619,"score_spread":0.21857149004606474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2090882695","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":[0.97901714,0.00036526393,0.001199382,0.0001424513,0.0006505402,0.000042088348,0.000014031723,0.00001689749,0.018552192],"genre_scores_gemma":[0.9982869,0.0003612171,0.0011727815,0.000007769674,0.00014271126,3.1861646e-7,5.52409e-7,0.000009987882,0.00001776005],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9991838,0.000023296361,0.0004272633,0.00003082585,0.00016214619,0.00017271073],"domain_scores_gemma":[0.9994419,0.00020957853,0.0001108368,0.0000930858,0.00006931487,0.00007527479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035719658,0.00006429755,0.0001468255,0.000026721473,0.00004027746,0.000028891674,0.00011666953,0.000031505697,0.00016204224],"category_scores_gemma":[0.000079686375,0.000038486567,0.000053664822,0.000052591735,0.00005536638,0.000121411285,0.000015751493,0.00016806928,0.000009417375],"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":[0.00037797683,0.00043003383,0.29693437,0.0018172327,0.0012242371,0.00014606377,0.0059095286,0.14538456,0.047262028,0.046677507,0.0343344,0.41950205],"study_design_scores_gemma":[0.0024161176,0.0010951004,0.8200204,0.0010148545,0.00022779116,0.00052281853,0.0013768467,0.09780678,0.015403588,0.015609675,0.043824635,0.00068143685],"about_ca_topic_score_codex":0.0000053721915,"about_ca_topic_score_gemma":0.0000031701188,"teacher_disagreement_score":0.52308595,"about_ca_system_score_codex":0.000016605054,"about_ca_system_score_gemma":0.000012737699,"threshold_uncertainty_score":0.1774249},"labels":[],"label_agreement":null},{"id":"W2091219501","doi":"10.1109/pes.2011.6039506","title":"Electricity price thresholding and classification","year":2011,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Calgary","funders":"","keywords":"Electricity; Volatility (finance); Electricity market; Electricity price forecasting; Computer science; Context (archaeology); Electricity price; Smart grid; Economics; Econometrics; Microeconomics; Engineering","score_opus":0.03827190472163588,"score_gpt":0.20732699017037698,"score_spread":0.1690550854487411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091219501","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":[0.5054756,0.0001480393,0.015514473,0.000003952679,0.0000703405,0.000020423162,1.7204685e-7,0.0002491267,0.4785179],"genre_scores_gemma":[0.9972993,0.00005153343,0.002440216,0.000017566685,0.000023598479,0.0000022331226,6.5907e-7,0.0000077747145,0.00015710265],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975926,0.000002480907,0.000058139914,0.000056761342,0.000028795846,0.00009457873],"domain_scores_gemma":[0.99989676,0.000010580703,0.000006312242,0.00005195217,0.000006620007,0.000027787148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000451707,0.000044075914,0.00003674829,0.000029260802,0.000024882183,0.0000084233625,0.00002962025,0.000026292477,0.00006240408],"category_scores_gemma":[0.00000515307,0.000039781702,0.000007474806,0.000076502045,0.0000061254295,0.00007579039,0.000005972399,0.000047171477,0.000009627129],"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":[0.00002092097,0.00007347142,0.111267194,0.000196594,0.00012284168,0.0000116442425,0.005327463,0.0012558639,0.2232906,0.44792643,0.0050684097,0.20543857],"study_design_scores_gemma":[0.00040690272,0.000077373625,0.2572352,0.000046497356,0.000024681014,0.000038584294,0.00016500472,0.529806,0.19216828,0.004048161,0.015303155,0.0006801448],"about_ca_topic_score_codex":0.0000090702015,"about_ca_topic_score_gemma":0.000004612852,"teacher_disagreement_score":0.52855015,"about_ca_system_score_codex":0.000008362027,"about_ca_system_score_gemma":0.0000016059379,"threshold_uncertainty_score":0.16222507},"labels":[],"label_agreement":null},{"id":"W2092428982","doi":"10.1504/ijica.2011.044568","title":"A hybrid swarm-machine intelligence approach for day ahead price forecasting","year":2011,"lang":"en","type":"article","venue":"International Journal of Innovative Computing and Applications","topic":"Energy Load and Power Forecasting","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":"Computer science; Support vector machine; Electricity price forecasting; Volatility (finance); Electricity market; Artificial intelligence; Machine learning; Electricity; Swarm intelligence; Econometrics; Economics; Particle swarm optimization","score_opus":0.04249823900232474,"score_gpt":0.27579994396050456,"score_spread":0.2333017049581798,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2092428982","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":[0.024976974,0.0002623808,0.96729743,0.0000316152,0.00025162505,0.00012300676,0.000021063086,0.000045555516,0.0069903545],"genre_scores_gemma":[0.889935,0.000024611154,0.10948115,0.000063116546,0.00042963316,0.000014245821,0.000019564563,0.000017925344,0.000014718127],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906814,0.0000129415885,0.00050918077,0.00011989892,0.00014423714,0.00014561815],"domain_scores_gemma":[0.99859995,0.0001925587,0.0002604674,0.000067796114,0.0008286098,0.000050627285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004433634,0.00012651493,0.0001561384,0.00019981926,0.00009045518,0.000039647053,0.00030198056,0.000028243561,0.0000044854755],"category_scores_gemma":[0.000093034585,0.00011627759,0.000048759506,0.0002385654,0.000050100738,0.00012647634,0.00005238543,0.00020960468,6.7380086e-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":[0.000060918148,0.00025311482,0.0025657956,0.00011600328,0.0006471,0.0000110171795,0.0036317387,0.067067534,0.0019116242,0.08257375,0.0005237537,0.8406376],"study_design_scores_gemma":[0.0007576556,0.0001924564,0.0011583673,0.00034405058,0.00003242559,0.0007552732,0.0005620036,0.93686557,0.033303943,0.0116274245,0.01387524,0.0005256194],"about_ca_topic_score_codex":0.000004648724,"about_ca_topic_score_gemma":3.562774e-7,"teacher_disagreement_score":0.869798,"about_ca_system_score_codex":0.00004027047,"about_ca_system_score_gemma":0.000023095989,"threshold_uncertainty_score":0.47416624},"labels":[],"label_agreement":null},{"id":"W2095731600","doi":"10.1109/tpwrs.2008.2008606","title":"Applying Wavelets to Short-Term Load Forecasting Using PSO-Based Neural Networks","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":409,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Particle swarm optimization; Artificial neural network; Computer science; Wavelet; Term (time); Backpropagation; Algorithm; Preprocessor; Data pre-processing; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.02992717806607406,"score_gpt":0.23633140543955458,"score_spread":0.20640422737348052,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095731600","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":[0.14514309,0.00019570942,0.84788394,0.000013537259,0.0041609053,0.00052201044,0.000014820639,0.000619096,0.0014469036],"genre_scores_gemma":[0.9986382,0.00000280607,0.00082619267,0.00014325061,0.00012690437,0.00010502647,0.0000036806518,0.00009040718,0.000063544794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792176,0.00004770915,0.0005522955,0.00038478518,0.00037523813,0.00071817916],"domain_scores_gemma":[0.99912024,0.00009293454,0.00004734596,0.0003873473,0.00007792655,0.0002742081],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023156479,0.00041875805,0.00038960282,0.0002628601,0.00030485218,0.0001722639,0.0002203486,0.00019633443,0.000021295988],"category_scores_gemma":[0.0000037818475,0.0004410168,0.00018672732,0.00051582704,0.000017534012,0.00021039715,0.0000010649237,0.0004511068,0.000012587632],"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":[0.000017670396,0.000033340035,0.000030839605,0.000038920105,0.000034854853,0.000037651083,0.00018809769,0.98345715,0.0039157206,0.000006478907,0.00006022615,0.012179034],"study_design_scores_gemma":[0.00027996433,0.00013811718,0.000030290306,0.00041373033,0.000035693694,0.000093940966,0.000050710987,0.9945646,0.0032167935,0.000001022114,0.0006937014,0.00048146016],"about_ca_topic_score_codex":0.00004323651,"about_ca_topic_score_gemma":0.000025591027,"teacher_disagreement_score":0.8534951,"about_ca_system_score_codex":0.00031146235,"about_ca_system_score_gemma":0.000029605366,"threshold_uncertainty_score":0.99980414},"labels":[],"label_agreement":null},{"id":"W2098671909","doi":"10.1109/ccece.2008.4564671","title":"The development of a fuzzy neural system for load forecasting","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Artificial neural network; Computer science; Fuzzy logic; Neuro-fuzzy; Controller (irrigation); Fuzzy control system; Adaptive neuro fuzzy inference system; Block (permutation group theory); Control theory (sociology); Fuzzy electronics; Control engineering; Artificial intelligence; Control (management); Engineering; Mathematics","score_opus":0.026845018686368148,"score_gpt":0.1833720307963962,"score_spread":0.15652701211002804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098671909","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":[0.9118473,0.00053382706,0.078063615,0.00011442056,0.0007658966,0.0005799558,0.000013710851,0.00050414616,0.0075771245],"genre_scores_gemma":[0.99563193,0.000027149194,0.004048485,0.00001423707,0.0001412617,0.00007073313,0.0000031387267,0.000031659773,0.000031424228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984852,0.0000031859831,0.00039088822,0.000260025,0.0001902818,0.00067042786],"domain_scores_gemma":[0.9991402,0.00011034217,0.000055991677,0.00007998998,0.00027483742,0.00033865398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016061576,0.00027969948,0.00028801023,0.00016020228,0.00036425414,0.000114350005,0.0002727379,0.000104517494,0.000001558683],"category_scores_gemma":[0.00003806484,0.00023629311,0.000052444455,0.00025615856,0.000045194018,0.000108733926,0.000027084729,0.00025464356,0.0000013137707],"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":[0.0001100357,0.00004413456,0.0028048726,0.0018753177,0.00041114222,0.00004495431,0.010501939,0.024904229,0.0060815993,0.26001266,0.001055063,0.69215405],"study_design_scores_gemma":[0.00023245673,0.00010566384,0.00054042833,0.00022299404,0.000008171665,0.000071692266,0.000052071326,0.9948688,0.0016532942,0.00005297726,0.0018912996,0.00030012045],"about_ca_topic_score_codex":0.00016031234,"about_ca_topic_score_gemma":0.0006001998,"teacher_disagreement_score":0.9699646,"about_ca_system_score_codex":0.00021353656,"about_ca_system_score_gemma":0.00035625306,"threshold_uncertainty_score":0.96357536},"labels":[],"label_agreement":null},{"id":"W2101924284","doi":"10.1007/s40095-014-0105-5","title":"Application of sliding window technique for prediction of wind velocity time series","year":2014,"lang":"en","type":"article","venue":"International journal of energy and environmental engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":79,"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":"Sliding window protocol; Artificial neural network; Perceptron; Mean squared error; Renewable energy; Series (stratigraphy); Time series; Wind power; Computer science; Multilayer perceptron; Grid; Data mining; Wind speed; Reliability (semiconductor); Window (computing); Statistics; Artificial intelligence; Engineering; Power (physics); Machine learning; Mathematics; Meteorology","score_opus":0.002843087895573815,"score_gpt":0.1602157319042069,"score_spread":0.1573726440086331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101924284","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":[0.26274863,0.00016988428,0.7364993,0.000010045708,0.000267029,0.0000343164,0.000047494843,0.00001501015,0.0002083049],"genre_scores_gemma":[0.99387646,0.000094446834,0.0058168415,0.000002630394,0.00015740591,0.0000037534758,0.000022474405,0.000013754393,0.000012230627],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99949145,0.0000044450053,0.00027422013,0.000048309026,0.00011965229,0.00006191568],"domain_scores_gemma":[0.99976754,0.00004461983,0.00010400849,0.000039186772,0.000016258447,0.000028379793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012852221,0.00007529588,0.00012387703,0.00009713179,0.00001165516,0.000004549579,0.00008528687,0.00004825397,0.0000046472364],"category_scores_gemma":[0.000014830912,0.000077702585,0.000049991126,0.00002382261,0.00001676501,0.00016091879,0.000016355954,0.000045184162,6.8032215e-8],"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":[0.000017858578,0.000010860614,0.0005074594,0.000023365179,0.00006643239,3.0580105e-7,0.000035579593,0.4127246,0.5814285,0.0010406234,0.000005504059,0.0041388995],"study_design_scores_gemma":[0.0004075076,0.0001293149,0.003517967,0.00014409116,0.000022280463,0.00008455144,0.000016063363,0.3189918,0.67178214,0.00021807052,0.0045865807,0.0000995976],"about_ca_topic_score_codex":0.0000019607132,"about_ca_topic_score_gemma":2.3318165e-7,"teacher_disagreement_score":0.73112786,"about_ca_system_score_codex":0.000035772646,"about_ca_system_score_gemma":0.0000023549685,"threshold_uncertainty_score":0.31686193},"labels":[],"label_agreement":null},{"id":"W2101928339","doi":"10.1109/ccece.2011.6030668","title":"Determination of the spectrum of frequencies generated by a saturated transformer","year":2011,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Hydro One (Canada); Western University","funders":"","keywords":"Geomagnetically induced current; Transformer; Distribution transformer; Frequency spectrum; Electronic engineering; Electrical engineering; Engineering; Acoustics; Geomagnetic storm; Voltage; Physics; Magnetic field","score_opus":0.01471305509577404,"score_gpt":0.17982938055207176,"score_spread":0.16511632545629773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101928339","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":[0.9366538,0.00012836148,0.005872016,0.00000825614,0.00015249128,0.000046566773,0.000010255134,0.00005592845,0.05707231],"genre_scores_gemma":[0.9987494,0.000015422243,0.0009888455,0.0000064462824,0.0000067887204,0.0000015125503,0.0000033042136,0.000008412068,0.00021991527],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966884,0.000007644672,0.0001448899,0.000039499355,0.000057460635,0.00008164194],"domain_scores_gemma":[0.99986356,0.000007717516,0.00002245198,0.00007462292,0.000019428971,0.000012202079],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000352762,0.000058328762,0.00007353573,0.000023456463,0.000013057595,0.000001932676,0.000074238545,0.000037886824,0.00015147429],"category_scores_gemma":[0.000003947763,0.000037896138,0.000033223776,0.00014515371,0.000026933949,0.000055767137,0.0000023779473,0.000042114727,8.118276e-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":[0.0000038763187,0.000012507972,0.0006714544,0.00003870386,0.000019392315,2.636376e-7,0.0013358872,0.00033981845,0.9925324,0.0011569007,0.00021540129,0.0036734184],"study_design_scores_gemma":[0.00007679901,0.000016387126,0.0008155489,0.000016101061,0.0000071313657,0.0000015890495,0.000032621836,0.010392672,0.9883131,0.00013477173,0.00014326378,0.000050023198],"about_ca_topic_score_codex":0.00014093186,"about_ca_topic_score_gemma":0.00011542642,"teacher_disagreement_score":0.062095538,"about_ca_system_score_codex":0.0000076238784,"about_ca_system_score_gemma":0.000006101829,"threshold_uncertainty_score":0.16585372},"labels":[],"label_agreement":null},{"id":"W2103066546","doi":"10.1109/pess.2000.867636","title":"Real-time load forecasting by artificial neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"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 New Brunswick","funders":"","keywords":"Artificial neural network; Computer science; Scheduling (production processes); Interval (graph theory); Electric power system; Peak load; Term (time); Real-time computing; Operations research; Power (physics); Artificial intelligence; Engineering; Automotive engineering; Operations management","score_opus":0.01990280654949745,"score_gpt":0.1919357594568303,"score_spread":0.17203295290733286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103066546","genre_codex":"other","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":[0.40015906,0.00059147214,0.007319377,0.00006228626,0.0010218576,0.00010350832,0.000007688475,0.0017941835,0.58894056],"genre_scores_gemma":[0.99524724,0.000046232803,0.00063515606,0.00004269088,0.0004821377,0.0000059035056,0.000013577462,0.00005280668,0.003474255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990055,0.000012351504,0.00024385068,0.0001604243,0.00013942907,0.00043842243],"domain_scores_gemma":[0.9996366,0.00006923594,0.000022782653,0.00014560389,0.000021331813,0.00010447445],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00008784531,0.00017657747,0.00015282337,0.00003115912,0.00009036141,0.00006066683,0.00011145731,0.00009517347,0.0014505343],"category_scores_gemma":[0.000017555405,0.00017128164,0.00006191018,0.00017352842,0.000020397163,0.0001385814,0.00002366285,0.00016464163,0.00012686699],"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":[0.0000048963175,0.000023583334,0.00012530778,0.000016182323,0.0000337763,0.0000246788,0.00021238172,0.75592756,0.008174992,0.00025533684,0.08575043,0.14945087],"study_design_scores_gemma":[0.00009934434,0.000019566503,0.0000051643506,0.0000122656365,0.0000066217412,0.000015857777,0.00000874497,0.9933249,0.0012664071,0.000023619554,0.0050065555,0.00021098765],"about_ca_topic_score_codex":0.000046047593,"about_ca_topic_score_gemma":0.000019388197,"teacher_disagreement_score":0.5950882,"about_ca_system_score_codex":0.000045789115,"about_ca_system_score_gemma":0.0000017853533,"threshold_uncertainty_score":0.99946225},"labels":[],"label_agreement":null},{"id":"W2103720627","doi":"10.1109/epec.2010.5697238","title":"Achieving CO2 emission targets for energy consumption at Canadian manufacturing and beyond; using Hybrid Optimization Model","year":2010,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Energy consumption; Coal; Artificial neural network; Consumption (sociology); Global warming; Oil consumption; Greenhouse gas; Climate change; Fuel efficiency; Computer science; Manufacturing; Fossil fuel; Environmental science; Environmental economics; Engineering; Business; Automotive engineering; Economics; Artificial intelligence; Waste management; Ecology","score_opus":0.012006321331776796,"score_gpt":0.2047199136115152,"score_spread":0.19271359227973842,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103720627","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":[0.60312223,0.00009165823,0.3936131,0.000017864146,0.00031856855,0.000061353145,0.000021564498,0.00012373793,0.002629912],"genre_scores_gemma":[0.94380116,0.000048198082,0.055607516,0.000064004984,0.000080436905,0.0000067205647,0.000089397196,0.000041853717,0.00026069506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993293,0.000004643303,0.00015197226,0.00016432014,0.00006103522,0.00028870578],"domain_scores_gemma":[0.9996108,0.000031147993,0.000024906594,0.00010094764,0.00001950171,0.00021273499],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000078192155,0.00014560261,0.0001099103,0.00011802315,0.0002635991,0.00004708945,0.00005236047,0.00008784442,0.00008056052],"category_scores_gemma":[0.000011994426,0.00014934347,0.00002842708,0.000021219364,0.000016174317,0.0001885232,0.000024396075,0.00009443394,5.726339e-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":[0.0000033519536,0.0000016248159,0.00016485203,0.000034184533,0.000008558327,0.0000015082421,0.000050010287,0.96328527,0.032804213,0.00025755994,0.00017621038,0.0032126585],"study_design_scores_gemma":[0.00015529028,0.0000043142554,0.000016794154,0.000018358776,0.000011048578,0.00002771992,0.000004780095,0.9041395,0.09415518,0.000044246714,0.0012478236,0.00017489708],"about_ca_topic_score_codex":0.0017598044,"about_ca_topic_score_gemma":0.011192966,"teacher_disagreement_score":0.34067896,"about_ca_system_score_codex":0.00008858031,"about_ca_system_score_gemma":0.000022381977,"threshold_uncertainty_score":0.6245938},"labels":[],"label_agreement":null},{"id":"W2105359440","doi":"","title":"Application of Gray-Fuzzy-Markov Chain Method for Day-Ahead Electric Load Forecasting","year":2012,"lang":"pl","type":"article","venue":"PRZEGLĄD ELEKTROTECHNICZNY","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"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":"Autoregressive integrated moving average; Markov chain; Gray (unit); Fuzzy logic; Computer science; Electric power system; Mathematical optimization; Artificial intelligence; Time series; Mathematics; Power (physics); Machine learning","score_opus":0.017816198216648767,"score_gpt":0.25973809737832615,"score_spread":0.2419218991616774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2105359440","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":[0.0068248357,0.007819318,0.97413325,0.00016661387,0.0010575115,0.0021757525,0.00011354046,0.00069686153,0.007012312],"genre_scores_gemma":[0.82997584,0.0002365683,0.16668111,0.00006790858,0.0013848753,0.0008468087,0.0000932465,0.0002795258,0.000434089],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949037,0.00017575423,0.0014686827,0.0007414581,0.0006452875,0.002065157],"domain_scores_gemma":[0.9965756,0.0009987159,0.00070186885,0.0009912065,0.00033921906,0.00039339115],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0039895927,0.00085983897,0.0011050759,0.00056174304,0.000312131,0.00005976213,0.00072972645,0.00080427184,0.000037560047],"category_scores_gemma":[0.0007583651,0.000952297,0.00055360317,0.001627238,0.00006677506,0.00051854464,0.00017501142,0.00088502036,0.000033284785],"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":[0.00015787367,0.00028629936,0.0013021032,0.0020499062,0.00041310952,0.0000020350672,0.0012838881,0.014498096,0.2734933,0.021338677,0.0015474732,0.68362725],"study_design_scores_gemma":[0.0013625048,0.00041378613,0.00036013525,0.0005307405,0.00045127652,0.000083118364,0.00007796673,0.7100197,0.22368585,0.0016263069,0.05999756,0.001391074],"about_ca_topic_score_codex":0.0003122397,"about_ca_topic_score_gemma":0.000039344082,"teacher_disagreement_score":0.82315105,"about_ca_system_score_codex":0.00059556816,"about_ca_system_score_gemma":0.0001464476,"threshold_uncertainty_score":0.99929273},"labels":[],"label_agreement":null},{"id":"W2107576415","doi":"10.1109/ccece.1997.608252","title":"An approach for metering real-time reactive power consumption using a neural network approach","year":2002,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Technical University of Nova Scotia","funders":"","keywords":"Metering mode; Artificial neural network; Electricity; Computer science; AC power; Electric power system; Spot contract; Real-time computing; Power (physics); Automotive engineering; Engineering; Artificial intelligence; Electrical engineering; Voltage; Finance","score_opus":0.0471779137847829,"score_gpt":0.24015451434872612,"score_spread":0.19297660056394322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107576415","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":[0.6386308,0.00014573494,0.29199845,0.0000023028028,0.00025691206,0.00031027553,0.000008695284,0.0008265342,0.06782031],"genre_scores_gemma":[0.78992134,0.000012261714,0.2095086,0.000015313204,0.00024078418,0.00003175546,0.000042803542,0.000067118395,0.00016002168],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988842,0.00003182124,0.00023223132,0.00028036156,0.000108125714,0.00046328484],"domain_scores_gemma":[0.9995315,0.000054967593,0.000041169413,0.000233823,0.000027406928,0.00011111778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017286048,0.00022638828,0.00023604259,0.000068731686,0.00013112088,0.00007344639,0.00012244398,0.00011644255,0.00010345033],"category_scores_gemma":[0.00000705564,0.00022051088,0.00008645731,0.00014105083,0.000025852605,0.00036471264,0.00001984848,0.00013095034,0.0000064702085],"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":[0.000013346073,0.00004185262,0.00023557954,0.000053512536,0.00005700877,0.0000012564776,0.00034942417,0.97893184,0.01840017,0.0003878937,0.0002461363,0.0012819626],"study_design_scores_gemma":[0.00025338435,0.000042996708,0.00008905798,0.000013731425,0.000028420756,0.0000356246,0.000048624384,0.99847066,0.0005354145,0.000016350825,0.00018004906,0.00028567028],"about_ca_topic_score_codex":0.000016881751,"about_ca_topic_score_gemma":8.251386e-7,"teacher_disagreement_score":0.15129055,"about_ca_system_score_codex":0.0000612651,"about_ca_system_score_gemma":0.000002329371,"threshold_uncertainty_score":0.89921725},"labels":[],"label_agreement":null},{"id":"W2108474403","doi":"10.1109/ccece.1995.528152","title":"A practical approach to electric load forecasting using artificial neural networks with corrective filtering","year":2002,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Waterloo","funders":"","keywords":"Artificial neural network; Computer science; Electric power system; Electrical load; Artificial intelligence; Machine learning; Power (physics); Electric power","score_opus":0.0641708921312416,"score_gpt":0.23668937704724866,"score_spread":0.17251848491600708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108474403","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":[0.19838062,0.00006472567,0.7487531,0.000019472542,0.0002963663,0.00017587871,6.0330115e-7,0.00043665047,0.0518726],"genre_scores_gemma":[0.94549817,0.0000025831744,0.053834036,0.0000819553,0.0004300951,0.000019269757,0.0000024861838,0.0000680287,0.000063396474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985464,0.000026907099,0.000253044,0.0002975716,0.00022538462,0.0006506807],"domain_scores_gemma":[0.99944603,0.00011805515,0.000037664264,0.0001603786,0.000060239057,0.00017762404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012889516,0.00026166625,0.00022903009,0.00011508868,0.00015288463,0.000115557166,0.00009478988,0.000092011476,0.000055403278],"category_scores_gemma":[0.000063520434,0.00023054479,0.00005076552,0.0007259335,0.000016262033,0.0003097962,0.000035280653,0.00037845765,0.000007182576],"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":[0.00002388676,0.000034958743,0.00007943638,0.000013417186,0.000033422853,0.000024003339,0.00030921682,0.9856811,0.00087454316,0.00030450334,0.00027357767,0.012347916],"study_design_scores_gemma":[0.00012524481,0.00009949231,0.000015257273,0.000033266846,0.000023877084,0.00042382698,0.0000984595,0.9973474,0.0012660248,0.0000059060867,0.00022956285,0.00033169077],"about_ca_topic_score_codex":0.000043703407,"about_ca_topic_score_gemma":0.00003343567,"teacher_disagreement_score":0.7471175,"about_ca_system_score_codex":0.00016525267,"about_ca_system_score_gemma":0.000011721429,"threshold_uncertainty_score":0.9401344},"labels":[],"label_agreement":null},{"id":"W2109669259","doi":"10.5430/rwe.v4n1p109","title":"A Study of the Relationship between Electricity Consumption and GDP Growth in Hainan International Tourism Island of China","year":2013,"lang":"en","type":"article","venue":"Research in World Economy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":22,"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":"Granger causality; Error correction model; Tourism; China; Economics; Consumption (sociology); Real gross domestic product; Electricity; Economy; Econometrics; Agricultural economics; Cointegration; Geography; Engineering","score_opus":0.06570387186269824,"score_gpt":0.3080897056826863,"score_spread":0.24238583381998804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2109669259","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":[0.99038905,0.000059006194,0.000012935999,0.00011476735,0.000029419192,0.00024480518,0.000002381054,0.000005321758,0.009142298],"genre_scores_gemma":[0.9997678,0.000012219086,0.00002665204,0.0000010264271,0.000029787881,0.000031980682,0.0000017650106,0.0000059968584,0.00012277564],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99934417,0.0000961004,0.00023571978,0.00008808366,0.000088732195,0.00014720789],"domain_scores_gemma":[0.9993625,0.00047018018,0.000029478542,0.000087772016,0.00002684813,0.000023202982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006059413,0.000049036877,0.00010726544,0.00041959548,0.000027187469,0.000017329923,0.0001343072,0.000025162097,0.00002689576],"category_scores_gemma":[0.000077000106,0.00004271936,0.00001238872,0.0003373347,0.00003949385,0.00014058781,0.000044920725,0.00030699355,0.000002062735],"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":[0.0000022081908,0.000026801785,0.99666095,0.000034877947,0.000009062426,5.4797977e-7,0.00064742425,0.0007965215,0.000011417815,0.0012290549,0.000073224524,0.00050792174],"study_design_scores_gemma":[0.0003627948,0.0000209183,0.9915014,0.00006640127,9.608931e-7,4.1174403e-7,0.00005682496,0.0033426224,0.00018997055,0.0043702195,0.000049425176,0.00003806017],"about_ca_topic_score_codex":0.0011108187,"about_ca_topic_score_gemma":0.0030083673,"teacher_disagreement_score":0.009378731,"about_ca_system_score_codex":0.00006603382,"about_ca_system_score_gemma":0.000011135396,"threshold_uncertainty_score":0.1742045},"labels":[],"label_agreement":null},{"id":"W2111528621","doi":"10.1016/j.enbuild.2005.02.005","title":"On-line building energy prediction using adaptive artificial neural networks","year":2005,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":327,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Concordia University","funders":"Natural Resources Canada","keywords":"Artificial neural network; Sliding window protocol; Training (meteorology); Variance (accounting); Computer science; Window (computing); Energy (signal processing); Line (geometry); Artificial intelligence; Training set; Machine learning; Engineering; Statistics; Mathematics","score_opus":0.017702235465621694,"score_gpt":0.21635782544566104,"score_spread":0.19865558998003935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111528621","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":[0.5484329,0.0009441628,0.4472704,0.00004466354,0.0009165394,0.000022739097,0.0000073181604,0.0004074942,0.0019538149],"genre_scores_gemma":[0.99446154,0.00011764625,0.0031516994,0.00019916844,0.0019085134,0.000006715313,0.000012874198,0.000054500557,0.00008735914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884796,0.000023260169,0.0002864924,0.0002842504,0.00014632769,0.0004116807],"domain_scores_gemma":[0.9995854,0.00008290004,0.000050811686,0.00012578543,0.000026220803,0.00012889189],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000105375155,0.000263512,0.00020469104,0.00014751559,0.00022702993,0.00006550415,0.00009676935,0.00016967158,0.000028897039],"category_scores_gemma":[0.000011891914,0.00026452867,0.00006776265,0.00022331513,0.000049041828,0.0002697447,0.000041376607,0.00017892547,4.7582483e-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":[0.000030807354,0.0000137542975,0.000022785842,0.0000030766628,0.000030345827,0.000004205278,0.000040625233,0.86033696,0.003940811,0.061711326,0.00018478207,0.07368053],"study_design_scores_gemma":[0.00018485152,0.00009230075,0.000011633181,0.000067194844,0.000022119375,0.000033443586,0.000012004114,0.97507423,0.014195058,0.00089677435,0.009151956,0.00025844068],"about_ca_topic_score_codex":0.00009715731,"about_ca_topic_score_gemma":0.00008292872,"teacher_disagreement_score":0.44602865,"about_ca_system_score_codex":0.00006910984,"about_ca_system_score_gemma":0.000006487325,"threshold_uncertainty_score":0.9999807},"labels":[],"label_agreement":null},{"id":"W2111659384","doi":"10.1109/ccece.1999.804865","title":"Wavelet neural network based short term load forecasting of electric power system commercial load","year":2003,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Artificial neural network; Morlet wavelet; Wavelet; Electrical load; Electric power system; Computer science; Wavelet transform; Electric power; Power (physics); Term (time); Artificial intelligence; Engineering; Discrete wavelet transform; Electrical engineering; Voltage","score_opus":0.0161490953640918,"score_gpt":0.20114135838405614,"score_spread":0.18499226301996435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111659384","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":[0.7780121,0.00055836345,0.009595759,0.0000072776693,0.0013112981,0.00019216991,0.000005357253,0.0005289803,0.20978868],"genre_scores_gemma":[0.9973644,0.0000028519942,0.0022198525,0.000050334176,0.00018137031,0.000011109542,0.0000067175906,0.000064393,0.00009896624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998204,0.000058453283,0.00051006675,0.00021998372,0.0003469599,0.0006605458],"domain_scores_gemma":[0.9992848,0.0001516023,0.000054415537,0.00027538682,0.000110159985,0.00012360852],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042650953,0.00029202158,0.00038524767,0.00007857376,0.00010269622,0.00003325782,0.00017748577,0.0001391395,0.00011076641],"category_scores_gemma":[0.000055349876,0.0002754283,0.00014700278,0.0005047578,0.000021523903,0.00010629756,0.000019527357,0.00025342955,0.000008171277],"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":[0.000058143858,0.00007071103,0.020362254,0.00057210104,0.00017134687,0.00009388459,0.00029132067,0.94232833,0.005099035,0.004507264,0.005302882,0.021142706],"study_design_scores_gemma":[0.0006282027,0.00013911737,0.0015250195,0.0002688872,0.000050943778,0.000091835245,0.000048706832,0.97629994,0.016986419,0.000012952384,0.0033511873,0.0005968116],"about_ca_topic_score_codex":0.00002949846,"about_ca_topic_score_gemma":0.000047010028,"teacher_disagreement_score":0.21935229,"about_ca_system_score_codex":0.0002256293,"about_ca_system_score_gemma":0.00008411963,"threshold_uncertainty_score":0.9999698},"labels":[],"label_agreement":null},{"id":"W2111979072","doi":"10.1007/s10182-005-0212-y","title":"Using Analysis of Variance and Factor Analysis for the Reduction of High Dimensional Variation in Time Series of Energy Consumption","year":2005,"lang":"en","type":"article","venue":"Allgemeines Statistisches Archiv","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Variance (accounting); Electricity; Econometrics; Dynamic factor; Series (stratigraphy); Consumption (sociology); Variation (astronomy); Time series; Liberalization; Statistics; Quarter (Canadian coin); Economics; Computer science; Mathematics; Engineering; Geography","score_opus":0.020265295866987106,"score_gpt":0.23620583227424807,"score_spread":0.21594053640726096,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2111979072","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":[0.67621857,0.00019140462,0.32322675,0.000013386762,0.000028126895,0.000040662042,0.00026406226,0.00000800174,0.000009050214],"genre_scores_gemma":[0.9364924,0.00010804088,0.06322991,0.000001640833,0.000022982866,0.0000054399993,0.00010850702,0.000008825886,0.000022275595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924344,0.000033605025,0.00038783945,0.00011817275,0.00011964719,0.000097291224],"domain_scores_gemma":[0.99933994,0.0003128229,0.0001636139,0.0001165739,0.000049993138,0.000017054952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012548207,0.00009477909,0.00031395984,0.00036321356,0.00002988893,0.0000064766105,0.00006042999,0.00002955845,0.000037572732],"category_scores_gemma":[0.00003083328,0.00008141468,0.000071377704,0.0005463186,0.00007021214,0.00012619274,0.00002011394,0.00003243959,6.104383e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004798412,0.00001859427,0.009441637,0.000060885595,0.0019004404,1.17290064e-7,0.00046364602,0.9238758,0.053798426,0.0060449173,0.0000034128573,0.0043441565],"study_design_scores_gemma":[0.00015256768,0.000019018344,0.28524202,0.000021144748,0.0010269916,8.161208e-7,0.0000064928777,0.7083644,0.0048331064,0.00025047286,0.00001570684,0.00006723088],"about_ca_topic_score_codex":0.000542833,"about_ca_topic_score_gemma":0.0005691829,"teacher_disagreement_score":0.2758004,"about_ca_system_score_codex":0.000012887284,"about_ca_system_score_gemma":0.000009220469,"threshold_uncertainty_score":0.33199942},"labels":[],"label_agreement":null},{"id":"W2112786698","doi":"10.1109/ccece.2000.849691","title":"Short term load forecasting by using wavelet neural networks","year":2002,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Artificial neural network; Backpropagation; Computer science; Morlet wavelet; Feedforward neural network; Wavelet; Time delay neural network; Probabilistic neural network; Activation function; Convergence (economics); Artificial intelligence; Generalization; Wavelet transform; Term (time); Function approximation; Machine learning; Discrete wavelet transform; Mathematics","score_opus":0.032653993063890524,"score_gpt":0.20870650585248768,"score_spread":0.17605251278859715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112786698","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":[0.8748629,0.0015128903,0.043200985,0.000016382954,0.0010178535,0.000093336894,0.000005526529,0.0008209115,0.0784692],"genre_scores_gemma":[0.99754554,0.000027746059,0.001395696,0.000061230145,0.0003344366,0.000003189389,0.00000904973,0.000057724457,0.00056539493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998933,0.0000098949595,0.00024375357,0.00017625919,0.00014803749,0.00048906595],"domain_scores_gemma":[0.9996509,0.000042307413,0.000016089243,0.00015931414,0.00002179691,0.00010959398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000077523466,0.00020842491,0.00016700565,0.000037342434,0.00010436729,0.00006642093,0.00012619175,0.000099625024,0.0003088023],"category_scores_gemma":[0.000010673559,0.00020080221,0.00006721002,0.0001648917,0.000021116606,0.00019044263,0.00003606762,0.00021675896,0.000007780893],"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":[0.0000018008834,0.000015549054,0.0031117536,0.000027945574,0.000041015628,0.000034493012,0.00017260865,0.75761807,0.002378031,0.000030264575,0.009191414,0.22737707],"study_design_scores_gemma":[0.00010088004,0.000011230763,0.000042750744,0.000028260578,0.000010630342,0.00006677072,0.000011456043,0.9969494,0.00071013137,0.0000027884594,0.0018089053,0.00025681048],"about_ca_topic_score_codex":0.000018580044,"about_ca_topic_score_gemma":0.000013396925,"teacher_disagreement_score":0.23933133,"about_ca_system_score_codex":0.00007501548,"about_ca_system_score_gemma":0.0000017208282,"threshold_uncertainty_score":0.81884766},"labels":[],"label_agreement":null},{"id":"W2114013437","doi":"10.1109/ccece.2008.4564635","title":"Locational marginal pricing prediction in a competitve electrical market using computational intelligence","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Particle swarm optimization; Artificial neural network; Computer science; Generalization; Backpropagation; Function (biology); Mathematical optimization; Wavelet; Artificial intelligence; Econometrics; Machine learning; Economics; Mathematics","score_opus":0.01839482676552288,"score_gpt":0.1908292407575846,"score_spread":0.17243441399206172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114013437","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":[0.5516684,0.0002903342,0.4399017,0.00018850989,0.0004091828,0.00039508133,0.000023460792,0.00045789022,0.006665413],"genre_scores_gemma":[0.99308455,0.00008876778,0.0064427517,0.00008425539,0.00018836356,0.000023489547,0.000016402673,0.00003537384,0.00003606941],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980788,0.000010883784,0.00042295482,0.00044554254,0.0002923897,0.00074942526],"domain_scores_gemma":[0.99914664,0.00008980026,0.000044912984,0.000074064876,0.00019094245,0.00045361454],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013845225,0.0003619306,0.00033607794,0.0007418644,0.00016929528,0.00014060113,0.00023412246,0.00017236592,0.00008297828],"category_scores_gemma":[0.00003177648,0.00040901193,0.000044296245,0.0008065091,0.00006469356,0.0002748833,0.00003052996,0.00064942223,0.00000776057],"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":[0.000056812234,0.00009338271,0.03775483,0.00020292703,0.00010065372,0.00012568996,0.0014228085,0.78183407,0.00077921164,0.134799,0.00072740513,0.042103194],"study_design_scores_gemma":[0.00020154756,0.00011824341,0.024290036,0.0002278203,0.0000071566615,0.0002830867,0.000011026029,0.9735999,0.000105318024,0.00049578224,0.00026299196,0.00039705774],"about_ca_topic_score_codex":0.00073276093,"about_ca_topic_score_gemma":0.00027040706,"teacher_disagreement_score":0.4414161,"about_ca_system_score_codex":0.00042221343,"about_ca_system_score_gemma":0.00036326167,"threshold_uncertainty_score":0.99983615},"labels":[],"label_agreement":null},{"id":"W2116238159","doi":"10.6000/1927-5129.2013.09.21","title":"Comparative Study of Temperature and Rainfall Fluctuation in Hunza-Nagar District","year":2013,"lang":"en","type":"article","venue":"Journal of Basic & Applied Sciences","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"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":"Environmental science; Regression analysis; Air temperature; Climate change; Mean radiant temperature; Statistics; Climatology; Econometrics; Meteorology; Mathematics; Geography; Geology","score_opus":0.017354598186692646,"score_gpt":0.2387741807796663,"score_spread":0.22141958259297367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116238159","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":[0.9948421,0.00017142182,0.000056744237,0.000017651833,0.00010411943,0.00009619753,4.4510054e-7,0.000005836172,0.004705445],"genre_scores_gemma":[0.99925137,0.000013255576,0.00067096466,0.000007895304,0.000046051548,0.0000028205834,2.1297018e-7,0.0000028441998,0.0000045744396],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99929684,0.000016573511,0.00029645942,0.00006994291,0.00021630895,0.000103841885],"domain_scores_gemma":[0.99968845,0.000082008984,0.00011138067,0.000039771232,0.000039809795,0.000038609393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034869125,0.00007452204,0.00020117623,0.00014961598,0.000048059068,0.00004646876,0.00012134439,0.00002598734,0.000013201244],"category_scores_gemma":[0.000011158255,0.000053683732,0.000015843381,0.00035469737,0.00006732685,0.00020658658,0.000013499842,0.00014157714,9.680286e-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":[0.00006541763,0.0006439915,0.05970425,0.00014062806,0.00018434746,0.00001849486,0.07803427,0.3316042,0.48210478,0.0033039819,0.0021178448,0.042077783],"study_design_scores_gemma":[0.0055145076,0.002464578,0.7687534,0.0004638779,0.0000898826,0.000088030014,0.087951176,0.08160196,0.046973556,0.0047342665,0.00042404002,0.0009406919],"about_ca_topic_score_codex":0.000015787076,"about_ca_topic_score_gemma":0.000043909968,"teacher_disagreement_score":0.70904917,"about_ca_system_score_codex":0.000015326626,"about_ca_system_score_gemma":0.000018839342,"threshold_uncertainty_score":0.2189159},"labels":[],"label_agreement":null},{"id":"W2116721779","doi":"10.1142/s0219024904002396","title":"CALIBRATION OF MULTIFACTOR MODELS IN ELECTRICITY MARKETS","year":2004,"lang":"en","type":"article","venue":"International Journal of Theoretical and Applied Finance","topic":"Energy Load and Power Forecasting","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":"Pacific Institute for the Mathematical Sciences; University of British Columbia","funders":"","keywords":"Kalman filter; Spot contract; Econometrics; Calibration; Electricity; Electricity market; Extended Kalman filter; Computer science; Economics; Futures contract; Financial economics; Statistics; Engineering; Mathematics; Artificial intelligence","score_opus":0.0054868299866070135,"score_gpt":0.2006814586176817,"score_spread":0.19519462863107467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116721779","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":[0.9301052,0.0001359126,0.06648741,0.00009763697,0.00014093285,0.000020913913,0.000003874642,0.00000578836,0.0030023456],"genre_scores_gemma":[0.99720234,0.00020757153,0.0025019583,0.000021780666,0.000056843488,8.337477e-7,8.2546484e-7,0.0000054690527,0.0000023852974],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99949646,0.0000047706903,0.00025017015,0.000044511147,0.00013252163,0.00007157719],"domain_scores_gemma":[0.9998119,0.00004705104,0.000058282803,0.000026089925,0.000036006284,0.000020698944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010739397,0.00005634155,0.000109439476,0.000068143585,0.000006078908,0.0000094161205,0.000105204934,0.000038955408,0.000009096349],"category_scores_gemma":[0.000017493197,0.000047422982,0.000026747535,0.000053041756,0.000058363486,0.00008973466,0.000012785875,0.00012420936,1.9083414e-7],"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":[0.00009249378,0.00002989086,0.00007076516,0.00000714699,0.000014152984,0.000007109074,0.00012129332,0.24858536,0.005536118,0.736751,0.000005046257,0.008779583],"study_design_scores_gemma":[0.0017811288,0.00006197792,0.002185952,0.0002665395,0.000008305248,0.00006277197,0.000019645287,0.25884905,0.1475228,0.5889276,0.00013892706,0.00017526596],"about_ca_topic_score_codex":0.0000017512157,"about_ca_topic_score_gemma":0.000001015452,"teacher_disagreement_score":0.14782344,"about_ca_system_score_codex":0.000030169072,"about_ca_system_score_gemma":0.000012105626,"threshold_uncertainty_score":0.1933853},"labels":[],"label_agreement":null},{"id":"W2117915583","doi":"10.1109/ccece.2007.74","title":"Short-Term Load Forecasting Using Artificial Neural Network Based on Particle Swarm Optimization Algorithm","year":2007,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Particle swarm optimization; Artificial neural network; Computer science; Term (time); Backpropagation; Algorithm; Feature (linguistics); Swarm behaviour; Set (abstract data type); Artificial intelligence; Mathematical optimization; Mathematics","score_opus":0.038960225178954595,"score_gpt":0.24476063501541678,"score_spread":0.2058004098364622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117915583","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":[0.30036908,0.0000326344,0.69400275,0.0000071285835,0.0007271725,0.00007895583,0.0000016987227,0.0003818944,0.0043986826],"genre_scores_gemma":[0.91471064,0.0000011376625,0.08427454,0.00007982961,0.00084327924,0.000002754922,0.000014815272,0.00005432178,0.000018712552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99851567,0.000017288077,0.0003742841,0.00021105212,0.00025131495,0.0006303764],"domain_scores_gemma":[0.9994876,0.00012413404,0.000031237723,0.00016935308,0.0000507493,0.0001369658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004200965,0.0002121684,0.00016173044,0.000054452805,0.00017857889,0.00007682774,0.00009141077,0.00009761534,0.00006800935],"category_scores_gemma":[0.000024417037,0.00021297655,0.0000726701,0.0003570831,0.000022923217,0.00015975031,0.000019958034,0.0001733919,0.0000055186392],"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":[0.000014248962,0.000017488408,0.00084556383,0.000008486104,0.000008604419,0.000026036694,0.000037864116,0.9188377,0.00040382033,0.00010421076,0.000021229976,0.07967472],"study_design_scores_gemma":[0.00014210735,0.000041661075,0.0001675365,0.0000525791,0.000015652879,0.000010238746,0.000019873658,0.9882414,0.010987482,0.000022093926,0.000047234636,0.0002521737],"about_ca_topic_score_codex":0.000014656694,"about_ca_topic_score_gemma":0.000040295483,"teacher_disagreement_score":0.61434156,"about_ca_system_score_codex":0.00012801244,"about_ca_system_score_gemma":0.000015726642,"threshold_uncertainty_score":0.86849314},"labels":[],"label_agreement":null},{"id":"W2118996143","doi":"","title":"Survey of Demand fluctuation of Dairy products at raipur Dugdh Sangh, Chhattisgarh, India for selection of appropriate forecasting","year":2013,"lang":"en","type":"article","venue":"Uncertain Supply Chain Management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":3,"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":"Selection (genetic algorithm); Business; Statistics; Agricultural economics; Mathematics; Economics; Computer science","score_opus":0.020292946679914547,"score_gpt":0.21680746359106445,"score_spread":0.1965145169111499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2118996143","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":[0.9774854,0.000312351,0.018354625,0.000031536212,0.00039559978,0.0015813258,0.000059121605,0.00008734744,0.0016926556],"genre_scores_gemma":[0.9902283,0.00005080142,0.008710515,0.000007146256,0.000066096,0.00016958248,0.00027769853,0.000047719328,0.00044211766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841905,0.00007322589,0.0006364801,0.0002736833,0.0002676783,0.00032991348],"domain_scores_gemma":[0.9989819,0.00017983126,0.00027793468,0.00024673066,0.00026909597,0.000044502707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008975274,0.00021901327,0.0003597495,0.00027348645,0.00006137908,0.000013394232,0.0001802053,0.0000833798,0.00008247586],"category_scores_gemma":[0.00014833767,0.00021837393,0.00006973068,0.00051446015,0.000048668095,0.00014371071,0.00008294264,0.00007136195,0.0000022707668],"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":[0.00022995695,0.00021916426,0.09340525,0.011255106,0.001040278,0.0000027745812,0.0042302716,0.6416735,0.06446672,0.0016005152,0.006243818,0.17563263],"study_design_scores_gemma":[0.0021298805,0.0003360415,0.17363922,0.0005749633,0.00013694493,0.0000037672369,0.0003795941,0.6735183,0.14701243,0.00077301543,0.00088700943,0.0006088693],"about_ca_topic_score_codex":0.0003856652,"about_ca_topic_score_gemma":0.00016165386,"teacher_disagreement_score":0.17502375,"about_ca_system_score_codex":0.00009941006,"about_ca_system_score_gemma":0.000015344338,"threshold_uncertainty_score":0.890503},"labels":[],"label_agreement":null},{"id":"W2121690610","doi":"10.1109/pmaps.2010.5528949","title":"Electricity market clearing price forecasting in a deregulated electricity market","year":2010,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Electricity market; Market clearing; Bidding; Electricity; Clearing; Electricity price forecasting; Artificial neural network; Computer science; Market price; Economics; Microeconomics; Artificial intelligence; Engineering; Finance","score_opus":0.008672922106375774,"score_gpt":0.19066279879480216,"score_spread":0.1819898766884264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121690610","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":[0.70334744,0.0000617142,0.0023373463,0.000014925422,0.00029182332,0.00012655051,0.0000013270541,0.0005561493,0.2932627],"genre_scores_gemma":[0.9940545,0.000023816881,0.004088994,0.00004919557,0.0001494698,0.000015777929,0.0000043532027,0.000072115676,0.0015417597],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99810034,0.00004517958,0.0004397232,0.00033076463,0.00021133812,0.0008726831],"domain_scores_gemma":[0.9991885,0.00028462888,0.00005562292,0.00027368285,0.000048504742,0.00014909559],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006830503,0.0003020205,0.00029495434,0.00034114698,0.000107831926,0.00008191652,0.00026974562,0.00024369836,0.001117978],"category_scores_gemma":[0.00028204656,0.0003082433,0.00008147623,0.0011919598,0.000023348894,0.00026740626,0.000063132145,0.00096755713,0.000012499669],"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":[0.0005088773,0.0005001578,0.23228511,0.00095022295,0.00044341845,0.00042474264,0.001426113,0.052119233,0.3152705,0.005052626,0.035944533,0.35507444],"study_design_scores_gemma":[0.00049650634,0.00003160936,0.03307485,0.000054988588,0.00001008067,0.00008466969,0.000013993767,0.943553,0.018346064,0.0002837143,0.0035710777,0.000479483],"about_ca_topic_score_codex":0.00020744657,"about_ca_topic_score_gemma":0.0011325667,"teacher_disagreement_score":0.8914337,"about_ca_system_score_codex":0.0000961731,"about_ca_system_score_gemma":0.000033041822,"threshold_uncertainty_score":0.99993694},"labels":[],"label_agreement":null},{"id":"W2121955751","doi":"10.1109/pesw.2001.916876","title":"ANN-based short-term load forecasting in electricity markets","year":2002,"lang":"en","type":"article","venue":"2001 IEEE Power Engineering Society Winter Meeting. Conference Proceedings (Cat. No.01CH37194)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":152,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Electricity; Term (time); Computer science; Electricity market; Artificial neural network; Electric power system; Electricity system; Electricity price; Operations research; Electricity generation; Artificial intelligence; Engineering; Power (physics); Electrical engineering","score_opus":0.020389564012500173,"score_gpt":0.20249291080940165,"score_spread":0.1821033467969015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121955751","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":[0.9673499,0.00014725896,0.0075633703,0.00009489601,0.0016523474,0.00046921975,0.000021936325,0.0017429695,0.020958116],"genre_scores_gemma":[0.97331196,0.00021501981,0.02484647,0.00016703717,0.0005084672,0.00014186409,0.000014944622,0.0002851257,0.00050912605],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99485403,0.000017634346,0.0011366153,0.0010219481,0.0007818202,0.002187945],"domain_scores_gemma":[0.99822265,0.00022488713,0.00017346193,0.00036492583,0.0005592543,0.0004548041],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009540338,0.0011118561,0.000886968,0.000329021,0.00018587786,0.0004076229,0.00087531836,0.0005645453,0.00033752978],"category_scores_gemma":[0.00037291826,0.0012622806,0.0005013277,0.0011170884,0.00010561474,0.00072998094,0.00011639599,0.0010623864,0.00008806243],"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":[0.000129913,0.00059009413,0.04587563,0.0029070391,0.0006541885,0.00013516244,0.019518707,0.091742516,0.7883809,0.00007943503,0.042734195,0.007252173],"study_design_scores_gemma":[0.0007886454,0.00011821246,0.0010380008,0.0014881387,0.000044580614,0.000044999662,0.00021807464,0.98046327,0.013131043,0.000009613531,0.001314346,0.0013410602],"about_ca_topic_score_codex":0.000033458684,"about_ca_topic_score_gemma":0.000014633492,"teacher_disagreement_score":0.88872075,"about_ca_system_score_codex":0.0009296825,"about_ca_system_score_gemma":0.00008585232,"threshold_uncertainty_score":0.99898267},"labels":[],"label_agreement":null},{"id":"W2124189150","doi":"10.1109/5326.971659","title":"Deregulated electricity market data representation by fuzzy regression models","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":34,"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 British Columbia","funders":"University of British Columbia; BC Hydro","keywords":"Fuzzy logic; Econometrics; Electricity market; Flexibility (engineering); Regression analysis; Computer science; Electricity; Fuzzy set; Mathematical optimization; Economics; Mathematics; Statistics; Artificial intelligence; Engineering","score_opus":0.03784609290652268,"score_gpt":0.2631781714748144,"score_spread":0.22533207856829174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124189150","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":[0.012789007,0.06088815,0.9056012,0.00011401489,0.000381536,0.0014481217,0.00019496439,0.00034246585,0.01824054],"genre_scores_gemma":[0.8425986,0.15321694,0.00033585267,0.000036358528,0.00007718432,0.00031294106,0.000109656765,0.000038941875,0.0032735183],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99880594,0.00007480619,0.00043108876,0.00036385463,0.0001250069,0.00019928942],"domain_scores_gemma":[0.9991143,0.000056334164,0.00008210212,0.0005865426,0.000031865893,0.00012887664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023308644,0.00020597271,0.00028330364,0.00007901269,0.00023230715,0.00008454089,0.00014837488,0.00011241311,0.000019587189],"category_scores_gemma":[0.0000016172119,0.00017406879,0.000036477333,0.00031210022,0.000042154912,0.00016937389,0.0000036680085,0.00016827902,0.000010458133],"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":[0.000048017977,0.00032573444,0.00013599303,0.0011222084,0.00026000896,0.000004938238,0.0003820974,0.066171736,0.004712906,0.0020266455,0.08903955,0.8357702],"study_design_scores_gemma":[0.00031420734,0.00003161551,0.000025217898,0.0004309547,0.00011229403,0.00008306859,0.00004793899,0.411488,0.0006255915,0.00016094075,0.58634007,0.0003400969],"about_ca_topic_score_codex":0.000046906614,"about_ca_topic_score_gemma":0.000023487157,"teacher_disagreement_score":0.90526533,"about_ca_system_score_codex":0.00002021978,"about_ca_system_score_gemma":0.0000061030773,"threshold_uncertainty_score":0.7098319},"labels":[],"label_agreement":null},{"id":"W2124535468","doi":"10.1109/smcia.2003.1231336","title":"Free electricity market: how industrial customers and ESCOs can make the most from load forecasting techniques","year":2003,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Electricity; Computer science; Process (computing); Identification (biology); Industrial engineering; Point (geometry); Electrical load; Artificial neural network; Operations research; Quarter (Canadian coin); Engineering; Artificial intelligence; Electrical engineering","score_opus":0.018968553449266835,"score_gpt":0.18916747371960912,"score_spread":0.17019892027034228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124535468","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":[0.5678383,0.0009445446,0.004267906,0.0004318951,0.0008028307,0.0005039138,0.000112017806,0.0011837904,0.4239148],"genre_scores_gemma":[0.9968776,0.00006475093,0.0018395698,0.00011189682,0.00024559797,0.000022678176,0.000006198355,0.000044957553,0.0007867484],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99897057,0.000056701145,0.00017707472,0.00020782233,0.00019702503,0.00039081453],"domain_scores_gemma":[0.99931884,0.00024199643,0.00004107003,0.00027127966,0.000035057157,0.000091750175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003374349,0.00022619563,0.00018541684,0.00006558524,0.00015456295,0.00012198288,0.00019602873,0.00017267127,0.00007820425],"category_scores_gemma":[0.00034642083,0.00016909435,0.000041435524,0.00034278395,0.000047568967,0.00006748611,0.000044782708,0.0004008928,6.0770805e-7],"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":[0.000065391716,0.000047303223,0.018343564,0.000062883046,0.00041874478,0.00006534577,0.0014237728,0.0017029328,0.0123641705,0.0038047892,0.06631601,0.8953851],"study_design_scores_gemma":[0.0028924192,0.00023243621,0.00082240894,0.0003503321,0.00020319552,0.00015905546,0.0010496441,0.11638423,0.3743649,0.0025323515,0.49879152,0.0022175123],"about_ca_topic_score_codex":0.0007182512,"about_ca_topic_score_gemma":0.0016757373,"teacher_disagreement_score":0.89316756,"about_ca_system_score_codex":0.00009506031,"about_ca_system_score_gemma":0.000047202015,"threshold_uncertainty_score":0.6895467},"labels":[],"label_agreement":null},{"id":"W2126328815","doi":"10.1109/tec.2004.827715","title":"Nonlinear Model Identification of Wind Turbine With a Neural Network","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Energy Conversion","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":96,"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 du Québec à Trois-Rivières","funders":"","keywords":"Anemometer; Wind speed; Turbine; Wind power; Control theory (sociology); Artificial neural network; Nonlinear system; Standard deviation; Computer science; Engineering; Meteorology; Mathematics; Statistics; Artificial intelligence; Physics","score_opus":0.008752681741895476,"score_gpt":0.18833762883456756,"score_spread":0.1795849470926721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126328815","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":[0.32394317,0.000036161066,0.67508346,0.000026053414,0.00039575066,0.000031929754,0.000010812951,0.00013824516,0.00033444905],"genre_scores_gemma":[0.997727,0.00006292314,0.0018692262,0.000036971785,0.00005504195,0.0000042360293,0.000012164236,0.000032462132,0.00019999518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992879,0.000009222862,0.0002143915,0.00015008988,0.00015730342,0.0001810724],"domain_scores_gemma":[0.9996511,0.000018750714,0.00004458522,0.00018336209,0.00004189589,0.00006031607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004553865,0.00014469754,0.00013521628,0.000097253345,0.00008786495,0.000010475687,0.00008897821,0.00007964198,0.000015888401],"category_scores_gemma":[4.4718217e-7,0.00013514033,0.00006490414,0.0002588025,0.000037952213,0.00015901132,5.901229e-7,0.0001271939,0.0000045428064],"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":[0.00004890058,0.000041583346,0.0000026010187,0.000019689383,0.000032154057,0.000002548602,0.000087353015,0.9840022,0.013615861,0.000104636594,0.000016449874,0.0020260452],"study_design_scores_gemma":[0.00055552507,0.0000808785,0.0000068316435,0.000057842084,0.00003017722,0.000006815802,0.000012955594,0.73993325,0.2590146,0.00004510616,0.00013130988,0.00012466844],"about_ca_topic_score_codex":0.000056588877,"about_ca_topic_score_gemma":0.00004892497,"teacher_disagreement_score":0.67378384,"about_ca_system_score_codex":0.000055516917,"about_ca_system_score_gemma":0.00002233936,"threshold_uncertainty_score":0.5510863},"labels":[],"label_agreement":null},{"id":"W2130107713","doi":"10.1109/ccece.2009.5090183","title":"Load and locational marginal pricing prediction in competitive electrical power environment using computational intelligence","year":2009,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Particle swarm optimization; Computer science; Artificial neural network; Data pre-processing; Generalization; Preprocessor; Electrical load; Radial basis function; Electric power system; Artificial intelligence; Function (biology); Data mining; Mathematical optimization; Machine learning; Power (physics); Mathematics","score_opus":0.008938839330126578,"score_gpt":0.2041268653638088,"score_spread":0.19518802603368224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130107713","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":[0.41896492,0.00029660176,0.56838113,0.000044640332,0.00006845964,0.00008048691,0.000003257529,0.00008325145,0.012077222],"genre_scores_gemma":[0.9872176,0.000018197186,0.012653883,0.000040312865,0.000030510102,0.0000013025725,0.00000954662,0.0000060542056,0.000022609081],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993888,0.00001438655,0.00016875853,0.00012420943,0.0001573652,0.00014647823],"domain_scores_gemma":[0.99983895,0.000056050765,0.000014312575,0.000034787165,0.000014094964,0.0000418223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010669638,0.00008947517,0.00007851669,0.00007695294,0.000043388696,0.000017110822,0.00003180363,0.000039641738,0.00008416554],"category_scores_gemma":[0.000008158237,0.00009381945,0.000012275375,0.00010857367,0.000020787444,0.00010502281,0.000008414078,0.0001401625,0.0000066400867],"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":[0.000005999032,0.000022327025,0.0037061924,0.000002770865,0.0000053311414,0.000002620425,0.00020511435,0.9738199,0.0006463861,0.017494863,0.000011413718,0.0040770904],"study_design_scores_gemma":[0.00010688182,0.00004545577,0.05352041,0.000034690656,0.0000029098553,0.000032727818,0.00005160899,0.94414896,0.0005881428,0.001137164,0.0002270028,0.000104054925],"about_ca_topic_score_codex":0.000008127996,"about_ca_topic_score_gemma":0.0000033689798,"teacher_disagreement_score":0.5682527,"about_ca_system_score_codex":0.00019647402,"about_ca_system_score_gemma":0.00001814055,"threshold_uncertainty_score":0.3825846},"labels":[],"label_agreement":null},{"id":"W2130710221","doi":"10.1109/pacrim.1997.620387","title":"Development of an integrated intelligent system","year":2002,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Electric power system; Computer science; Controller (irrigation); Voltage; Electric power transmission; Expert system; AC power; Operator (biology); Control engineering; Transmission system; Artificial neural network; Power transmission; High voltage; Power (physics); Transmission (telecommunications); Systems engineering; Electrical engineering; Engineering; Telecommunications; Artificial intelligence","score_opus":0.024241353084908348,"score_gpt":0.1980562008053722,"score_spread":0.17381484772046385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130710221","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":[0.8117465,0.00020777574,0.049646236,0.0000016521079,0.00037459523,0.000046758847,0.0000013383677,0.0006188565,0.1373563],"genre_scores_gemma":[0.9652941,0.0000043584037,0.03435524,0.0000027687927,0.000015857298,0.000002811748,0.000004219308,0.000011517683,0.0003090872],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959385,0.000004595178,0.00018820296,0.000054831486,0.000060106824,0.00009843316],"domain_scores_gemma":[0.99984235,0.000006479988,0.000011140521,0.00008105323,0.000016434295,0.00004252961],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000050075643,0.00006753592,0.00008144686,0.000033004217,0.000016581454,0.000006532098,0.00006593444,0.000029026476,0.00024568156],"category_scores_gemma":[0.0000021353032,0.00005533338,0.0000146103985,0.00009222043,0.0000052083233,0.000042598742,0.000007502995,0.000040996598,0.000046673573],"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":[0.0000033892843,0.00007471256,0.00013239215,0.00040041097,0.00011505322,0.000011939899,0.006784831,0.07352076,0.012888807,0.006848279,0.0007307373,0.8984887],"study_design_scores_gemma":[0.00008336323,0.00001720821,0.00003791503,0.00013561976,0.000004048389,0.0000095950145,0.0011948912,0.682356,0.26206163,0.0000017409925,0.053930502,0.00016747846],"about_ca_topic_score_codex":0.0000056201493,"about_ca_topic_score_gemma":0.000028922941,"teacher_disagreement_score":0.8983212,"about_ca_system_score_codex":0.000038271635,"about_ca_system_score_gemma":0.0000028828624,"threshold_uncertainty_score":0.26900405},"labels":[],"label_agreement":null},{"id":"W2131854103","doi":"","title":"Electricity Price Forecasting in Ontario Electricity Market Using Wavelet Transform in Artificial Neural Network Based Model","year":2008,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":58,"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":"Autoregressive integrated moving average; Artificial neural network; Electricity market; Heuristic; Wavelet; Computer science; Econometrics; Wavelet transform; Time series; Electricity; Artificial intelligence; Machine learning; Mathematics; Engineering","score_opus":0.042337420990551655,"score_gpt":0.205533934489411,"score_spread":0.16319651349885936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131854103","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":[0.89303344,0.00005209601,0.08144119,0.000013149982,0.0001431793,0.0002167533,0.0000016967281,0.0001906055,0.024907887],"genre_scores_gemma":[0.9892385,0.000007087357,0.010328842,0.00009342453,0.00010510883,0.000013751502,0.0000100154875,0.000054136555,0.00014915392],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99765116,0.00004589556,0.00064373977,0.00032620804,0.00024132832,0.0010916515],"domain_scores_gemma":[0.9995049,0.00014998319,0.000049360668,0.00015809176,0.000029454086,0.00010821191],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004426716,0.0003341745,0.00039691845,0.00031963747,0.0001438483,0.000029307983,0.00017244389,0.0001887406,0.000101891244],"category_scores_gemma":[0.000032567885,0.00035783552,0.000104441664,0.0011854884,0.0000231145,0.0002920706,0.000015911133,0.00081037724,0.000001138572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073982774,0.000044425833,0.014832553,0.000019759556,0.000007611108,0.0000663538,0.00029222685,0.9816329,0.00062913843,0.000063612526,0.000095575575,0.0022418776],"study_design_scores_gemma":[0.00046069667,0.00003553289,0.0044758427,0.000048966274,0.0000068424006,0.00004105496,0.0000048494544,0.9921452,0.002034773,0.00028644045,0.000057713783,0.00040212035],"about_ca_topic_score_codex":0.014496476,"about_ca_topic_score_gemma":0.3029758,"teacher_disagreement_score":0.28847933,"about_ca_system_score_codex":0.00076564203,"about_ca_system_score_gemma":0.00022220607,"threshold_uncertainty_score":0.99988735},"labels":[],"label_agreement":null},{"id":"W2132421074","doi":"10.1109/icps.2007.4292100","title":"PCA-based Least Squares Support Vector Machines in Week-Ahead Load Forecasting","year":2007,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Independent Electricity System Operator","keywords":"Support vector machine; Computer science; Principal component analysis; Artificial neural network; Feature extraction; Artificial intelligence; Least squares support vector machine; Electricity; Feature (linguistics); Data mining; Machine learning; Pattern recognition (psychology); Engineering","score_opus":0.02242372112217885,"score_gpt":0.23026608279619612,"score_spread":0.20784236167401726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132421074","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":[0.82534504,0.00022315654,0.016693106,0.000065012275,0.00088130526,0.00012673097,0.000009234459,0.0007253322,0.15593112],"genre_scores_gemma":[0.9961267,0.0000024230221,0.0028211593,0.000096326745,0.00025237128,0.000006965768,0.000022383494,0.0000588669,0.00061277463],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850595,0.0000126857585,0.0004173767,0.00021681987,0.0002354171,0.0006117736],"domain_scores_gemma":[0.99944097,0.00017279893,0.000033326203,0.00018563835,0.000038786726,0.00012846658],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049000635,0.00024748634,0.0002282086,0.00020419173,0.00005788929,0.00003968167,0.00015557559,0.00011353089,0.00045908967],"category_scores_gemma":[0.00007491174,0.00023439621,0.00008024255,0.00035253807,0.000027667635,0.00014777441,0.000024438305,0.00024364167,0.000054427244],"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":[0.00020452205,0.00033123756,0.1626929,0.0007497675,0.00009633287,0.00081881625,0.0020444673,0.47245735,0.015654808,0.0028366256,0.0058013797,0.3363118],"study_design_scores_gemma":[0.0023226442,0.00029779723,0.040048786,0.00040616884,0.00002395514,0.00008409821,0.00026920243,0.8845118,0.037705176,0.00017304788,0.032782182,0.0013751183],"about_ca_topic_score_codex":0.00037076932,"about_ca_topic_score_gemma":0.003983228,"teacher_disagreement_score":0.41205448,"about_ca_system_score_codex":0.00013296741,"about_ca_system_score_gemma":0.000043453434,"threshold_uncertainty_score":0.95584},"labels":[],"label_agreement":null},{"id":"W2133426918","doi":"10.1260/0309-524x.35.3.369","title":"Wind Speed Prediction for a Target Station Using Neural Networks and Particle Swarm Optimization","year":2011,"lang":"en","type":"article","venue":"Wind Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Particle swarm optimization; Artificial neural network; MATLAB; Wind speed; Computer science; Meteorology; Artificial intelligence; Machine learning; Geography","score_opus":0.019654800170293096,"score_gpt":0.1906000481606321,"score_spread":0.17094524799033903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133426918","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":[0.51659256,0.00014542231,0.48228875,0.0000015686385,0.0006122256,0.000091086855,0.000007121677,0.00020556737,0.000055692883],"genre_scores_gemma":[0.973027,0.000012043357,0.02662533,0.0000058271103,0.00024228505,0.0000020155983,0.00003026834,0.00004785418,0.0000073959427],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993891,0.000004700393,0.00017634375,0.0001232541,0.000056959492,0.00024962684],"domain_scores_gemma":[0.99978757,0.00002384694,0.000021456868,0.000072657545,0.000022251046,0.00007224156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000871496,0.00013138188,0.00010466894,0.000052943436,0.000057026147,0.000030582185,0.00003620621,0.00006882456,0.000010444862],"category_scores_gemma":[0.000016396612,0.00014897334,0.000027103151,0.00012085611,0.000007022559,0.00028946332,0.000010701001,0.00008226148,2.6831708e-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":[0.000008159021,0.0000046504774,0.0008338096,0.000040690225,0.000020663665,0.0000010748082,0.00042822197,0.9965232,0.0016918799,0.000049551076,0.000009137069,0.00038894004],"study_design_scores_gemma":[0.00028189484,0.000030874064,0.0007410347,0.000032704873,0.000021475844,0.000006887232,0.000030348854,0.9961034,0.0025271708,0.000009606818,0.00006921432,0.0001453992],"about_ca_topic_score_codex":0.000009528635,"about_ca_topic_score_gemma":7.249069e-7,"teacher_disagreement_score":0.4564344,"about_ca_system_score_codex":0.000031963275,"about_ca_system_score_gemma":0.000003019082,"threshold_uncertainty_score":0.6074956},"labels":[],"label_agreement":null},{"id":"W2133604230","doi":"10.1109/pes.2011.6039625","title":"Wind power ramp events classification and forecasting: A data mining approach","year":2011,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Wind power; Support vector machine; Wind power forecasting; Data mining; Computer science; Data set; Data modeling; Wind speed; Classifier (UML); Set (abstract data type); Machine learning; Power (physics); Electric power system; Artificial intelligence; Meteorology; Engineering; Database; Geography","score_opus":0.14789672543276552,"score_gpt":0.23939399688577517,"score_spread":0.09149727145300965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133604230","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":[0.598686,0.00018160346,0.017214976,0.0000071059826,0.00024397617,0.000088583976,0.0000098036435,0.00028613867,0.38328186],"genre_scores_gemma":[0.9595569,0.000010500167,0.039949644,0.000019612722,0.000042707125,0.00000307235,0.00006197035,0.000027653796,0.0003279484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992893,0.00001182831,0.00018555629,0.00022517599,0.00008876649,0.00019938439],"domain_scores_gemma":[0.9994936,0.00003023611,0.0000294966,0.00036022757,0.000014139649,0.00007227744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019751248,0.00012114528,0.000108695,0.00005569883,0.000051980594,0.000019635698,0.00020361674,0.00006686919,0.00007165528],"category_scores_gemma":[0.000034610617,0.00011067147,0.000014392794,0.000099239354,0.000018785375,0.00026939792,0.00009442247,0.000084547726,0.000006230244],"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":[0.00019688148,0.0007204902,0.34190732,0.001156151,0.0011495992,0.00004697435,0.036684237,0.0056457254,0.022181615,0.032295786,0.023872314,0.5341429],"study_design_scores_gemma":[0.0006809086,0.000053177828,0.05015008,0.00008673014,0.00003656699,0.00007584159,0.0014381201,0.9384771,0.00085060165,0.00020813145,0.007316083,0.00062667765],"about_ca_topic_score_codex":0.00001096665,"about_ca_topic_score_gemma":0.000005215419,"teacher_disagreement_score":0.93283135,"about_ca_system_score_codex":0.000009603348,"about_ca_system_score_gemma":0.0000060009884,"threshold_uncertainty_score":0.45130515},"labels":[],"label_agreement":null},{"id":"W2135832440","doi":"10.1109/pesmg.2013.6672955","title":"Intelligent Wind Generator models for power flow studies in PSS&amp;#x00AE;E and PSS&amp;#x00AE;SINCAL","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University","funders":"","keywords":"Generator (circuit theory); Nonlinear system; Permanent magnet synchronous generator; Computer science; Three-phase; Control theory (sociology); Wind power; Induction generator; AC power; Artificial neural network; Power (physics); Voltage; Electric power system; Control engineering; Electronic engineering; Engineering; Electrical engineering; Artificial intelligence","score_opus":0.06987408191128211,"score_gpt":0.2754108873585475,"score_spread":0.2055368054472654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2135832440","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":[0.893461,0.0055057476,0.092988566,0.00017356257,0.0012636741,0.00056267605,0.000027101818,0.00035975323,0.0056579397],"genre_scores_gemma":[0.9397687,0.0006065056,0.056133498,0.00024948496,0.0003075865,0.00012579482,0.000031356227,0.00010564756,0.0026714704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983194,0.000025826435,0.00049859774,0.00038163358,0.0001760538,0.00059851666],"domain_scores_gemma":[0.9992009,0.000181519,0.00003507665,0.00029069744,0.000110341505,0.00018148612],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021653346,0.0003804891,0.0004383374,0.00018193916,0.000099983175,0.00009952548,0.00015848347,0.00017189058,0.00022205086],"category_scores_gemma":[0.00006605306,0.00032683677,0.00009650104,0.00015588524,0.000081299586,0.00039509276,0.00010232683,0.0002130223,0.0000772008],"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":[0.000048369922,0.0001486778,0.0016289904,0.00048451274,0.00050576666,0.000006395852,0.010035444,0.9143083,0.011261919,0.006686463,0.029082134,0.025803037],"study_design_scores_gemma":[0.001992639,0.00021338479,0.00041749113,0.00046559537,0.00007973429,0.000055148066,0.0014625335,0.8090258,0.009911805,0.012199173,0.1620979,0.0020787818],"about_ca_topic_score_codex":0.00009743471,"about_ca_topic_score_gemma":0.00074469007,"teacher_disagreement_score":0.13301577,"about_ca_system_score_codex":0.000107465414,"about_ca_system_score_gemma":0.000020140798,"threshold_uncertainty_score":0.99991834},"labels":[],"label_agreement":null},{"id":"W2138674411","doi":"10.1139/l03-049","title":"Modélisation non paramétrique de la relation entre les caractéristiques du vent et la différence de niveaux sur un grand réservoir","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Inflow; Hydroelectricity; Water level; Nonparametric statistics; Hydrology (agriculture); Environmental science; Regression analysis; Regression; Nonparametric regression; Geology; Statistics; Meteorology; Mathematics; Geography; Geotechnical engineering; Engineering","score_opus":0.0048226022075622335,"score_gpt":0.1852512461661584,"score_spread":0.18042864395859617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2138674411","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":[0.7906864,0.0012147723,0.20154427,0.000033176126,0.00024123865,0.00003784718,0.000005198852,0.000041603016,0.0061954483],"genre_scores_gemma":[0.9984147,0.00025229072,0.0011709947,0.000012631619,0.00009716253,0.0000024171202,0.0000033730346,0.000036454952,0.0000099855315],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992602,0.00008265811,0.00023734174,0.00006779996,0.00008661651,0.0002653747],"domain_scores_gemma":[0.9993041,0.00020182042,0.00006508159,0.000070119095,0.000052469022,0.00030637992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005945326,0.00014351397,0.00015439196,0.00020523462,0.000055824574,0.000072685536,0.00009430621,0.00013296895,0.000030901952],"category_scores_gemma":[0.00027271564,0.00015952029,0.00006853351,0.00012368296,0.0000168015,0.00022206431,0.0000026219145,0.00039132638,4.967555e-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":[0.000001875472,0.0000036508961,0.01437891,0.00004172029,0.000029643745,0.00013817698,0.0013138094,0.9807374,0.0011918643,0.0014275181,0.00012046027,0.0006149643],"study_design_scores_gemma":[0.0010721151,0.00007255633,0.11826735,0.0012162694,0.00008272114,0.0014544183,0.00014996009,0.8308986,0.010607787,0.0011556369,0.034347437,0.00067514315],"about_ca_topic_score_codex":0.0006568554,"about_ca_topic_score_gemma":0.011796081,"teacher_disagreement_score":0.20772824,"about_ca_system_score_codex":0.0002729284,"about_ca_system_score_gemma":0.00027477022,"threshold_uncertainty_score":0.658249},"labels":[],"label_agreement":null},{"id":"W2143724371","doi":"10.1155/2014/972580","title":"Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting","year":2014,"lang":"en","type":"article","venue":"International Scholarly Research Notices","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"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; Waterloo CFD Engineering Consulting; University of Waterloo","funders":"","keywords":"Initialization; Artificial neural network; Computer science; Wind speed; Autoregressive model; Wind power; Nonlinear system; Nonlinear autoregressive exogenous model; Particle swarm optimization; Wind power forecasting; Term (time); Power (physics); Meteorology; Artificial intelligence; Machine learning; Electric power system; Econometrics; Mathematics; Engineering","score_opus":0.07826748943136302,"score_gpt":0.32530981460463626,"score_spread":0.24704232517327324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143724371","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":[0.9886879,0.00033789303,0.0037075267,0.00016709529,0.0015525548,0.00025580666,0.000028232449,0.00012702555,0.005136008],"genre_scores_gemma":[0.99631864,0.000012904904,0.0017201802,0.00003204638,0.0015983114,0.000010624143,0.000055285916,0.000054386885,0.00019759792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980491,0.00008666716,0.00032511522,0.00033155805,0.00060289184,0.0006046677],"domain_scores_gemma":[0.9982519,0.00084828545,0.0000454202,0.00016677081,0.0004982363,0.00018935585],"candidate_categories":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0012897808,0.0001939054,0.00018597531,0.00023883958,0.0003442583,0.0011743369,0.00042345063,0.00014452303,0.000051642408],"category_scores_gemma":[0.0008396963,0.00018414386,0.00007320378,0.00013912901,0.000106000865,0.0011207748,0.00014143731,0.00068418117,0.0000073971523],"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":[0.00036453293,0.00014105602,0.022404164,0.00013840591,0.00040552972,0.000103952414,0.0018367915,0.7470454,0.025530474,0.0031718197,0.0007636547,0.1980942],"study_design_scores_gemma":[0.00025311735,0.00016455115,0.0018522204,0.000091044785,0.000009612089,0.000021700223,0.00006453737,0.99123347,0.0013763037,0.00035969887,0.0043611466,0.00021259543],"about_ca_topic_score_codex":0.000026658263,"about_ca_topic_score_gemma":0.00008137354,"teacher_disagreement_score":0.24418806,"about_ca_system_score_codex":0.00007808574,"about_ca_system_score_gemma":0.00002001554,"threshold_uncertainty_score":0.99986255},"labels":[],"label_agreement":null},{"id":"W2144729371","doi":"10.1109/pesmg.2013.6672957","title":"A practical real-time OPF method using new triangular approximate model of wind electric generators","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University","funders":"","keywords":"Monte Carlo method; Probabilistic logic; Wind speed; Wind power; Electric power system; Transmission (telecommunications); Computer science; Economic dispatch; Engineering; Simulation; Power (physics); Mathematical optimization; Real-time computing; Control theory (sociology); Statistics; Electrical engineering; Mathematics","score_opus":0.0370577150171267,"score_gpt":0.2785609968392805,"score_spread":0.24150328182215378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144729371","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29299283,0.000063499065,0.69030863,0.000028345046,0.00010076266,0.00016052519,0.0000014630504,0.00023104099,0.016112916],"genre_scores_gemma":[0.111949444,0.000025427593,0.8864591,0.000024504212,0.00015164318,0.000004154149,0.0000041257344,0.0000646583,0.0013169382],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989348,0.00004181753,0.00035669334,0.00017464944,0.00015663901,0.0003353788],"domain_scores_gemma":[0.9994399,0.000086724875,0.00005846414,0.00021790335,0.000043443233,0.00015358916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024781207,0.00017648804,0.00028039527,0.0001307981,0.00003368682,0.00003823531,0.00009281248,0.00012105956,0.00027076958],"category_scores_gemma":[0.00006146134,0.00015745695,0.00007978044,0.00031900132,0.000008494672,0.00027315592,0.000026167801,0.00013568669,0.00002451543],"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":[0.0000035263158,0.000011680204,0.000008806605,0.000021915086,0.00004488585,0.0000014218055,0.000082009916,0.47906846,0.5154222,0.001598356,0.0013897577,0.0023469736],"study_design_scores_gemma":[0.00021508276,0.000015869815,0.0000024677763,0.000011081466,0.000030574654,0.000013886381,0.000008266594,0.8234722,0.17548093,0.0005327247,0.000064726126,0.00015218585],"about_ca_topic_score_codex":0.0003448377,"about_ca_topic_score_gemma":0.0000016567664,"teacher_disagreement_score":0.34440374,"about_ca_system_score_codex":0.000042229534,"about_ca_system_score_gemma":0.000082663515,"threshold_uncertainty_score":0.6420908},"labels":[],"label_agreement":null},{"id":"W2144876719","doi":"10.1109/tpwrd.2002.807462","title":"An approach to implement electricity metering in real-time using artificial neural networks","year":2003,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Metering mode; Electricity; Artificial neural network; Electric power system; Computer science; Electric power; Electricity market; Process (computing); Automotive engineering; Real-time computing; Engineering; Power (physics); Artificial intelligence; Electrical engineering","score_opus":0.02579802336824112,"score_gpt":0.24055373960289692,"score_spread":0.21475571623465578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144876719","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":[0.5193604,0.000014610934,0.4786143,0.0000012162687,0.00038257215,0.00010585217,0.0000065188433,0.00015044425,0.0013640329],"genre_scores_gemma":[0.99503344,0.000015612648,0.0047768694,0.00004265522,0.00003647453,0.000023393332,0.000003689419,0.00005664964,0.000011203628],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857926,0.000086123764,0.00034507358,0.00029787872,0.0001593827,0.00053225824],"domain_scores_gemma":[0.99950546,0.000046033027,0.000023025399,0.00024541531,0.000025064728,0.0001549863],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023233193,0.00024210717,0.00022547162,0.00031809873,0.00013044702,0.00005869726,0.00012135532,0.000103321974,0.000075259624],"category_scores_gemma":[0.0000017908648,0.00027405427,0.00009024631,0.00056699954,0.000010049147,0.00022985009,8.4525544e-7,0.00029991666,0.000008235982],"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":[0.000025144675,0.00010549952,0.000011746861,0.0000059516146,0.000024060533,0.0000065125514,0.00020746017,0.9571954,0.039055973,0.000059250582,0.000008773901,0.0032942188],"study_design_scores_gemma":[0.00017662348,0.00010755982,0.000047861235,0.000017836113,0.000021151147,0.000019059979,0.000057127374,0.9701049,0.029042244,0.000012515342,0.00006667704,0.00032644378],"about_ca_topic_score_codex":0.00015782223,"about_ca_topic_score_gemma":0.00006261121,"teacher_disagreement_score":0.475673,"about_ca_system_score_codex":0.00019600014,"about_ca_system_score_gemma":0.000016515318,"threshold_uncertainty_score":0.99997115},"labels":[],"label_agreement":null},{"id":"W2148149982","doi":"10.1109/ccece.2003.1226242","title":"A new approach using artificial neural network and time series models for short term load forecasting","year":2004,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"McMaster University","funders":"","keywords":"Artificial neural network; Term (time); Computer science; Time series; Series (stratigraphy); Process (computing); Approximation error; Artificial intelligence; Machine learning; Algorithm","score_opus":0.04940162912456559,"score_gpt":0.22637798696014869,"score_spread":0.1769763578355831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148149982","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":[0.1647169,0.00023516684,0.81780934,0.000015825279,0.00026163855,0.00021368293,0.0000046440027,0.00036051104,0.016382277],"genre_scores_gemma":[0.7567654,0.000003975674,0.2420542,0.000021194544,0.0008322493,0.000010451109,0.000014384339,0.000057945203,0.00024018553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900705,0.0000062544955,0.00023611114,0.00021480235,0.00010398115,0.00043180984],"domain_scores_gemma":[0.9996938,0.000034219567,0.000017985796,0.000112591035,0.000024796447,0.000116600146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015515392,0.00020203376,0.00020696977,0.00003440087,0.00015456381,0.00010220705,0.00007610947,0.00008858624,0.000011862168],"category_scores_gemma":[0.000010790623,0.00019571754,0.00006049461,0.0001242128,0.000022364746,0.00039604265,0.000037734964,0.00010044479,0.0000013315611],"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":[0.00001734468,0.0000043608206,0.000033172222,0.00003912557,0.000022623162,0.000002452324,0.00022138104,0.9791369,0.0010141651,0.00513477,0.00008857576,0.014285121],"study_design_scores_gemma":[0.0001695286,0.00002669297,0.0000053354984,0.000046091074,0.000025063635,0.000091350485,0.00002698986,0.9895038,0.0009319465,0.008838266,0.00009089234,0.00024403936],"about_ca_topic_score_codex":0.000040909967,"about_ca_topic_score_gemma":0.00003319321,"teacher_disagreement_score":0.5920485,"about_ca_system_score_codex":0.00005906472,"about_ca_system_score_gemma":0.000039317172,"threshold_uncertainty_score":0.7981129},"labels":[],"label_agreement":null},{"id":"W2149906128","doi":"10.1109/tpwrs.2014.2299801","title":"A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":203,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Teshmont (Canada)","funders":"National Renewable Energy Laboratory","keywords":"Wind power; Wind power forecasting; Probabilistic forecasting; Probabilistic logic; Support vector machine; Electric power system; Wind speed; Computer science; Particle swarm optimization; Quantile regression; Engineering; Mathematical optimization; Artificial intelligence; Machine learning; Power (physics); Meteorology; Mathematics","score_opus":0.03460675173194757,"score_gpt":0.23671738082360996,"score_spread":0.20211062909166239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149906128","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":[0.03162325,0.00013516906,0.9642396,0.0000045697952,0.0015997231,0.0010354057,0.00014317637,0.00025546236,0.0009636327],"genre_scores_gemma":[0.99325335,0.000006008884,0.0056966054,0.000014455127,0.0000394591,0.00046240786,0.000014640066,0.00010930615,0.0004037712],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984524,0.00003292207,0.00049168686,0.00041200654,0.0001638458,0.0004471148],"domain_scores_gemma":[0.9989933,0.00038352347,0.00008514799,0.00030134822,0.00008385942,0.00015281595],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037486924,0.00033891335,0.0003918911,0.00016029205,0.00026853447,0.00010670004,0.00013929332,0.00012758639,0.0000034334764],"category_scores_gemma":[0.0000336226,0.00030110497,0.00015524703,0.000080781414,0.000046157318,0.00012702186,0.0000016999619,0.00017500692,0.0000021046667],"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":[0.00006670842,0.00005986791,0.0000016772048,0.0006777244,0.000049799077,7.082615e-7,0.00069042767,0.99519646,0.00044240602,0.00038078037,0.00016795381,0.002265457],"study_design_scores_gemma":[0.0005111979,0.0002844676,6.373169e-7,0.00034863007,0.000061400075,0.00006217514,0.00009957259,0.99566555,0.0014211482,0.00013339186,0.0010620281,0.00034978616],"about_ca_topic_score_codex":0.0000052356504,"about_ca_topic_score_gemma":0.000003993004,"teacher_disagreement_score":0.9616301,"about_ca_system_score_codex":0.00007112372,"about_ca_system_score_gemma":0.00002096138,"threshold_uncertainty_score":0.9999441},"labels":[],"label_agreement":null},{"id":"W2150180277","doi":"10.1002/er.3171","title":"Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet-PSO-NNs","year":2014,"lang":"en","type":"article","venue":"International Journal of Energy Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Particle swarm optimization; Intermittency; Wind power; Wind power forecasting; Wind speed; Artificial neural network; Benchmark (surveying); Hybrid power; Computer science; Power (physics); Electric power system; Meteorology; Engineering; Algorithm; Artificial intelligence; Geography","score_opus":0.056166681914099875,"score_gpt":0.3027093758806047,"score_spread":0.24654269396650483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150180277","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":[0.99017537,0.00025461405,0.0041055707,0.0001045429,0.0023709922,0.00003484098,0.0000074260797,0.000022442628,0.0029242283],"genre_scores_gemma":[0.9967685,0.00006067969,0.0017280784,0.000010965669,0.0011605865,6.6401867e-7,0.000010512314,0.000045775607,0.00021428485],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754906,0.00013547021,0.0006619562,0.00016581669,0.0011228167,0.00036486215],"domain_scores_gemma":[0.99760765,0.0003139744,0.0002461693,0.00015780101,0.0015485489,0.00012586653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013686313,0.00015673129,0.00025130992,0.00073579885,0.00008848582,0.00006952457,0.000494196,0.00007462148,0.00006314508],"category_scores_gemma":[0.0010956521,0.00014346563,0.0001331338,0.00030455028,0.000107139684,0.00036533707,0.00009700735,0.0004778921,0.0000019853842],"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":[0.00026959545,0.00015477325,0.0068665813,0.000052161635,0.00061278977,0.0001565005,0.00056209345,0.7801344,0.16531491,0.0008521911,0.00091252534,0.044111475],"study_design_scores_gemma":[0.0015860938,0.00035093803,0.0011277533,0.0012966987,0.000025015917,0.0015694269,0.00021695034,0.5908843,0.37200406,0.0012751792,0.029262325,0.00040121318],"about_ca_topic_score_codex":0.000077117824,"about_ca_topic_score_gemma":0.000026242655,"teacher_disagreement_score":0.20668916,"about_ca_system_score_codex":0.00018419686,"about_ca_system_score_gemma":0.000080844286,"threshold_uncertainty_score":0.58503586},"labels":[],"label_agreement":null},{"id":"W2150462782","doi":"10.1109/ptc.2009.5282220","title":"Application of generalized neuron in electricity price forecasting","year":2009,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Bidding; Computer science; Artificial neural network; Electricity market; Data modeling; Electricity price forecasting; Electricity; Function (biology); Artificial intelligence; Data set; Relation (database); Econometrics; Machine learning; Data mining; Economics; Microeconomics; Engineering","score_opus":0.0123320764217805,"score_gpt":0.20869757481564882,"score_spread":0.19636549839386833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150462782","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":[0.8805826,0.000112940346,0.076955274,0.000018671575,0.000040882016,0.00007692302,4.2261846e-7,0.00015050583,0.042061783],"genre_scores_gemma":[0.99650276,0.000015201616,0.0033601616,0.00003883628,0.000032853924,0.000003753197,0.000003545483,0.000008046698,0.00003486948],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995104,0.00000802631,0.00018607572,0.000081933045,0.000061255916,0.00015231752],"domain_scores_gemma":[0.9998304,0.000025015024,0.000024537838,0.000084972315,0.000012821148,0.000022246999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000080066646,0.00006959223,0.00010101672,0.000081430575,0.000011234062,0.000004761294,0.00006437756,0.000035894343,0.0000075051216],"category_scores_gemma":[0.000016055148,0.00006901217,0.000021887474,0.00032091243,0.0000034446257,0.00006130831,0.000004482776,0.00007216607,0.0000014818914],"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":[0.000015287955,0.000052118838,0.0050061997,0.00005158345,0.0000073710867,0.0000032834748,0.00021833027,0.5041333,0.23829982,0.011292813,0.00015646129,0.24076347],"study_design_scores_gemma":[0.00021019188,0.00003192151,0.0073112515,0.000010628024,0.000002115497,0.0000036404497,0.0000027893375,0.94544,0.045699917,0.00053644617,0.00065541157,0.00009571095],"about_ca_topic_score_codex":0.000041910887,"about_ca_topic_score_gemma":0.000020854197,"teacher_disagreement_score":0.4413067,"about_ca_system_score_codex":0.000020883523,"about_ca_system_score_gemma":0.0000037230704,"threshold_uncertainty_score":0.28142345},"labels":[],"label_agreement":null},{"id":"W2150555733","doi":"10.1109/pes.2009.5275710","title":"The impact of tower shadow, yaw error, and wind shears on power quality in a wind-diesel system","year":2009,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Simon Fraser University; Western University","funders":"","keywords":"Turbine; Wind power; Aerodynamics; Diesel generator; Marine engineering; Automotive engineering; Wind speed; Diesel fuel; Engineering; Electrical engineering; Aerospace engineering; Meteorology; Physics","score_opus":0.016085751709859455,"score_gpt":0.27189176031460077,"score_spread":0.25580600860474134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2150555733","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":[0.89636785,0.00029109025,0.00003196895,0.000047400714,0.000107391665,0.00007932472,0.0000050530803,0.00008550805,0.10298441],"genre_scores_gemma":[0.99965614,0.000013565176,0.000049960006,0.00002212059,0.00001915078,7.832642e-7,0.0000011868037,0.000012515345,0.00022456227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991354,0.00003500958,0.00029995525,0.00012928376,0.00013330419,0.0002670597],"domain_scores_gemma":[0.9995325,0.0001319503,0.000033515567,0.0002191797,0.000019665564,0.00006317059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038123957,0.00015258067,0.0002152554,0.000063340914,0.000046833098,0.000030080912,0.00009942307,0.000076904835,0.000033266973],"category_scores_gemma":[0.000032488693,0.000095756535,0.000073840885,0.00015163871,0.000028377623,0.00008074193,0.000014154119,0.0001564014,0.0000034073873],"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":[0.00090520387,0.00052747625,0.11634218,0.00050991453,0.0007070185,0.00011174029,0.021096021,0.70508,0.020948503,0.085068375,0.009742757,0.038960833],"study_design_scores_gemma":[0.0021121749,0.0010846059,0.92653054,0.0009356762,0.000019445555,0.000043915512,0.0028891067,0.061013732,0.0023642464,0.0006395415,0.0014085701,0.00095844147],"about_ca_topic_score_codex":0.00037712578,"about_ca_topic_score_gemma":0.00008805806,"teacher_disagreement_score":0.81018835,"about_ca_system_score_codex":0.00007659148,"about_ca_system_score_gemma":0.000012603912,"threshold_uncertainty_score":0.39048383},"labels":[],"label_agreement":null},{"id":"W2155890926","doi":"","title":"SAGIPE: A real-time data acquisition system for the massive integration of wind generation in Hydro-Québec's power system","year":2009,"lang":"en","type":"article","venue":"2009 CIGRE/IEEE PES Joint Symposium Integration of Wide-Scale Renewable Resources Into the Power Delivery System","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Nanoacademic Technologies; Hydro-Québec; SNC-Lavalin (Canada)","funders":"","keywords":"Wind power; Electricity generation; Procurement; Electric power system; Meteorology; Renewable energy; Engineering; Power (physics); Environmental science; Telecommunications; Electrical engineering; Business; Geography","score_opus":0.013990169899568985,"score_gpt":0.21356178753074428,"score_spread":0.1995716176311753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155890926","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":[0.8781,0.004378898,0.09255248,0.0009904018,0.0024549155,0.003320286,0.00053941365,0.0008388884,0.016824724],"genre_scores_gemma":[0.9972453,0.00019139025,0.0011838168,0.00003222889,0.0004197698,0.000103272025,0.00033166842,0.00009312132,0.00039944274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99506676,0.00048916804,0.002274546,0.0007474254,0.0008285178,0.0005935597],"domain_scores_gemma":[0.9958725,0.0006325831,0.0010424769,0.0017089054,0.000606452,0.00013706101],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0021844364,0.00068861013,0.001066677,0.0005444125,0.0004055939,0.00020831985,0.0011178199,0.00040603668,0.000022788503],"category_scores_gemma":[0.00008312598,0.0004775097,0.00036709858,0.0006631002,0.00016285031,0.00078694883,0.00009678891,0.00034786089,0.00002019655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023244435,0.000069643516,0.0000622883,0.00065604405,0.00019821532,0.0000070318565,0.008735485,0.49861333,0.48561844,0.0004368111,0.004052349,0.0013179217],"study_design_scores_gemma":[0.0007633042,0.0004238014,0.00017810574,0.0036425674,0.0002580299,0.0000762288,0.012972314,0.8248099,0.15502238,0.00001115777,0.0013049394,0.00053723814],"about_ca_topic_score_codex":0.009675708,"about_ca_topic_score_gemma":0.003430932,"teacher_disagreement_score":0.33059603,"about_ca_system_score_codex":0.0012970164,"about_ca_system_score_gemma":0.00014021229,"threshold_uncertainty_score":0.99976766},"labels":[],"label_agreement":null},{"id":"W2155981096","doi":"10.1109/ccece.1997.614843","title":"A hybrid intelligent system architecture for utility demand forecasting","year":2002,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Intelligent decision support system; Expert system; Artificial neural network; Knowledge base; Artificial intelligence; Knowledge-based systems; Legal expert system; Fuzzy logic; Architecture; Hybrid system; Machine learning","score_opus":0.0321312844982951,"score_gpt":0.2041976463544125,"score_spread":0.1720663618561174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155981096","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":[0.12572876,0.0010782474,0.78082234,0.000027635608,0.00071547483,0.00029853312,0.000024858233,0.0010645584,0.09023963],"genre_scores_gemma":[0.992513,0.000009830779,0.006777133,0.000021216227,0.00020233354,0.000036373265,0.0000072143353,0.000035253142,0.00039763222],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991613,0.00001066482,0.0002411219,0.00016916577,0.00008597912,0.0003317902],"domain_scores_gemma":[0.9995757,0.0001232533,0.000021827642,0.00016504177,0.000024831128,0.00008935307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014078878,0.00016396334,0.00017032622,0.000044052376,0.00008956532,0.000035223573,0.00010908679,0.000044576936,0.00012163202],"category_scores_gemma":[0.000033673576,0.00014120425,0.00009984255,0.00006874696,0.000014582587,0.000048581554,0.000020624262,0.00011223112,0.000017705832],"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":[0.000026132006,0.000053906064,0.00053625344,0.0025174175,0.00022304905,0.00004231714,0.001487309,0.3436623,0.0009079947,0.0052787582,0.01618624,0.6290783],"study_design_scores_gemma":[0.00014559673,0.000025509387,0.0000075016897,0.000099773606,0.0000129980135,0.00008100102,0.000058676822,0.96030974,0.010805868,0.00014396154,0.028132612,0.0001767449],"about_ca_topic_score_codex":0.0000061473484,"about_ca_topic_score_gemma":0.000016765409,"teacher_disagreement_score":0.8667843,"about_ca_system_score_codex":0.000038881382,"about_ca_system_score_gemma":0.0000019977347,"threshold_uncertainty_score":0.5758142},"labels":[],"label_agreement":null},{"id":"W2156200529","doi":"10.1109/ccece.1999.804886","title":"Neural network based power flow predictor","year":2003,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Alberta","funders":"","keywords":"Computer science; Artificial neural network; Power flow; Flow (mathematics); Power (physics); Artificial intelligence; Electric power system; Physics; Mechanics","score_opus":0.0073929868807352,"score_gpt":0.176997199037132,"score_spread":0.16960421215639682,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156200529","genre_codex":"other","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":[0.1904043,0.00059623685,0.05121845,0.000038808397,0.0037662634,0.00009654307,0.0000055824753,0.0014820257,0.7523918],"genre_scores_gemma":[0.99042404,0.0000018482158,0.008595066,0.00018417195,0.00012647115,0.0000044011454,0.000004917125,0.000027018661,0.0006320518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994602,0.000013418308,0.00010576253,0.0000847624,0.00007428928,0.0002615395],"domain_scores_gemma":[0.99975014,0.000036560406,0.0000064015317,0.00012471766,0.000009596663,0.00007257522],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000067408815,0.00010138959,0.000080098296,0.000019607152,0.00003597604,0.000019700941,0.00005486158,0.00004721546,0.0012719793],"category_scores_gemma":[0.000014740058,0.00008980262,0.000042043466,0.00011592395,0.000008456494,0.000055323908,0.000004265172,0.00008735962,0.00003630149],"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":[0.0000012314097,0.0000039648885,0.0017509423,0.000005805771,0.00000874384,0.000004516097,0.000018673747,0.9835085,0.00007928177,0.0014880574,0.012618103,0.0005121731],"study_design_scores_gemma":[0.00022962171,0.00002285517,0.0005050802,0.000014762293,0.0000049402643,0.0000045939473,0.000005177661,0.8901699,0.0010875963,0.00009861976,0.10768445,0.0001723886],"about_ca_topic_score_codex":0.0000014243224,"about_ca_topic_score_gemma":0.000006639061,"teacher_disagreement_score":0.80001974,"about_ca_system_score_codex":0.000011800155,"about_ca_system_score_gemma":0.0000068262434,"threshold_uncertainty_score":0.999641},"labels":[],"label_agreement":null},{"id":"W2159040640","doi":"10.1109/tpwrs.2009.2030380","title":"Economic Impact of Electricity Market Price Forecasting Errors: A Demand-Side Analysis","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":113,"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; University of Calgary","funders":"","keywords":"Electricity market; Electricity; Electricity price; Economics; Economic forecasting; Electricity price forecasting; Econometrics; Electricity generation; Engineering; Power (physics)","score_opus":0.011092739019856595,"score_gpt":0.22513237465419295,"score_spread":0.21403963563433634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159040640","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":[0.39464474,0.00022653781,0.58923906,0.0000040540667,0.00062022125,0.00015643991,0.00006559021,0.00023390238,0.014809487],"genre_scores_gemma":[0.99949217,0.000027976139,0.00015277315,0.0000071267887,0.000024541368,0.00001448096,0.0000034606728,0.000032273234,0.00024520291],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852276,0.00006322564,0.0005817358,0.00025348293,0.00016015246,0.00041863674],"domain_scores_gemma":[0.9991879,0.00014409801,0.00013012828,0.00033246176,0.00004089948,0.00016450627],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031339464,0.0002852633,0.0005278328,0.0006277649,0.00009691928,0.000049052265,0.00017343265,0.00013842905,0.0001736582],"category_scores_gemma":[0.000005892631,0.00027015057,0.0005333526,0.0007908292,0.000016565107,0.00020721473,4.9809483e-7,0.00024032037,0.000012574755],"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":[0.000034383003,0.00003464769,0.00016850908,0.000029748022,0.00090256875,0.0000051002316,0.00025022932,0.9965337,0.0011713665,0.000013120811,0.00024277133,0.0006138409],"study_design_scores_gemma":[0.00036252727,0.00032680325,0.0016096605,0.000115582174,0.00034642618,0.000049414746,0.00004636484,0.99120337,0.0053931447,0.000011093397,0.00016099407,0.00037463146],"about_ca_topic_score_codex":0.00035985623,"about_ca_topic_score_gemma":0.00009942777,"teacher_disagreement_score":0.60484743,"about_ca_system_score_codex":0.00042303168,"about_ca_system_score_gemma":0.000060872717,"threshold_uncertainty_score":0.9999751},"labels":[],"label_agreement":null},{"id":"W2159833660","doi":"10.1109/tpwrs.2010.2052116","title":"Classification of Future Electricity Market Prices","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Electricity market; Electricity; Electricity price forecasting; Economics; Context (archaeology); Computer science; Scheduling (production processes); Econometrics; Term (time); Market price; Operations research; Microeconomics; Engineering; Operations management","score_opus":0.007764022047784758,"score_gpt":0.20196486271535166,"score_spread":0.1942008406675669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159833660","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":[0.3874176,0.00022229794,0.5656009,0.000027163895,0.011574709,0.00020873541,0.000045666722,0.0004213827,0.034481585],"genre_scores_gemma":[0.9992288,0.000032776898,0.0001694766,0.000004821358,0.000067436755,0.000028692313,0.0000016603663,0.000028768762,0.00043759728],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992206,0.000020530562,0.00027177017,0.00013853253,0.00017009626,0.00017844682],"domain_scores_gemma":[0.9995084,0.00006014892,0.00005717659,0.00026038723,0.000052038424,0.00006187051],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015494828,0.00013929143,0.00016510021,0.00015557765,0.000066308356,0.000025971343,0.00012900887,0.00016059629,0.00013153983],"category_scores_gemma":[0.000001885612,0.00013094555,0.00007624724,0.00029449177,0.000020405349,0.00011772282,2.2845921e-7,0.0003519438,0.000017063028],"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":[0.00011490607,0.0004361019,0.0005679975,0.00079737435,0.0004685189,0.00000813552,0.0020516873,0.16025785,0.8065298,0.0042672222,0.009174691,0.01532569],"study_design_scores_gemma":[0.0013392812,0.0003785763,0.009235962,0.0003441181,0.00015695298,0.00014904149,0.000866885,0.4921536,0.28958493,0.000038771937,0.20431118,0.0014406914],"about_ca_topic_score_codex":0.000025188772,"about_ca_topic_score_gemma":0.000058267957,"teacher_disagreement_score":0.61181116,"about_ca_system_score_codex":0.000024874862,"about_ca_system_score_gemma":0.000014778024,"threshold_uncertainty_score":0.5339805},"labels":[],"label_agreement":null},{"id":"W2159893886","doi":"10.1109/pes.2006.1709474","title":"Forecasting the hourly Ontario energy price by multivariate adaptive regression splines","year":2006,"lang":"en","type":"article","venue":"2006 IEEE Power Engineering Society General Meeting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":55,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multivariate adaptive regression splines; Electricity price forecasting; Mars Exploration Program; Econometrics; Multivariate statistics; Probabilistic forecasting; Computer science; Variable (mathematics); Regression analysis; Cross-sectional regression; Regression; Variable renewable energy; Demand forecasting; Electricity; Electricity market; Nonparametric regression; Bayesian multivariate linear regression; Economics; Statistics; Engineering; Operations research; Machine learning; Artificial intelligence; Mathematics; Electric power system","score_opus":0.00925709688761082,"score_gpt":0.18511990225364908,"score_spread":0.17586280536603827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2159893886","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":[0.8311314,0.0039134677,0.14694268,0.000098409335,0.0040194634,0.0002836763,0.00007448911,0.001692093,0.011844306],"genre_scores_gemma":[0.96649754,0.000038438844,0.028777337,0.000102245445,0.0013675614,0.00006260587,0.00006901026,0.00021912956,0.0028661448],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974104,0.00004153891,0.00063071726,0.0004690934,0.00041499003,0.001033225],"domain_scores_gemma":[0.9990131,0.00024994733,0.00015474443,0.00035334306,0.00009531679,0.00013353104],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004834226,0.0006418144,0.00040171284,0.000060750364,0.00038961877,0.00015226401,0.00038481608,0.00027835794,0.000026349613],"category_scores_gemma":[0.000032295804,0.00052135426,0.00036115872,0.00041988768,0.00004822667,0.0002703728,0.00007839026,0.0006227277,0.000005363412],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006419542,0.000023584129,0.0003912822,0.00003199097,0.00010825729,0.000007902503,0.00086645194,0.88874567,0.079332106,0.00023869872,0.029682321,0.00056529767],"study_design_scores_gemma":[0.00046711377,0.00003646444,0.00024337591,0.00033935366,0.00004258176,0.000030738818,0.0000862048,0.9183466,0.040015962,0.00004635267,0.0395226,0.00082264224],"about_ca_topic_score_codex":0.012953653,"about_ca_topic_score_gemma":0.0014719589,"teacher_disagreement_score":0.1353661,"about_ca_system_score_codex":0.0003881484,"about_ca_system_score_gemma":0.00004240615,"threshold_uncertainty_score":0.9997238},"labels":[],"label_agreement":null},{"id":"W2160123863","doi":"10.1109/icset.2008.4747199","title":"Forecasting spot electricity market prices using time series models","year":2008,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":19,"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":"Electricity market; Electricity; Volatility (finance); Spot contract; Econometrics; Electricity price forecasting; Spot market; Time series; Series (stratigraphy); Economics; Financial economics; Computer science; Engineering","score_opus":0.0299094107181056,"score_gpt":0.19531008773974307,"score_spread":0.16540067702163747,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160123863","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":[0.7152424,0.000257568,0.028967226,0.000005666996,0.00013968917,0.00006170896,0.000003234734,0.0005993426,0.2547232],"genre_scores_gemma":[0.95942813,0.000064563545,0.037101556,0.000024579402,0.00017477613,0.0000036121962,0.000004637253,0.00005274858,0.003145406],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990551,0.000012060235,0.00022265404,0.00016029169,0.00014686224,0.0004030562],"domain_scores_gemma":[0.9996654,0.000059425984,0.000031653504,0.00013350275,0.000032729433,0.0000773361],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011628353,0.00018945984,0.00018972864,0.00009947302,0.00019051638,0.00002846773,0.000118554235,0.000077012686,0.000312708],"category_scores_gemma":[0.000019401106,0.0001811912,0.000059782666,0.00030120532,0.000028571158,0.00058023375,0.000033098728,0.00013583615,0.000012968533],"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":[0.000023217628,0.000019369112,0.0008002004,0.000076135206,0.00007241514,0.00008707639,0.00051665085,0.9840339,0.007796882,0.00060706027,0.0029801293,0.0029869548],"study_design_scores_gemma":[0.00009612392,0.000017390497,0.000040741183,0.000027999979,0.000008302291,0.00031607016,0.00001073824,0.98747516,0.010293559,0.00030615364,0.0011651784,0.0002425595],"about_ca_topic_score_codex":0.000041136023,"about_ca_topic_score_gemma":0.000008767872,"teacher_disagreement_score":0.2515778,"about_ca_system_score_codex":0.00005261969,"about_ca_system_score_gemma":0.000020573047,"threshold_uncertainty_score":0.7388762},"labels":[],"label_agreement":null},{"id":"W2160271847","doi":"10.1109/ijcnn.2009.5178649","title":"Climatic variation of the structure of maximum daily temperatures in Spain: A combined statistical and computational intelligence approach.","year":2009,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"National Research Council Canada","funders":"","keywords":"Peninsula; Mediterranean climate; Genetic programming; Distribution (mathematics); Computer science; Variation (astronomy); Empirical orthogonal functions; Climate change; Climatology; Function (biology); Physical geography; Geography; Geology; Mathematics; Artificial intelligence","score_opus":0.008071113899183436,"score_gpt":0.20708274883080405,"score_spread":0.1990116349316206,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2160271847","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":[0.7080346,0.000070422844,0.2900847,0.00005863949,0.00007699381,0.00012741493,0.000030507903,0.000029077575,0.0014877005],"genre_scores_gemma":[0.9692825,0.0000025703168,0.030659255,0.000027913657,0.000006437913,3.1913612e-7,0.0000140957445,0.0000038126552,0.0000031161012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99955165,0.000022604318,0.00020371181,0.00006208209,0.0000897715,0.00007014781],"domain_scores_gemma":[0.9997841,0.00009376343,0.000028259205,0.00006021693,0.000018568328,0.000015055279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006127879,0.00006371249,0.00010989234,0.000038331593,0.00001363574,0.000008154145,0.00005721114,0.00003412433,0.000014737242],"category_scores_gemma":[0.000030863102,0.00004464051,0.0000116087285,0.00012932606,0.000028552127,0.00003622684,0.0000097554475,0.00008631951,7.180978e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001310651,0.00003236744,0.0024600038,0.00014891269,0.000013157291,4.818787e-7,0.0011888405,0.82719785,0.0032619135,0.16288361,0.00001736619,0.0027824016],"study_design_scores_gemma":[0.00016596868,0.00004201511,0.11760382,0.000047844263,0.000006029078,0.0000049092214,0.00007970549,0.8350245,0.0009965553,0.045959156,0.0000011976541,0.000068309426],"about_ca_topic_score_codex":0.000012490351,"about_ca_topic_score_gemma":0.000008710149,"teacher_disagreement_score":0.26124793,"about_ca_system_score_codex":0.000008093206,"about_ca_system_score_gemma":0.00000826016,"threshold_uncertainty_score":0.18203871},"labels":[],"label_agreement":null},{"id":"W2163173612","doi":"10.1109/tcst.2011.2134098","title":"Online Parameter Optimization-Based Prediction for Converter Gas System by Parallel Strategies","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Control Systems Technology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":47,"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":"Baosteel Group Corporation","keywords":"Particle swarm optimization; Computer science; Scheduling (production processes); Mathematical optimization; Algorithm","score_opus":0.012161845182146882,"score_gpt":0.1929484667040544,"score_spread":0.18078662152190753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163173612","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":[0.003269706,0.00022706704,0.990806,0.00004207378,0.0018059448,0.0007156381,0.0005935303,0.0021395877,0.00040044586],"genre_scores_gemma":[0.99493426,0.000013587646,0.003916495,0.00002322837,0.000052444964,0.00089674105,0.000035138557,0.00006709494,0.00006100653],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987426,0.000033439534,0.00047826924,0.00027799307,0.00010938866,0.00035835837],"domain_scores_gemma":[0.99931127,0.00013067304,0.00008208174,0.00031398257,0.000098471515,0.00006353816],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009831351,0.00027077,0.00037714068,0.00033067277,0.00013417023,0.0000389872,0.00017847265,0.00042693812,0.000017278202],"category_scores_gemma":[0.0000040402165,0.00026289086,0.000113286886,0.00017647447,0.00006844518,0.0001481205,3.6907682e-7,0.00024053667,0.000008828227],"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":[0.00006769497,0.00009215905,0.000016103473,0.00015143554,0.00018275958,0.0000024098115,0.000043820877,0.9965868,0.00085707795,0.00049163855,0.00018330492,0.0013248082],"study_design_scores_gemma":[0.0019158996,0.00028832039,0.0000014461339,0.00014526055,0.00009643653,0.000020208152,0.00038363403,0.9939018,0.0024283372,0.000018353147,0.0005753219,0.00022497901],"about_ca_topic_score_codex":0.000058567864,"about_ca_topic_score_gemma":0.00004182172,"teacher_disagreement_score":0.9916645,"about_ca_system_score_codex":0.00011125618,"about_ca_system_score_gemma":0.000031448435,"threshold_uncertainty_score":0.99998236},"labels":[],"label_agreement":null},{"id":"W2165220437","doi":"10.1007/s11269-012-0122-1","title":"Erratum to: Forecasting Urban Water Demand Via Wavelet-Denoising and Neural Network Models. Case Study: City of Syracuse, Italy","year":2012,"lang":"en","type":"erratum","venue":"Water Resources Management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial neural network; Wavelet; Computer science; Filter (signal processing); Generalization; Daubechies wavelet; Noise (video); Haar wavelet; Wavelet transform; Artificial intelligence; Machine learning; Econometrics; Discrete wavelet transform; Mathematics","score_opus":0.02236027769712346,"score_gpt":0.2068371371714715,"score_spread":0.18447685947434805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165220437","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":[0.95148337,0.002392102,0.0030256265,0.000038030677,0.009477571,0.0012913827,0.00001872222,0.00042741562,0.031845763],"genre_scores_gemma":[0.9772135,0.000037473408,0.0007197799,0.000058514805,0.0020709962,0.000081901504,0.00013268318,0.00026262458,0.019422542],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99616784,0.000114528055,0.0009457305,0.00071486045,0.0005297851,0.0015272609],"domain_scores_gemma":[0.9987625,0.000029905666,0.00012916469,0.00069295656,0.0000598783,0.0003256235],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007746831,0.00088574295,0.00092493324,0.00041819006,0.00036491675,0.00026740954,0.000430914,0.00032445492,0.00003462397],"category_scores_gemma":[0.0000036030751,0.00063887413,0.00017223193,0.00021161961,0.000063681335,0.0003252896,0.0011301477,0.00081777887,0.000007979355],"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":[0.00010160512,0.00022538149,0.0015216209,0.0048024715,0.001956229,0.0066789663,0.065816306,0.75443536,0.00013472863,0.000017352477,0.14600247,0.018307485],"study_design_scores_gemma":[0.0023556368,0.0008746343,0.00029559646,0.0028645932,0.0028688204,0.0026930494,0.0044240854,0.5916028,0.0008044794,0.0007011183,0.38538128,0.0051339013],"about_ca_topic_score_codex":0.00036381526,"about_ca_topic_score_gemma":0.00026882312,"teacher_disagreement_score":0.2393788,"about_ca_system_score_codex":0.0001040629,"about_ca_system_score_gemma":0.000002648966,"threshold_uncertainty_score":0.99960625},"labels":[],"label_agreement":null},{"id":"W2166693625","doi":"10.1175/jamc-d-12-0339.1","title":"Wind Conditions in a Fjordlike Bay and Predictions of Wind Speed Using Neighboring Stations Employing Neural Network Models","year":2013,"lang":"en","type":"article","venue":"Journal of Applied Meteorology and Climatology","topic":"Energy Load and Power Forecasting","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":"Fisheries and Oceans Canada","funders":"","keywords":"Wind speed; Artificial neural network; Environmental science; Meteorology; Wind direction; Bay; Prevailing winds; Range (aeronautics); Computer science; Geology; Geography; Oceanography; Engineering; Machine learning","score_opus":0.017632508772412382,"score_gpt":0.23541445383305792,"score_spread":0.21778194506064555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166693625","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":[0.9955858,0.00059510267,0.0021767984,0.000047276444,0.00034759138,0.00009796652,0.0000064993756,0.000017352944,0.0011256112],"genre_scores_gemma":[0.9962275,0.00019045213,0.003430608,0.000049318674,0.00007773671,0.0000016440499,0.0000035340502,0.000017516482,0.0000017156397],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99889934,0.000041803767,0.0005894874,0.00011033381,0.000062292325,0.00029672994],"domain_scores_gemma":[0.9993005,0.00029270086,0.00019225167,0.00007356779,0.000052705487,0.00008829587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020889631,0.00013491698,0.00042634684,0.00024188517,0.00010329889,0.000013170498,0.00006572507,0.00016262602,0.000029628725],"category_scores_gemma":[0.000014005311,0.00013159159,0.000039734598,0.00017178555,0.00015136316,0.00024429357,0.00003201591,0.00036233518,3.9964823e-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":[0.000031139574,0.000018507691,0.022638531,0.000036209687,0.000095008414,0.000009854579,0.00064894365,0.9636136,0.00505018,0.007580937,0.000022689706,0.0002543778],"study_design_scores_gemma":[0.001996049,0.00020457964,0.028967347,0.00010934957,0.00022350681,0.0012818478,0.0012087784,0.9297282,0.00028321097,0.035658445,0.00006774352,0.0002709319],"about_ca_topic_score_codex":0.000013885515,"about_ca_topic_score_gemma":0.000030511263,"teacher_disagreement_score":0.033885412,"about_ca_system_score_codex":0.000016377615,"about_ca_system_score_gemma":0.000021599048,"threshold_uncertainty_score":0.5366149},"labels":[],"label_agreement":null},{"id":"W2166799085","doi":"10.1109/pes.2007.385613","title":"On Efficient Tuning of LS-SVM Hyper-Parameters in Short-Term Load Forecasting: A Comparative Study","year":2007,"lang":"en","type":"article","venue":"IEEE Power Engineering Society General Meeting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Independent Electricity System Operator","keywords":"Support vector machine; Simulated annealing; Computer science; Bayesian probability; Scheduling (production processes); Bayesian optimization; Genetic algorithm; Data mining; Cross-validation; Naive Bayes classifier; Machine learning; Term (time); Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.02876676293304292,"score_gpt":0.25122582648632646,"score_spread":0.22245906355328354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166799085","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":[0.9862714,0.0003295929,0.008264076,0.0000021422684,0.0014634769,0.00034902676,0.000004999879,0.0003374998,0.002977776],"genre_scores_gemma":[0.98919976,0.0000054497323,0.01048854,0.00002016824,0.00013657089,0.000026950203,0.0000043486184,0.00009884056,0.000019370384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99733275,0.000035460118,0.0008268165,0.00043206103,0.0005232667,0.0008496717],"domain_scores_gemma":[0.99893636,0.00042638494,0.00006892257,0.00030768654,0.00009202315,0.00016860536],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012800517,0.00049849326,0.00059862016,0.00020501266,0.0000859698,0.000043324857,0.00028210555,0.00016708627,0.000003780872],"category_scores_gemma":[0.00007986432,0.00053598545,0.00026901616,0.00065809314,0.000042111824,0.000089679386,0.000056915404,0.0006018566,0.0000028544123],"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":[0.00001781557,0.00019591946,0.0031124724,0.00008270203,0.00010659363,0.00001778227,0.009818961,0.92560846,0.060304478,0.000057571007,0.000044075492,0.0006332036],"study_design_scores_gemma":[0.000912461,0.00026188756,0.0020965184,0.0008151558,0.000035654877,0.000016548947,0.0021198175,0.92094237,0.07196928,0.000004227722,0.000068280824,0.0007577788],"about_ca_topic_score_codex":0.0000572846,"about_ca_topic_score_gemma":0.000034790635,"teacher_disagreement_score":0.011664804,"about_ca_system_score_codex":0.00039800064,"about_ca_system_score_gemma":0.000041088868,"threshold_uncertainty_score":0.9997092},"labels":[],"label_agreement":null},{"id":"W2168326514","doi":"10.1109/ptc.2005.4524367","title":"Energy price forecasting and bidding strategy in the Ontario power system market","year":2005,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Toronto; Kinectrics (Canada)","funders":"","keywords":"Bidding; Electricity market; Fuzzy logic; Computer science; Artificial neural network; Electric power system; Electricity; Electricity price forecasting; Risk aversion (psychology); Generator (circuit theory); Wind power; Energy (signal processing); Econometrics; Operations research; Economics; Microeconomics; Power (physics); Artificial intelligence; Engineering; Financial economics; Expected utility hypothesis; Mathematics; Statistics; Electrical engineering","score_opus":0.013554785969045283,"score_gpt":0.18178490631084482,"score_spread":0.16823012034179954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2168326514","genre_codex":"other","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":[0.38384047,0.00026359022,0.0014861146,0.000018286371,0.00009181039,0.000037401456,7.254477e-7,0.000126129,0.61413544],"genre_scores_gemma":[0.99746555,0.000008311684,0.0007758377,0.00005644345,0.00009405125,0.000009808738,0.0000016900487,0.000020066047,0.0015682481],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992252,0.000025392877,0.00021596836,0.00013447826,0.000108046755,0.0002909427],"domain_scores_gemma":[0.9996867,0.00011742645,0.000021604961,0.000121401594,0.000009933324,0.000042904427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002942324,0.00014296279,0.0001229028,0.00007758545,0.00006236946,0.000081776154,0.00011571388,0.00006191129,0.00017391074],"category_scores_gemma":[0.000005599027,0.00010394462,0.000025601552,0.00014815363,0.000011154085,0.00017331158,0.000023460805,0.0001528978,0.0000025710974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006203933,0.00010696758,0.02748992,0.0005527243,0.0002254677,0.00032069307,0.02250968,0.5834197,0.0012925648,0.10576239,0.01309024,0.24516763],"study_design_scores_gemma":[0.00047940333,0.00004839296,0.005143192,0.0002691835,0.000012715203,0.00032386126,0.0024931682,0.9068989,0.00046604255,0.000054382268,0.08335691,0.00045383556],"about_ca_topic_score_codex":0.0025975287,"about_ca_topic_score_gemma":0.04354013,"teacher_disagreement_score":0.61362505,"about_ca_system_score_codex":0.00012820875,"about_ca_system_score_gemma":0.000013224301,"threshold_uncertainty_score":0.9739128},"labels":[],"label_agreement":null},{"id":"W2169682984","doi":"10.1109/naps.2008.5307388","title":"Application of linear lazy learning approach to short-term load forecasting","year":2008,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Waterloo","funders":"","keywords":"Term (time); Computer science; Electric power system; Artificial intelligence; Electrical load; Machine learning; Power (physics)","score_opus":0.029895516290697113,"score_gpt":0.2216093595884618,"score_spread":0.1917138432977647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169682984","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":[0.5215895,0.000058596117,0.32691512,0.0000027979431,0.00005816798,0.00009439995,9.551072e-7,0.00030123617,0.1509792],"genre_scores_gemma":[0.9748077,0.000010783426,0.024524074,0.000010299601,0.00013415319,0.000022345497,0.000014536809,0.00003349245,0.0004426645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992249,0.000008462108,0.00023669732,0.0001543714,0.00016000471,0.0002155373],"domain_scores_gemma":[0.999675,0.000031780746,0.000021045824,0.00014239973,0.000051774965,0.00007804059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000116537434,0.00012353758,0.0001578167,0.00006778433,0.0000858376,0.000005565224,0.00011321313,0.00006279026,0.000009924207],"category_scores_gemma":[0.000028956645,0.000121494224,0.000048245452,0.00023916252,0.00001739163,0.00007495407,0.000033446195,0.00014518086,0.000016020487],"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":[0.0000048779157,0.000023987322,0.012765507,0.00008254231,0.000020696783,0.000001860867,0.001244416,0.94614106,0.013500581,0.00037374158,0.00013150115,0.025709251],"study_design_scores_gemma":[0.000113026144,0.000035019104,0.0011548885,0.000028418308,0.000007163158,0.000043027812,0.00006665992,0.9745894,0.019257694,0.000007175862,0.004479004,0.00021848733],"about_ca_topic_score_codex":0.00003424092,"about_ca_topic_score_gemma":0.000006502896,"teacher_disagreement_score":0.45321813,"about_ca_system_score_codex":0.000034982342,"about_ca_system_score_gemma":0.000011194004,"threshold_uncertainty_score":0.49543905},"labels":[],"label_agreement":null},{"id":"W2185482623","doi":"10.1109/pedes.2006.344294","title":"Electricity Price Forecasting Using Artificial Neural Network","year":2006,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Electricity; Electricity market; Artificial neural network; Backpropagation; Levenberg–Marquardt algorithm; Computer science; Artificial intelligence; Operations research; Engineering; Electrical engineering","score_opus":0.025675472070367446,"score_gpt":0.20781203342574073,"score_spread":0.1821365613553733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2185482623","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":[0.85217303,0.00023068464,0.07329508,0.0000072569696,0.00062209234,0.000059308066,9.807951e-7,0.00060199824,0.07300958],"genre_scores_gemma":[0.9896197,0.0000012900948,0.008638681,0.000029855515,0.0015561901,0.0000022057789,0.0000066424186,0.000037126665,0.000108317545],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989505,0.000013553626,0.0002574161,0.00013955838,0.00011383486,0.0005251817],"domain_scores_gemma":[0.9997377,0.00006266591,0.000030532592,0.000100029894,0.000022425855,0.000046651243],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120561715,0.00015467058,0.00013732287,0.000051568713,0.00014766525,0.000058631664,0.00008435548,0.0000683719,0.000058536556],"category_scores_gemma":[0.000011765271,0.00015325159,0.000057709924,0.00042090355,0.000012759815,0.00013395738,0.000020506917,0.00015925233,0.000007465023],"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":[0.0000025998056,0.0000061727956,0.0010289205,0.000009867911,0.000006771093,0.000008399773,0.000008719438,0.98613024,0.0035493553,0.0043620467,0.00052595243,0.0043609794],"study_design_scores_gemma":[0.000056630724,0.000010911132,0.00026334662,0.000013678948,0.000008565839,0.000030507794,0.0000037899774,0.991754,0.0050851665,0.0014849971,0.0010907836,0.00019762291],"about_ca_topic_score_codex":0.00016076457,"about_ca_topic_score_gemma":0.00011913009,"teacher_disagreement_score":0.13744667,"about_ca_system_score_codex":0.000051199462,"about_ca_system_score_gemma":0.0000088159495,"threshold_uncertainty_score":0.6249418},"labels":[],"label_agreement":null},{"id":"W2186560469","doi":"10.1007/978-3-319-07455-9_20","title":"Developing Data-driven Models to Predict BEMS Energy Consumption for Demand Response Systems","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Energy consumption; Building management system; Smart grid; Key (lock); Energy modeling; Chiller; Data modeling; Demand response; Building automation; Energy (signal processing); Consumption (sociology); Energy management; Database; Artificial intelligence; Engineering; Electricity; Computer security; Control (management)","score_opus":0.052694841814471975,"score_gpt":0.2520939832018173,"score_spread":0.19939914138734532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2186560469","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":[0.00035564718,0.0006171707,0.995672,0.00004860342,0.0024008239,0.0002693286,0.00006871743,0.00021314363,0.00035456734],"genre_scores_gemma":[0.7011275,0.000098663244,0.2962201,0.00045850663,0.0014206817,0.000055268745,0.00016317137,0.00014516962,0.00031090435],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979219,0.000027336264,0.0004273835,0.0007783392,0.0003700737,0.00047497847],"domain_scores_gemma":[0.9981423,0.00073381764,0.00008923253,0.00079145463,0.00010569175,0.00013749178],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00091839506,0.00037658075,0.00041965363,0.00048427322,0.00014979322,0.00021822928,0.0011640135,0.0002662242,0.0000024557553],"category_scores_gemma":[0.00006501163,0.00036714182,0.000045151442,0.0001426603,0.00012119706,0.00023201841,0.00040670278,0.00023157659,0.0000052722935],"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":[0.000023076389,0.0000011190128,0.0000050069675,0.00011375664,0.000014638982,0.0000048517886,0.00016730356,0.9552874,0.00012504717,0.006835113,0.00006056211,0.037362102],"study_design_scores_gemma":[0.00015314299,0.000060524202,0.0000052170603,0.0011560699,0.000011300916,0.000026376245,9.033581e-8,0.9817752,0.00030118987,0.0037502437,0.012320785,0.00043984485],"about_ca_topic_score_codex":0.000016082622,"about_ca_topic_score_gemma":0.00008488701,"teacher_disagreement_score":0.70077187,"about_ca_system_score_codex":0.00025417996,"about_ca_system_score_gemma":0.00015363189,"threshold_uncertainty_score":0.99987805},"labels":[],"label_agreement":null},{"id":"W2188605683","doi":"","title":"Computing Electricity Consumption Profiles from Household Smart Meter Data","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":44,"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":"Smart meter; Autoregressive model; Computer science; Electricity; Scalability; Consumption (sociology); Outlier; Metric (unit); Smart grid; Data mining; Electricity meter; Data set; Power consumption; Automatic meter reading; Power (physics); Econometrics; Artificial intelligence; Engineering; Database; Mathematics; Telecommunications","score_opus":0.04880249084444879,"score_gpt":0.22883131534952933,"score_spread":0.18002882450508054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2188605683","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":[0.83549666,0.00013072048,0.15342517,0.000010746387,0.00036728865,0.000046706577,0.00003400205,0.00076070393,0.00972802],"genre_scores_gemma":[0.9882346,0.000014164554,0.011147573,0.00007750503,0.00023182832,0.0000010715358,0.0002102918,0.00002602263,0.000056984132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993205,0.000025504412,0.00016192395,0.00019310926,0.00009245942,0.00020647],"domain_scores_gemma":[0.999391,0.00015774804,0.00002127562,0.00037356323,0.000008702875,0.00004767846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018022476,0.000113890026,0.00012662147,0.00004031575,0.00005320731,0.000046905097,0.00023028428,0.000056656765,0.00008553145],"category_scores_gemma":[0.000033419743,0.00010361186,0.00002003889,0.00006789215,0.000012238296,0.00014767426,0.000084527885,0.00012687848,0.000050269304],"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":[0.000031900687,0.00012636483,0.26678962,0.00035473585,0.00058831583,0.0000130388025,0.00062153116,0.16412587,0.14463572,0.006933784,0.063557126,0.352222],"study_design_scores_gemma":[0.00022632937,0.000012857038,0.011911507,0.000040443225,0.000021860089,0.000002852634,0.00000390906,0.94772637,0.028058013,0.00008765159,0.01165945,0.0002487697],"about_ca_topic_score_codex":0.0001501485,"about_ca_topic_score_gemma":0.0000755154,"teacher_disagreement_score":0.7836005,"about_ca_system_score_codex":0.000014010165,"about_ca_system_score_gemma":0.0000034358332,"threshold_uncertainty_score":0.4225169},"labels":[],"label_agreement":null},{"id":"W2189180073","doi":"","title":"Joint distributions of Wind/Waves/Current in West Africa and derivation of multivariate extreme I-FORM contours","year":2007,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"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 Association for Laboratory Animal Science","funders":"","keywords":"Swell; Extreme value theory; Wind wave; Coherence (philosophical gambling strategy); Generalized extreme value distribution; Joint probability distribution; Significant wave height; Wave height; Mathematics; Wind speed; Principal component analysis; Current (fluid); Meteorology; Statistics; Geodesy; Geology; Physics","score_opus":0.035999245503090034,"score_gpt":0.23572451403033567,"score_spread":0.19972526852724565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189180073","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":[0.92948914,0.0003297507,0.061310496,0.00001255288,0.0001482295,0.00007210937,0.000018834768,0.00004137813,0.008577514],"genre_scores_gemma":[0.9986035,0.000025856414,0.001308052,8.401774e-7,0.000018519842,0.0000010317244,0.000017655124,0.0000068022423,0.000017722816],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994062,0.0000050677218,0.00030257966,0.00006692227,0.00006966075,0.00014953007],"domain_scores_gemma":[0.9997648,0.00005166471,0.000044202483,0.000069962116,0.000030545452,0.000038839706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001818934,0.00007556701,0.0001310981,0.000086301894,0.000014430703,0.0000039388497,0.00003155284,0.00003784817,0.000020921658],"category_scores_gemma":[0.000039464416,0.0000700081,0.00002356853,0.00015030577,0.000026196258,0.00007568619,0.00001592931,0.000069876165,4.6994515e-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":[0.00007141466,0.0005041109,0.08174646,0.00090083055,0.00013999664,0.000011194427,0.009667254,0.03209285,0.54662806,0.13783461,0.00028440965,0.19011882],"study_design_scores_gemma":[0.0015443212,0.00008571735,0.75714517,0.00049680326,0.000025924228,0.0000058310457,0.00042162338,0.064266965,0.17116806,0.001023162,0.003427227,0.000389222],"about_ca_topic_score_codex":0.000103225226,"about_ca_topic_score_gemma":0.00026523488,"teacher_disagreement_score":0.6753987,"about_ca_system_score_codex":0.00003062051,"about_ca_system_score_gemma":0.0000067936408,"threshold_uncertainty_score":0.28548473},"labels":[],"label_agreement":null},{"id":"W2192061331","doi":"10.3390/app5041756","title":"A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid","year":2015,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":48,"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; Dalhousie University","funders":"","keywords":"Computer science; Artificial neural network; Sigmoid function; MATLAB; Feature selection; Smart grid; Grid; Feature (linguistics); Selection (genetic algorithm); Artificial intelligence; Data mining; Engineering","score_opus":0.0652869732839335,"score_gpt":0.24372597385873554,"score_spread":0.17843900057480205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2192061331","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":[0.76376206,0.00028136544,0.22546783,0.00017052036,0.0009891071,0.00041597642,0.000012939023,0.00045986963,0.008440361],"genre_scores_gemma":[0.98443586,9.3078853e-7,0.014867496,0.00008476528,0.00047908514,0.0000673444,0.000008704992,0.000018404417,0.00003739439],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988655,0.000010174522,0.00016212519,0.0002778332,0.0002369116,0.0004474637],"domain_scores_gemma":[0.9996108,0.00010436936,0.000042219013,0.00007048193,0.00004820054,0.00012396212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007115198,0.00016623276,0.00015911151,0.000060782368,0.0003203824,0.0001340005,0.00012793537,0.00008855868,8.950901e-7],"category_scores_gemma":[0.0000401738,0.00014939051,0.00003382614,0.00037591328,0.00008675708,0.0001421319,0.000021636153,0.00013540834,9.3170814e-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":[0.00002319366,0.0000049493237,0.000094933566,0.000014991732,0.000004429527,3.2260354e-7,0.0002795654,0.98647326,0.0009921808,0.0018929263,0.001289071,0.00893019],"study_design_scores_gemma":[0.00025785746,0.00005163897,0.000014905649,0.000016269658,0.000011318568,0.0000044358294,0.000045014986,0.9950487,0.0010746181,0.002927413,0.00035994506,0.00018790744],"about_ca_topic_score_codex":0.00001447199,"about_ca_topic_score_gemma":0.00029868374,"teacher_disagreement_score":0.22067384,"about_ca_system_score_codex":0.000045101177,"about_ca_system_score_gemma":0.000093747054,"threshold_uncertainty_score":0.60919684},"labels":[],"label_agreement":null},{"id":"W2199543770","doi":"10.1016/j.procs.2015.08.298","title":"RETRACTED: An adaptive predictor for system property forecasting","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":true,"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":"Computer science; Property (philosophy); Scientific publishing; Publishing; Process (computing); Data science; Work (physics); Scientific misconduct; Operations research; Law; Programming language; Medicine; Political science; Epistemology; Philosophy","score_opus":0.05561450297604993,"score_gpt":0.22186579423495723,"score_spread":0.1662512912589073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2199543770","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":[0.23316282,0.00009742759,0.7567891,0.000015932555,0.0035253006,0.0004847648,0.000014322986,0.0013585538,0.004551758],"genre_scores_gemma":[0.8843766,5.1015274e-7,0.11458907,0.000020026491,0.0009085935,0.0000515081,0.0000039997876,0.000024789695,0.00002489692],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985904,0.000009156083,0.00021736941,0.00035749478,0.0003585599,0.000467037],"domain_scores_gemma":[0.9990376,0.00004198555,0.000046919933,0.00019734428,0.0003413,0.0003348396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000715384,0.00016443123,0.00015464882,0.00011764363,0.0001368337,0.00016085235,0.0004766482,0.00006551255,5.5671273e-7],"category_scores_gemma":[0.00007895949,0.00011942023,0.000029079372,0.00047458254,0.00010131446,0.00095309794,0.00007939259,0.00014098895,0.0000050155036],"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":[0.0001739825,0.00015555968,0.0035471288,0.0014357953,0.0000723489,0.00004743042,0.020960191,0.54338026,0.00479577,0.009734258,0.00505742,0.41063985],"study_design_scores_gemma":[0.00022414778,0.00029002852,0.000096856704,0.00013283157,0.000005390538,0.00005814743,0.000083090665,0.99575984,0.0018323908,0.00007224468,0.0012480123,0.00019700314],"about_ca_topic_score_codex":0.0000073906176,"about_ca_topic_score_gemma":0.000005552862,"teacher_disagreement_score":0.65121377,"about_ca_system_score_codex":0.00015400292,"about_ca_system_score_gemma":0.00018274446,"threshold_uncertainty_score":0.48698154},"labels":[],"label_agreement":null},{"id":"W2201233344","doi":"10.1016/j.enbuild.2015.12.010","title":"Energy Forecasting for Event Venues: Big Data and Prediction Accuracy","year":2015,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":162,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"London Hydro; Western University","funders":"","keywords":"Computer science; Context (archaeology); Support vector machine; Predictive modelling; Big data; Energy consumption; Granularity; Event (particle physics); Data mining; Artificial neural network; Machine learning; Engineering; Geography","score_opus":0.05950155995618753,"score_gpt":0.24272306753251047,"score_spread":0.18322150757632294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2201233344","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":[0.60828954,0.015844189,0.3671123,0.00016919845,0.003836048,0.000114578914,0.00029175894,0.00061191194,0.0037304435],"genre_scores_gemma":[0.9962388,0.00048309556,0.0018323112,0.000062951054,0.001013765,0.000020446261,0.00016075543,0.00003563624,0.00015220682],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992344,0.000010667616,0.00018249116,0.0002457115,0.00009278147,0.00023398064],"domain_scores_gemma":[0.9994785,0.00011871925,0.000043616994,0.00019239669,0.000030785744,0.00013597503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021842736,0.0001478193,0.0001404373,0.00006871404,0.00010534536,0.000057796995,0.0001343182,0.000087289554,0.0000011992131],"category_scores_gemma":[0.000114921924,0.00014578458,0.000019826995,0.00009042327,0.00003063746,0.0002707523,0.00012067049,0.0000553798,1.417431e-7],"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":[0.000045896686,0.000018208235,0.00077018834,0.00009925016,0.00008731346,0.0000074577033,0.00065317634,0.011574371,0.0019773543,0.0061935494,0.012346034,0.9662272],"study_design_scores_gemma":[0.00047025795,0.00006630174,0.000026614402,0.0001055462,0.000026444579,0.000046675974,0.000065581924,0.33591264,0.002458927,0.0017430817,0.65890074,0.00017720224],"about_ca_topic_score_codex":0.000083494386,"about_ca_topic_score_gemma":0.000057642075,"teacher_disagreement_score":0.96604997,"about_ca_system_score_codex":0.000020502599,"about_ca_system_score_gemma":0.000015729676,"threshold_uncertainty_score":0.59449226},"labels":[],"label_agreement":null},{"id":"W2204396457","doi":"","title":"L’estimation de distribution à l'aide d'un autoencodeur","year":2015,"lang":"fr","type":"article","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Estimation; Computer science; Economics","score_opus":0.016556288368776977,"score_gpt":0.19603441939564276,"score_spread":0.17947813102686577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2204396457","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":[0.35239547,0.017432548,0.60713,0.0002353343,0.0019172516,0.00013246239,0.00006251709,0.00007538484,0.020619027],"genre_scores_gemma":[0.9683738,0.0004077976,0.028787049,0.000004699453,0.00013179424,2.706706e-7,0.00004558453,0.000013687947,0.002235313],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887294,0.00010748015,0.00027347656,0.00018205337,0.00027545806,0.0002885748],"domain_scores_gemma":[0.9992701,0.000053864587,0.000092162925,0.0002450139,0.0001623889,0.0001765157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030197552,0.00020567137,0.00025279174,0.00006770854,0.00028907188,0.000014584568,0.00046164764,0.00021618351,0.00003376026],"category_scores_gemma":[0.00011912713,0.00022395079,0.00021936702,0.0003497955,0.0004888352,0.00036394072,0.0001852273,0.00024486624,0.000048519232],"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":[0.00005158041,0.00018190515,0.0028615901,0.0018442953,0.000144776,0.000013963845,0.0046948167,0.9238368,0.0054471865,0.040258717,0.0030397195,0.017624639],"study_design_scores_gemma":[0.0009044653,0.000077623044,0.015699456,0.0044478322,0.0002697247,0.00009180038,0.00016793379,0.8547892,0.07438587,0.0014635414,0.047428664,0.00027391146],"about_ca_topic_score_codex":0.002101632,"about_ca_topic_score_gemma":0.0023228168,"teacher_disagreement_score":0.6159783,"about_ca_system_score_codex":0.002235085,"about_ca_system_score_gemma":0.0002845658,"threshold_uncertainty_score":0.9132448},"labels":[],"label_agreement":null},{"id":"W2234609056","doi":"10.4172/2151-6219.1000165","title":"Non-Linear Series Inversion Method for Forecasting Canadian GDP Growth","year":2015,"lang":"en","type":"article","venue":"Business and Economics Journal","topic":"Energy Load and Power Forecasting","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":"Series (stratigraphy); Economics; Econometrics; Inversion (geology); Time series; Mathematics; Geology; Statistics","score_opus":0.035404173417343296,"score_gpt":0.21403684854556365,"score_spread":0.17863267512822034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2234609056","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":[0.954977,0.0002392367,0.039288107,0.00038501446,0.001842758,0.000076333796,0.000034891444,0.00003118385,0.0031255027],"genre_scores_gemma":[0.85904807,0.00075028284,0.13759346,0.00022732595,0.0020163583,0.000009460521,0.00003860011,0.00009381014,0.00022263231],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941796,0.0000046526907,0.00019525512,0.0000904929,0.000023457442,0.0002681949],"domain_scores_gemma":[0.9994359,0.000023582046,0.000047742968,0.000049467882,0.00012238757,0.00032091537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033475203,0.00011902307,0.00015988095,0.0001315413,0.00017644494,0.00011699901,0.000074410076,0.00006957985,0.0000070434357],"category_scores_gemma":[0.00003645786,0.00011861538,0.000034087112,0.00006707665,0.000013220049,0.00040031,0.0000129193295,0.00010701538,0.0000025938728],"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":[0.00014449435,0.000016638092,0.013291026,0.0004033674,0.00024022805,0.000059773847,0.0032020523,0.83939,0.00025220498,0.0016277549,0.018365782,0.12300669],"study_design_scores_gemma":[0.0008072891,0.000037219703,0.00041113535,0.00007409366,0.000023301456,0.00069189153,0.00030065316,0.9355525,0.00038962887,0.0029663837,0.05843362,0.00031229385],"about_ca_topic_score_codex":0.0030179631,"about_ca_topic_score_gemma":0.01175186,"teacher_disagreement_score":0.122694395,"about_ca_system_score_codex":0.000103601065,"about_ca_system_score_gemma":0.0001123497,"threshold_uncertainty_score":0.6557814},"labels":[],"label_agreement":null},{"id":"W2243107379","doi":"10.1109/fskd.2015.7382083","title":"A statistical model for predicting power demand peaks in power systems","year":2015,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"York University","funders":"Independent Electricity System Operator","keywords":"Electricity; Computer science; Peak demand; Software deployment; Environmental economics; Fiscal year; Work (physics); Commodity; Electricity generation; Power (physics); Operations research; Business; Finance; Economics; Engineering; Electrical engineering; Operating system","score_opus":0.02508364262876674,"score_gpt":0.23761606270737337,"score_spread":0.21253242007860662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2243107379","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":[0.11539317,0.00025995297,0.8285025,0.000012088334,0.00054600247,0.00016600227,0.00003431913,0.0002572123,0.05482875],"genre_scores_gemma":[0.99156874,0.000001638584,0.0075838175,0.000017769853,0.000033806973,0.000038142156,0.000011729968,0.00003597489,0.00070834823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991664,0.000011281115,0.0002525109,0.00013784369,0.00012459641,0.00030734515],"domain_scores_gemma":[0.99959743,0.00010342635,0.000015318526,0.00010917113,0.000039370498,0.00013527696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002991892,0.00012438072,0.00016498499,0.000066213295,0.000021177368,0.000040854306,0.00007532329,0.00008051866,0.000013264238],"category_scores_gemma":[0.00009799426,0.000112483314,0.00002379304,0.00006800746,0.000011643291,0.00011083516,0.00001947003,0.00010456843,0.000007558272],"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":[0.000013135058,0.00001218735,0.0018793438,0.000046227284,0.000014269324,0.0000056346134,0.0011601552,0.9796255,0.00006704653,0.012926699,0.004177484,0.00007228218],"study_design_scores_gemma":[0.00050915295,0.000038230522,0.000073980744,0.00005012675,0.0000048318857,0.000007936989,0.00028226557,0.9973085,0.00003274166,0.0003567016,0.0011911188,0.00014444428],"about_ca_topic_score_codex":0.00002881904,"about_ca_topic_score_gemma":0.000050295435,"teacher_disagreement_score":0.8761756,"about_ca_system_score_codex":0.00005866189,"about_ca_system_score_gemma":0.000025312287,"threshold_uncertainty_score":0.45869365},"labels":[],"label_agreement":null},{"id":"W2250552586","doi":"","title":"Application of a New Hybrid Method for Day-Ahead Energy Price Forecasting in Iranian Electricity Market","year":2012,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Energy Load and Power Forecasting","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":"Electricity price forecasting; Electricity market; Electricity; Economics; Electricity price; Econometrics; Financial economics; Computer science; Engineering; Electrical engineering","score_opus":0.14801421888911304,"score_gpt":0.4637263069368092,"score_spread":0.3157120880476962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2250552586","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":[0.21689585,0.0156187145,0.7518836,0.000026794747,0.00059111015,0.0005658295,0.000045089622,0.000094254145,0.0142787285],"genre_scores_gemma":[0.98284024,0.0010879831,0.015262931,0.000057683952,0.00039692133,0.00008325664,0.000018493338,0.00009060543,0.00016188572],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9976073,0.00015900638,0.0009829274,0.00027807592,0.00033921556,0.0006334767],"domain_scores_gemma":[0.99791545,0.00080543046,0.0005513585,0.00032310962,0.00013288172,0.00027175128],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0023035146,0.00031484754,0.00072381925,0.00074566,0.00009481269,0.00017179128,0.0011689448,0.00011144159,0.0004965762],"category_scores_gemma":[0.00036241993,0.0003236031,0.00017252173,0.0011273304,0.000023425377,0.0015489877,0.0002069195,0.00028093232,8.7505686e-7],"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":[0.00027389842,0.00026904064,0.20310979,0.0005635515,0.0002919043,0.000005680965,0.0005872116,0.023359811,0.16173412,0.00069492596,0.020529278,0.5885808],"study_design_scores_gemma":[0.0017897343,0.000031161715,0.16611066,0.00093638693,0.00017147334,0.0000703318,0.00005525204,0.35360724,0.3951232,0.0074349395,0.07342093,0.0012486711],"about_ca_topic_score_codex":0.001414072,"about_ca_topic_score_gemma":0.00011714098,"teacher_disagreement_score":0.7659444,"about_ca_system_score_codex":0.0001393405,"about_ca_system_score_gemma":0.000068270696,"threshold_uncertainty_score":0.9999216},"labels":[],"label_agreement":null},{"id":"W2256360925","doi":"","title":"A Comparison of Aggregate and Multi-Region Load Forecasting Models in Saskatchewan","year":2012,"lang":"en","type":"dissertation","venue":"oURspace (University of Regina)","topic":"Energy Load and Power Forecasting","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":"Aggregate (composite); Diversification (marketing strategy); Electrical load; Artificial neural network; Aggregate demand; Diversity (politics); Peak load; Electric power system; Computer science; Econometrics; Engineering; Power (physics); Economics; Artificial intelligence; Automotive engineering; Electrical engineering","score_opus":0.03131387289424407,"score_gpt":0.23000915587831786,"score_spread":0.1986952829840738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2256360925","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":[0.9897737,0.0032665036,0.004047768,0.0009887278,0.00023640145,0.0001277823,0.0000081917315,0.00006365286,0.0014872594],"genre_scores_gemma":[0.9831955,0.00023660173,0.0042648003,6.0471126e-7,0.000021686814,3.2418848e-7,0.00006029178,0.00003795106,0.012182255],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993042,0.000024226463,0.00003887691,0.00018577119,0.00017857959,0.00026836427],"domain_scores_gemma":[0.9993571,0.00004320659,0.0002672808,0.00016516988,0.00008403673,0.00008323136],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001153791,0.0002255638,0.0005260892,0.00025997657,0.000053832322,0.000006289151,0.00015966974,0.00029264903,0.0000012650179],"category_scores_gemma":[0.000009831457,0.00030674355,0.000089428395,0.00021237576,0.000055378183,0.0002716782,0.000029428229,0.0002991788,8.047623e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006625166,0.00030270868,0.046393774,0.011812648,0.00073298655,0.00020864978,0.22683553,0.38263562,0.009080866,0.0012373561,0.025399344,0.294698],"study_design_scores_gemma":[0.0020234014,0.00011760137,0.0030520319,0.004955986,0.00026949594,0.000018959536,0.12609133,0.839526,0.0018508084,0.000022973714,0.02113893,0.0009325108],"about_ca_topic_score_codex":0.00021456915,"about_ca_topic_score_gemma":0.021863628,"teacher_disagreement_score":0.45689034,"about_ca_system_score_codex":0.0000973344,"about_ca_system_score_gemma":0.000057858124,"threshold_uncertainty_score":0.9999385},"labels":[],"label_agreement":null},{"id":"W2256372749","doi":"","title":"A New Iterative Neural Based Method to Spot Price Forecasting","year":2015,"lang":"en","type":"article","venue":"International journal of smart electrical engineering","topic":"Energy Load and Power Forecasting","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":"Artificial neural network; Normalization (sociology); Volatility (finance); Computer science; Electricity price forecasting; Electricity market; Electricity; Electricity price; Spot contract; Feature selection; Artificial intelligence; Econometrics; Machine learning; Economics; Engineering; Finance","score_opus":0.02267564258381881,"score_gpt":0.2577668015285304,"score_spread":0.23509115894471155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2256372749","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":[0.027853305,0.00031966038,0.96861595,0.00026490272,0.0017230728,0.000051804065,0.0000023732011,0.0001019977,0.0010669605],"genre_scores_gemma":[0.79387534,0.0000023002099,0.20467198,0.00015174263,0.00119604,0.0000025457794,0.000002121264,0.000042783326,0.0000551334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857116,0.000020778349,0.0004749221,0.000112684575,0.0005222205,0.00029826397],"domain_scores_gemma":[0.99883,0.00024435655,0.000083587,0.00006700845,0.0003314283,0.0004435929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039057163,0.00018839995,0.00023998975,0.0004829226,0.00001612792,0.00009628261,0.00035985524,0.00006163317,0.000021488137],"category_scores_gemma":[0.00045935388,0.00017947028,0.00012012013,0.0004328741,0.0000031244122,0.00028143582,0.00003237405,0.00039278617,0.000006619485],"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":[0.00006340091,0.000011206394,0.0001226213,0.000004992392,0.00009423062,0.00012877185,0.00019883178,0.9498489,0.0046660933,0.0003117811,0.0013194146,0.043229785],"study_design_scores_gemma":[0.0006428227,0.0002085592,0.00010739694,0.00010466038,0.00001395801,0.00041726226,0.0000055495966,0.96472067,0.014247217,0.00007036797,0.01926345,0.00019810365],"about_ca_topic_score_codex":0.000008984237,"about_ca_topic_score_gemma":0.0000015691141,"teacher_disagreement_score":0.766022,"about_ca_system_score_codex":0.00029213788,"about_ca_system_score_gemma":0.00010383835,"threshold_uncertainty_score":0.73185855},"labels":[],"label_agreement":null},{"id":"W2279348442","doi":"10.1016/j.jweia.2016.02.004","title":"Analysis of energy dissipation and turbulence kinetic energy using high frequency data for wind energy applications","year":2016,"lang":"en","type":"article","venue":"Journal of Wind Engineering and Industrial Aerodynamics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Consejo Nacional de Ciencia y Tecnología; Secretaría de Educación Pública","keywords":"Dissipation; Turbulence kinetic energy; Kinetic energy; Turbulence; Energy (signal processing); Wind power; Physics; Mechanics; Environmental science; Classical mechanics; Engineering; Thermodynamics; Electrical engineering","score_opus":0.023526764181368863,"score_gpt":0.22009018992446217,"score_spread":0.1965634257430933,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279348442","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":[0.49150664,0.0007773986,0.507051,0.000028877565,0.0003654355,0.000028863806,0.00019973486,0.000022821408,0.000019217534],"genre_scores_gemma":[0.9954341,0.00071332004,0.0031886566,0.000004899689,0.0005463307,0.0000018973523,0.00006390149,0.000031271647,0.00001563138],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988485,0.000016219888,0.0005921512,0.00017882753,0.00015629081,0.00020801961],"domain_scores_gemma":[0.99906933,0.00021869117,0.00022986408,0.0002588506,0.00009495439,0.0001282906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021052793,0.00019144427,0.00041826253,0.00041019564,0.000047655827,0.00003204938,0.0002183468,0.00018147241,0.0000038029007],"category_scores_gemma":[0.00008292496,0.0001540678,0.000075187316,0.00044440717,0.000038225084,0.00031456334,0.000049862836,0.00010253843,1.35248985e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002994138,0.000027753737,0.0013531754,0.000045349338,0.00095074176,0.0000026638575,0.000031812793,0.8921159,0.03761452,0.0057250774,0.000031512685,0.062071584],"study_design_scores_gemma":[0.0009870377,0.00012240873,0.001254307,0.0002942178,0.0009726522,0.000033153556,0.0000140864,0.9908374,0.0013100406,0.00020989333,0.0036258833,0.0003389203],"about_ca_topic_score_codex":0.00013551647,"about_ca_topic_score_gemma":0.000034956403,"teacher_disagreement_score":0.50392747,"about_ca_system_score_codex":0.000059533108,"about_ca_system_score_gemma":0.00003483149,"threshold_uncertainty_score":0.6282703},"labels":[],"label_agreement":null},{"id":"W2279522025","doi":"10.2139/ssrn.2695385","title":"Using Low Frequency Information for Predicting High Frequency Variables","year":2015,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of Canada; Bank of Canada","funders":"","keywords":"Computer science; Statistics; Mathematics","score_opus":0.013736692920050867,"score_gpt":0.21763374308100022,"score_spread":0.20389705016094936,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279522025","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":[0.4378028,0.0017137829,0.55425984,0.000034676155,0.0015060424,0.00013971486,0.000013894618,0.0002365374,0.0042927214],"genre_scores_gemma":[0.9872784,0.00020656601,0.011628933,0.000024038412,0.00076040166,0.000007740209,0.000024764428,0.000036196154,0.000032977452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99777657,0.00002166503,0.0003987838,0.00008202295,0.00019904398,0.0015219267],"domain_scores_gemma":[0.99946237,0.000040469007,0.00011850416,0.00010553707,0.00015086144,0.00012226898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001138973,0.00017006448,0.00016066467,0.00014336352,0.00017581241,0.00010245085,0.00017948497,0.0001091696,0.000007776608],"category_scores_gemma":[0.0001500316,0.00016541874,0.00006826549,0.00017531205,0.000013936295,0.0011755753,0.00001294341,0.00081682677,0.000006629151],"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":[0.000039714472,0.000035533183,0.007750666,0.00016579275,0.00049572677,0.000004410722,0.001761853,0.5121478,0.005708132,0.44052446,0.00024467628,0.031121224],"study_design_scores_gemma":[0.0026834789,0.0003625036,0.000095236515,0.00031576896,0.00013460575,0.0011120165,0.0019954205,0.25947604,0.0016752689,0.7300296,0.0013132168,0.0008068254],"about_ca_topic_score_codex":0.00015691362,"about_ca_topic_score_gemma":0.0001227629,"teacher_disagreement_score":0.5494756,"about_ca_system_score_codex":0.001053058,"about_ca_system_score_gemma":0.0009306613,"threshold_uncertainty_score":0.67455804},"labels":[],"label_agreement":null},{"id":"W2279575618","doi":"","title":"PERFORMANCE EVALUATION OF NEW AND ADVANCED NEURAL NETWORKS FOR SHORT TERM LOAD FORECASTING: CASE STUDIES FOR MARITIMES AND ONTARIO","year":2014,"lang":"en","type":"dissertation","venue":"Library and Archives Canada (Government of Canada)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Term (time); Artificial neural network; Computer science; Operations research; Industrial engineering; Artificial intelligence; Engineering","score_opus":0.012722212259941927,"score_gpt":0.1924315154199853,"score_spread":0.17970930316004335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2279575618","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":[0.9873541,0.0029476522,0.00008089532,0.00002368805,0.00044606384,0.00042988386,0.000079512705,0.000008658597,0.008629523],"genre_scores_gemma":[0.9957067,0.00027086312,0.0011887361,0.0000246038,0.00008573909,0.00004606676,0.00009417455,0.000033927678,0.002549229],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873894,0.000012591561,0.00031031863,0.00019711239,0.00052677107,0.00021423434],"domain_scores_gemma":[0.99938774,0.0002876219,0.00011637581,0.00008647526,0.0000048854777,0.00011689448],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003060739,0.00023322833,0.00035918402,0.000021919535,0.00012845834,0.00001449352,0.00005870925,0.00004847985,0.0000022505035],"category_scores_gemma":[0.00000970777,0.00023551492,0.000026418445,0.000023849598,0.00002710842,0.00020557508,0.00002431582,0.00010043772,3.161377e-11],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013486326,0.000011181862,0.017169762,0.005342804,0.00060397014,0.000029425768,0.0017615998,0.08702554,0.0012307527,0.0011963396,0.0006704192,0.8836096],"study_design_scores_gemma":[0.000981084,0.0002274223,0.011670388,0.00080246874,0.00030411928,0.00005075864,0.0018390014,0.9747457,0.0071768113,0.00021358224,0.0015494019,0.00043924124],"about_ca_topic_score_codex":0.0021469698,"about_ca_topic_score_gemma":0.4559923,"teacher_disagreement_score":0.88772017,"about_ca_system_score_codex":0.000019732595,"about_ca_system_score_gemma":0.0006102355,"threshold_uncertainty_score":0.96040195},"labels":[],"label_agreement":null},{"id":"W2294620084","doi":"10.1109/hicss.2016.316","title":"Big Data Analytics for Modelling the Impact of Wind Power Generation on Competitive Electricity Market Prices","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Calgary","funders":"","keywords":"Electricity market; Big data; Electricity; Wind power; Electricity generation; Analytics; Industrial organization; Electricity retailing; Environmental economics; Data science; Computer science; Power (physics); Business; Economics; Electrical engineering; Engineering; Data mining","score_opus":0.07623698846193426,"score_gpt":0.26360332488863353,"score_spread":0.18736633642669925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294620084","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":[0.27409545,0.00007574465,0.69603443,0.000035119738,0.00016822718,0.00012586359,0.00014826283,0.000051745577,0.029265137],"genre_scores_gemma":[0.99826896,0.000057186717,0.0012058708,0.000009996127,0.00018100005,0.0000014975859,0.000024459832,0.000015373813,0.00023562855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947715,0.000012080304,0.000150253,0.00012045365,0.000084985084,0.00015509193],"domain_scores_gemma":[0.9993317,0.00027463541,0.000038568214,0.0002824222,0.0000472336,0.000025426523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022179405,0.00009802348,0.00010613798,0.000050150433,0.000043239277,0.000017826334,0.00018330188,0.000037174766,0.000053680033],"category_scores_gemma":[0.00003466795,0.000047981146,0.000049979233,0.000107802254,0.000014597659,0.00010276593,0.000022149801,0.000043529308,0.0000014143114],"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":[0.000029200866,0.000022284168,0.00023413394,0.000008547446,0.00015343782,2.440901e-7,0.00007994468,0.9762406,0.009253289,0.0049093915,0.004097376,0.0049715717],"study_design_scores_gemma":[0.00016025637,0.00008264457,0.00015184219,0.00002451574,0.000013202658,6.393978e-7,0.00000855179,0.98820996,0.009992181,0.00008920622,0.001181246,0.00008576994],"about_ca_topic_score_codex":0.00003878742,"about_ca_topic_score_gemma":0.000024168166,"teacher_disagreement_score":0.72417355,"about_ca_system_score_codex":0.000036770325,"about_ca_system_score_gemma":0.000018018633,"threshold_uncertainty_score":0.19566143},"labels":[],"label_agreement":null},{"id":"W2295444306","doi":"10.4018/ijmstr.2015070103","title":"A Black-Box Model for Estimation of the Induction Machine Parameters Based on Stochastic Algorithms","year":2015,"lang":"en","type":"article","venue":"International Journal of Monitoring and Surveillance Technologies Research","topic":"Energy Load and Power Forecasting","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 du Québec à Chicoutimi","funders":"","keywords":"Asynchronous communication; Identification (biology); Computer science; Genetic algorithm; Black box; Algorithm; Process (computing); Generator (circuit theory); Position (finance); Power (physics); Machine learning; Artificial intelligence","score_opus":0.08471957099865073,"score_gpt":0.34624207801402296,"score_spread":0.2615225070153722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295444306","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":[0.79754436,0.00029340957,0.20019339,0.0005815537,0.0011471794,0.0000967418,0.000017334107,0.00007175661,0.000054267126],"genre_scores_gemma":[0.9916971,0.000057504254,0.00814134,0.0000010224701,0.00007285893,0.0000073046194,0.000001121674,0.000010481576,0.000011315484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899095,0.000030539548,0.00022626144,0.000072623625,0.000546511,0.00013308332],"domain_scores_gemma":[0.9989893,0.00031812183,0.00008381522,0.000107179454,0.00047522265,0.000026399666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000958457,0.0000723312,0.00011658532,0.00028170604,0.000039075498,0.0000334591,0.0003262724,0.00007274594,1.5767775e-7],"category_scores_gemma":[0.001228305,0.000051697407,0.000043944696,0.00015390586,0.00011746619,0.00007677758,0.000046336383,0.00033447394,1.6635431e-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":[0.00006373707,0.0000125814395,0.0013338408,0.000011840217,0.00002688735,0.0000010348656,0.00010173328,0.94482064,0.00041967418,0.000053153886,0.00005688826,0.05309798],"study_design_scores_gemma":[0.00038752373,0.000115102855,0.00036966472,0.00013912556,0.0000015748807,0.00000833908,0.00023875799,0.9901594,0.007069567,0.0014398951,0.000022659191,0.00004838826],"about_ca_topic_score_codex":0.000009296996,"about_ca_topic_score_gemma":0.000001770758,"teacher_disagreement_score":0.19415268,"about_ca_system_score_codex":0.00012339454,"about_ca_system_score_gemma":0.000043393742,"threshold_uncertainty_score":0.2108159},"labels":[],"label_agreement":null},{"id":"W2298351336","doi":"10.1016/j.jweia.2016.02.007","title":"Minimizing errors in interpolated discrete stochastic wind fields","year":2016,"lang":"en","type":"article","venue":"Journal of Wind Engineering and Industrial Aerodynamics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Interpolation (computer graphics); Discretization; Turbulence; Wind speed; Sampling (signal processing); Grid; Computation; Linear interpolation; Turbine; Mathematics; Applied mathematics; Computer science; Meteorology; Algorithm; Mathematical analysis; Engineering; Filter (signal processing); Geometry; Physics; Aerospace engineering","score_opus":0.012876953861677498,"score_gpt":0.19943834077172606,"score_spread":0.18656138691004856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2298351336","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":[0.9869128,0.0001951736,0.011028001,0.00010838302,0.0015654614,0.00004035528,0.0000062924223,0.000041218085,0.00010232722],"genre_scores_gemma":[0.99902284,0.00006023228,0.00036091334,0.000005964153,0.00048086015,3.4364575e-7,0.0000010738596,0.000033303695,0.000034449855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894094,0.000014973806,0.00052898214,0.00009854943,0.00013277114,0.0002837859],"domain_scores_gemma":[0.9995056,0.0001581332,0.00009063744,0.000086967266,0.000027496953,0.00013116663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027612297,0.00019163167,0.00030927302,0.00027748587,0.000021635773,0.000031875217,0.00012924614,0.0002263922,0.000009053078],"category_scores_gemma":[0.00019519363,0.00014232736,0.00006998981,0.0001911702,0.000021580903,0.0002627238,0.000027850101,0.00051053957,8.105692e-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":[0.00004753565,0.000009655825,0.00088592374,0.000021122562,0.00007546355,0.000035970348,0.0003270158,0.981483,0.0065764817,0.00006376138,0.000078905716,0.010395195],"study_design_scores_gemma":[0.0059371577,0.0005718623,0.0037162888,0.0045626895,0.000103715276,0.000472924,0.00028041744,0.9802438,0.0010304513,0.000119954726,0.0019387009,0.0010220454],"about_ca_topic_score_codex":0.000008070902,"about_ca_topic_score_gemma":0.000010431992,"teacher_disagreement_score":0.01211007,"about_ca_system_score_codex":0.00009780327,"about_ca_system_score_gemma":0.000023413475,"threshold_uncertainty_score":0.5803941},"labels":[],"label_agreement":null},{"id":"W2317633928","doi":"10.1109/naps.2014.6965360","title":"On comparison of two strategies in net demand forecasting using Wavelet Neural Network","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Artificial neural network; Computer science; Demand forecasting; Volatility (finance); Morlet wavelet; Wavelet; Electric power system; Wavelet transform; Mathematical optimization; Artificial intelligence; Power (physics); Econometrics; Engineering; Operations research; Mathematics; Discrete wavelet transform","score_opus":0.03709016974128378,"score_gpt":0.26745297404493057,"score_spread":0.2303628043036468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2317633928","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":[0.9286939,0.00008699855,0.04284585,0.000002599603,0.00029869162,0.000043280237,6.389172e-7,0.00009127833,0.027936798],"genre_scores_gemma":[0.9941055,0.000001232042,0.0056547504,0.000018241637,0.00017910909,0.0000013754175,0.000004565882,0.000024836982,0.000010385576],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909925,0.000034033892,0.00033164714,0.00011688488,0.00009979244,0.0003183614],"domain_scores_gemma":[0.9995837,0.0001984217,0.000049264952,0.00011479541,0.000013365898,0.000040452873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019645719,0.00014652195,0.00026109873,0.000070160204,0.000039824496,0.0000327169,0.00008841892,0.000050963867,0.000025086738],"category_scores_gemma":[0.00001874857,0.00013608437,0.0000365961,0.00018450468,0.000020216637,0.00012024211,0.00002198537,0.00016689031,0.0000013286964],"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":[0.000005586255,0.000007933187,0.0068757245,0.000034800014,0.0000070944234,0.0000012334996,0.0001749017,0.97709554,0.0004726422,0.011361469,0.00008706185,0.003876014],"study_design_scores_gemma":[0.00030007446,0.00005313428,0.0005581063,0.00013471571,0.0000053422177,0.0000035276767,0.000082506325,0.99598444,0.0011662217,0.001498233,0.00006980027,0.00014391913],"about_ca_topic_score_codex":0.00011650681,"about_ca_topic_score_gemma":0.0004824595,"teacher_disagreement_score":0.065411635,"about_ca_system_score_codex":0.000019421115,"about_ca_system_score_gemma":0.000006464524,"threshold_uncertainty_score":0.55493593},"labels":[],"label_agreement":null},{"id":"W2319709116","doi":"10.1016/j.enconman.2016.03.078","title":"Using data-driven approach for wind power prediction: A comparative study","year":2016,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":120,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Universiti Malaya; Ryerson University","keywords":"Wind power; Feature selection; Data mining; Computer science; Wind speed; Adaptive neuro fuzzy inference system; Multilayer perceptron; Support vector machine; Random forest; Crossover; Wind power forecasting; Mutual information; Artificial neural network; Electric power system; Artificial intelligence; Machine learning; Power (physics); Engineering; Fuzzy logic; Fuzzy control system; Meteorology","score_opus":0.05906736449610096,"score_gpt":0.2589633877705562,"score_spread":0.19989602327445527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2319709116","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":[0.19716774,0.0003310104,0.7520378,0.000053917673,0.0013779263,0.00075982226,0.00010956783,0.00045050879,0.04771166],"genre_scores_gemma":[0.9945285,0.00008163784,0.0043407422,0.000031375534,0.000060613456,0.000014002424,0.00003894689,0.000014674005,0.00088951807],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993499,0.00001721942,0.00012883992,0.00024831467,0.00010180187,0.00015393777],"domain_scores_gemma":[0.99963784,0.000022588409,0.00002290188,0.00024425867,0.000015208959,0.0000572131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009088832,0.0001222087,0.0001290025,0.00007545543,0.000095854644,0.000022825105,0.00013485498,0.000027866354,0.000031121548],"category_scores_gemma":[0.0000012154919,0.00009100257,0.000020419087,0.00006714357,0.000023272114,0.00017532919,0.00016237356,0.000022901813,0.0000014137872],"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":[0.00074707146,0.001898192,0.009706126,0.0011968607,0.007066077,0.00012441994,0.009712674,0.65420103,0.0051207705,0.07860175,0.1452106,0.08641446],"study_design_scores_gemma":[0.0031524505,0.00018736355,0.0006499225,0.00007912171,0.00014146135,0.0000045299485,0.0024776845,0.7397727,0.000311935,0.000031581705,0.25286186,0.0003294129],"about_ca_topic_score_codex":0.000009393352,"about_ca_topic_score_gemma":0.000004150333,"teacher_disagreement_score":0.7973608,"about_ca_system_score_codex":0.000031120082,"about_ca_system_score_gemma":0.0000028304557,"threshold_uncertainty_score":0.3710977},"labels":[],"label_agreement":null},{"id":"W2322769727","doi":"10.1002/we.1934","title":"A stochastic power curve for wind turbines with reduced variability using conditional copula","year":2015,"lang":"en","type":"article","venue":"Wind Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Turbine; Wind power; Wind speed; Copula (linguistics); Meteorology; Wind power forecasting; Wind profile power law; Environmental science; Econometrics; Statistics; Power (physics); Engineering; Mathematics; Electric power system; Geography; Physics; Aerospace engineering","score_opus":0.025349188135654635,"score_gpt":0.23278275797227047,"score_spread":0.20743356983661582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322769727","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":[0.80264574,0.00013780195,0.19231479,0.000040791696,0.0009502783,0.00011466842,0.00009353478,0.00024771137,0.0034546778],"genre_scores_gemma":[0.99590087,4.076152e-7,0.0033208327,0.0000700571,0.00037919756,0.000011784874,0.00013883389,0.000055575354,0.00012241598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989999,0.000027992988,0.00021640061,0.0002353326,0.00018719683,0.00033320763],"domain_scores_gemma":[0.99933976,0.00011347553,0.000043276672,0.00019802885,0.00012808839,0.00017735842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017473895,0.00021702638,0.00022484154,0.00007142031,0.00007596981,0.000036923713,0.00010705547,0.00010605952,0.000042493386],"category_scores_gemma":[0.000082623585,0.00019312571,0.000055452747,0.00016215793,0.00005866342,0.00020075908,0.000020588746,0.00009022124,0.0000023134073],"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":[0.000057221565,0.00002589745,0.00008367547,0.0000186518,0.00006816081,0.0000045011475,0.00023670813,0.99231994,0.0017645626,0.0046885153,0.0005992366,0.0001329119],"study_design_scores_gemma":[0.003893587,0.00046506926,0.00066705304,0.00027844807,0.00015440396,0.0002570622,0.00028042152,0.9489349,0.006496425,0.012174386,0.024955278,0.0014429541],"about_ca_topic_score_codex":0.000098513236,"about_ca_topic_score_gemma":0.000019883539,"teacher_disagreement_score":0.19325514,"about_ca_system_score_codex":0.00010577232,"about_ca_system_score_gemma":0.0000762474,"threshold_uncertainty_score":0.78754383},"labels":[],"label_agreement":null},{"id":"W2341910059","doi":"10.1109/tpwrs.2015.2438322","title":"A Novel Wavelet-Based Ensemble Method for Short-Term Load Forecasting with Hybrid Neural Networks and Feature Selection","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":226,"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":"Taiyuan University of Technology; University of Saskatchewan","keywords":"Extreme learning machine; Artificial neural network; Computer science; Feature selection; Ensemble learning; Artificial intelligence; Ensemble forecasting; Machine learning; Feature (linguistics); Wavelet; Wavelet transform; Term (time); Data mining","score_opus":0.027361881201614518,"score_gpt":0.23532289711346455,"score_spread":0.20796101591185004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2341910059","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":[0.021640554,0.00024876077,0.9748194,0.00002108993,0.0019269169,0.00044400097,0.00004087422,0.00038523728,0.00047313015],"genre_scores_gemma":[0.98362947,0.0000022997015,0.015762508,0.000028812701,0.000091091344,0.00017096255,0.000009894373,0.000098051496,0.00020689517],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986954,0.00003842872,0.00026083898,0.00033124143,0.0002389618,0.00043515844],"domain_scores_gemma":[0.9992273,0.00017459305,0.000049919177,0.00017915055,0.00016442123,0.00020465131],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033856547,0.0003243207,0.00032291305,0.00013657902,0.00018236083,0.00012698413,0.00008508766,0.00014735655,0.000001907256],"category_scores_gemma":[0.0000054054844,0.00028843683,0.00008657173,0.00024559436,0.000022148099,0.00018539828,7.460793e-7,0.00033761343,7.132565e-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":[0.00014307036,0.000033211123,0.000020404132,0.000104486244,0.000093134084,0.000006737609,0.00017927225,0.9904041,0.0025233005,0.000010724423,0.00035039565,0.0061311373],"study_design_scores_gemma":[0.0010569574,0.00037685785,0.000008060791,0.00019496187,0.00007337019,0.00051025767,0.00006866475,0.9900423,0.0061398307,0.0000011892137,0.0011821099,0.00034540935],"about_ca_topic_score_codex":0.000055591532,"about_ca_topic_score_gemma":0.00020377287,"teacher_disagreement_score":0.9619889,"about_ca_system_score_codex":0.00017569767,"about_ca_system_score_gemma":0.000049646962,"threshold_uncertainty_score":0.9999568},"labels":[],"label_agreement":null},{"id":"W2342050018","doi":"10.3233/ifs-162142","title":"A novel intelligent strategy for probabilistic electricity price forecasting: Wavelet neural network based modified dolphin optimization algorithm","year":2016,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":20,"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":"Electricity price forecasting; Computer science; Probabilistic logic; Probabilistic forecasting; Electricity market; Electricity; Artificial neural network; Mathematical optimization; Econometrics; Artificial intelligence; Economics; Engineering; Mathematics","score_opus":0.04796024059173303,"score_gpt":0.24285127735864456,"score_spread":0.19489103676691152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2342050018","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":[0.007828347,0.0011376335,0.9869279,0.000047311612,0.0026992941,0.0006659007,0.000035187943,0.0001138716,0.00054450723],"genre_scores_gemma":[0.96383405,0.00012377475,0.033680953,0.000030043022,0.0020035955,0.00005733548,0.00001578866,0.00010845087,0.00014598801],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99684703,0.00008679771,0.0016019724,0.00027076222,0.00046851492,0.0007249198],"domain_scores_gemma":[0.99736106,0.0007568903,0.0006930256,0.0002419917,0.00067200465,0.000275],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012349186,0.00041348737,0.0006393678,0.00028449655,0.00012612189,0.0001564342,0.00039905525,0.00019900617,0.00001561623],"category_scores_gemma":[0.00032943347,0.00028960692,0.0003206955,0.00047400064,0.000038560414,0.00029380718,0.000025600468,0.00031005073,0.0000028083505],"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":[0.0001057064,0.0000842017,0.000027512582,0.0002334987,0.00014135262,0.000012105112,0.00007550092,0.96399033,0.0006967461,0.00053895434,0.00059123425,0.033502884],"study_design_scores_gemma":[0.0006575857,0.0005690483,0.000007921827,0.00083721615,0.000070676215,0.0002402467,0.000049560145,0.9934577,0.0022415686,0.00020266329,0.0013056849,0.00036017224],"about_ca_topic_score_codex":0.000021122281,"about_ca_topic_score_gemma":0.000006841278,"teacher_disagreement_score":0.9560057,"about_ca_system_score_codex":0.00045162608,"about_ca_system_score_gemma":0.00013211204,"threshold_uncertainty_score":0.9999556},"labels":[],"label_agreement":null},{"id":"W2344823099","doi":"10.1109/tsg.2015.2502140","title":"A Data-Driven Approach for Estimating the Power Generation of Invisible Solar Sites","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":143,"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 Calgary","funders":"","keywords":"Photovoltaic system; Cluster analysis; Electric power system; Solar power; Principal component analysis; Power (physics); Function (biology); Computer science; Dimension (graph theory); Block (permutation group theory); Electricity generation; Engineering; Mathematics; Electrical engineering; Artificial intelligence","score_opus":0.09231131024793797,"score_gpt":0.2660205589908784,"score_spread":0.17370924874294041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344823099","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":[0.03299008,0.000052373718,0.9643803,0.00003897532,0.0012613346,0.0001726285,0.00023958436,0.00011642122,0.0007482581],"genre_scores_gemma":[0.8927915,0.0000036808772,0.10665212,0.00003214716,0.00025543585,0.000050312166,0.00013381154,0.000031636228,0.00004932081],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993028,0.000022227068,0.00021687947,0.00015910121,0.0001387291,0.0001602715],"domain_scores_gemma":[0.99939674,0.00007568905,0.000035928806,0.00038192468,0.00005547609,0.000054267377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026406435,0.000120132536,0.00013228397,0.00006495644,0.000120787234,0.000033008084,0.00020779108,0.00005719485,0.000009027381],"category_scores_gemma":[0.000012608263,0.000095484975,0.00005324523,0.00014605834,0.000027629609,0.00021816118,0.000001950518,0.00013668738,0.0000040943987],"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":[0.0000074468217,0.000032183656,0.000010579531,0.000023766677,0.000046118013,1.7634099e-7,0.00038895418,0.9911025,0.0033967278,0.000026808651,0.0031948795,0.0017698298],"study_design_scores_gemma":[0.0002462417,0.00005956938,0.0000032992623,0.0000127775575,0.00003571611,0.0000051514153,0.000068806796,0.97728187,0.019437384,0.000010674228,0.00273255,0.00010596135],"about_ca_topic_score_codex":0.000014869598,"about_ca_topic_score_gemma":0.000045095607,"teacher_disagreement_score":0.8598015,"about_ca_system_score_codex":0.000024414001,"about_ca_system_score_gemma":0.000024230198,"threshold_uncertainty_score":0.38937643},"labels":[],"label_agreement":null},{"id":"W2392412830","doi":"","title":"Energy Demand Forecast in China Based on Particle Swarm Optimization Algorithm","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"CAE (Canada)","funders":"","keywords":"Particle swarm optimization; Population; Mathematical optimization; Sample (material); Energy consumption; Energy (signal processing); Exponential growth; Econometrics; Statistics; Computer science; Mathematics; Engineering","score_opus":0.005600526783616405,"score_gpt":0.17601010592751987,"score_spread":0.17040957914390345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2392412830","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":[0.09871435,0.000051660703,0.85858876,0.000087174005,0.00022003957,0.000078257806,0.000001842472,0.00030143542,0.041956455],"genre_scores_gemma":[0.9803443,0.000012223958,0.019115841,0.000120115765,0.000056444016,0.0000357053,0.000015263427,0.000026788295,0.00027330278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938273,0.000014321012,0.00015991788,0.000117339056,0.00008890256,0.00023679662],"domain_scores_gemma":[0.99975693,0.00003244738,0.0000124591315,0.00011798112,0.000012513187,0.00006764994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000058076315,0.000115806215,0.00009764884,0.00006923159,0.000029056151,0.00003836965,0.00006181325,0.00005479859,0.0004578075],"category_scores_gemma":[0.0000066897987,0.00010371383,0.00002687406,0.00018916086,0.000009213423,0.00015242536,0.0000090517005,0.00006392893,0.00002442266],"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":[0.0000012801717,0.000022828037,0.00038641578,0.00000486013,0.0000031611685,0.0000020461111,0.000036820315,0.95773137,0.00007228493,0.00025127904,0.0002661275,0.041221537],"study_design_scores_gemma":[0.0003249232,0.000033090426,0.000651439,0.000024679282,0.0000018386976,0.0000010784945,0.000008431019,0.9871902,0.011200771,0.00007593352,0.00035415392,0.00013347882],"about_ca_topic_score_codex":0.00024132871,"about_ca_topic_score_gemma":0.000060945393,"teacher_disagreement_score":0.88162994,"about_ca_system_score_codex":0.000032689586,"about_ca_system_score_gemma":0.0000052308405,"threshold_uncertainty_score":0.50126714},"labels":[],"label_agreement":null},{"id":"W2460981252","doi":"10.1504/ijbidm.2016.076418","title":"Prediction of retail prices of products using local competitors","year":2016,"lang":"en","type":"article","venue":"International Journal of Business Intelligence and Data Mining","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"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 Regina","funders":"","keywords":"Competitor analysis; Product (mathematics); Vector autoregression; Econometrics; Autoregressive model; Computer science; Industrial organization; Business; Marketing; Economics; Mathematics","score_opus":0.09727817833099535,"score_gpt":0.279116483935165,"score_spread":0.1818383056041697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2460981252","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":[0.55434936,0.00068854087,0.44325542,0.000052680636,0.0014122319,0.00001701106,0.00007732081,0.000008097974,0.00013933521],"genre_scores_gemma":[0.9893462,0.0006656829,0.009631433,0.000003255331,0.00032878004,1.371375e-7,0.0000098023065,0.000008923306,0.000005750304],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902284,0.000009292476,0.0005232573,0.00009453443,0.00027247638,0.00007762684],"domain_scores_gemma":[0.9988363,0.00010743738,0.00028811116,0.00013533075,0.0006056394,0.000027231097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002957155,0.000078799814,0.00016146369,0.00018285243,0.000013714507,0.00001502005,0.00041651307,0.000035223962,0.000023069006],"category_scores_gemma":[0.0002401856,0.000056530203,0.000018396577,0.00014993655,0.00010378246,0.00073369587,0.00010570033,0.000054010645,3.0075944e-7],"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":[0.00014363903,0.0000737545,0.031828236,0.00027160716,0.00040106045,0.00003620823,0.00082689774,0.059371546,0.09429119,0.00061600015,0.00016989555,0.81197],"study_design_scores_gemma":[0.0009905352,0.00028202633,0.02079713,0.013348041,0.00022282607,0.0014034557,0.0024205355,0.278204,0.67024624,0.00041351296,0.011109397,0.0005623271],"about_ca_topic_score_codex":0.000016808011,"about_ca_topic_score_gemma":0.000003318967,"teacher_disagreement_score":0.8114076,"about_ca_system_score_codex":0.000021971136,"about_ca_system_score_gemma":0.00004824715,"threshold_uncertainty_score":0.23052348},"labels":[],"label_agreement":null},{"id":"W2465887865","doi":"10.1109/tii.2016.2585378","title":"Probabilistic Forecasting of Hourly Electricity Price by Generalization of ELM for Usage in Improved Wavelet Neural Network","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":129,"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":"Electricity price forecasting; Probabilistic forecasting; Computer science; Probabilistic logic; Electricity market; Bootstrapping (finance); Artificial neural network; Generalization; Machine learning; Time series; Extreme learning machine; Artificial intelligence; Electricity; Econometrics; Wavelet; Engineering; Economics; Mathematics","score_opus":0.03247552482087535,"score_gpt":0.21765232555713104,"score_spread":0.1851768007362557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2465887865","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":[0.38267,0.000010631365,0.61592484,0.00000956724,0.00054475694,0.00041607945,0.00017115907,0.00005466827,0.00019833757],"genre_scores_gemma":[0.99712896,0.00001868226,0.0026148765,0.000011365961,0.00010047998,0.00004944423,0.000013648205,0.000025495961,0.000037055794],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985321,0.000025741272,0.0009082832,0.0000743872,0.00013502402,0.00032442567],"domain_scores_gemma":[0.99912757,0.00037253465,0.00022891145,0.0001381885,0.000079837955,0.000052933843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031851977,0.00017103371,0.00028533846,0.00014945309,0.000052824602,0.000013422929,0.00012298061,0.00020608385,0.00001009779],"category_scores_gemma":[0.0000622531,0.00014046859,0.000084686864,0.00048912526,0.00003321777,0.00029178645,0.0000011639731,0.00019183836,3.653567e-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":[0.00008964639,0.000041712014,0.000030917487,0.00013773011,0.000038826143,1.0014856e-7,0.00030258796,0.9379901,0.004098907,0.000046486173,0.00042103493,0.05680191],"study_design_scores_gemma":[0.0016082415,0.0002890246,0.0000048423017,0.00021814079,0.000032478492,0.0000024210644,0.000025136054,0.9170056,0.08028052,0.00006216625,0.0002859791,0.00018544261],"about_ca_topic_score_codex":0.000034753735,"about_ca_topic_score_gemma":0.00003628366,"teacher_disagreement_score":0.614459,"about_ca_system_score_codex":0.00010184135,"about_ca_system_score_gemma":0.000044225177,"threshold_uncertainty_score":0.5728143},"labels":[],"label_agreement":null},{"id":"W2486671495","doi":"10.1007/978-3-319-33681-7_24","title":"Trends in Short-Term Renewable and Load Forecasting for Applications in Smart Grid","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Smart grid; Renewable energy; Computer science; Grid; Term (time); Demand response; Demand forecasting; Industrial engineering; Operations research; Engineering; Electricity; Electrical engineering","score_opus":0.027989621554565844,"score_gpt":0.23463029527122592,"score_spread":0.20664067371666006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2486671495","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030840782,0.001367095,0.9830444,0.00018965149,0.0005885264,0.00080025895,0.00023528405,0.000096909236,0.010593802],"genre_scores_gemma":[0.16619438,0.00046950742,0.83219296,0.00004694432,0.00041391343,0.00026578,0.00016539676,0.00007397305,0.00017714278],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990118,0.0000031803816,0.0005303133,0.0001153303,0.0001065346,0.00023281468],"domain_scores_gemma":[0.99928087,0.00030084493,0.00009202862,0.00024018995,0.000052636882,0.000033410408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026948014,0.00022936668,0.00031205747,0.00031395865,0.00028492324,0.00006912903,0.00047334732,0.00017570313,5.527972e-7],"category_scores_gemma":[0.000022417496,0.00018552007,0.000096971,0.00018787237,0.00016847363,0.00019515518,0.00017642914,0.00021692658,4.6404733e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000014165964,0.000004904112,0.00006647345,0.0002705592,0.000027565842,4.3141398e-8,0.000867956,0.94094837,0.000021011863,0.005594462,0.000020289623,0.05217698],"study_design_scores_gemma":[0.00035532287,0.000038676262,0.00015676145,0.00075089384,0.000032125114,0.000007850925,0.0000060098096,0.9615663,0.00013236598,0.0009820401,0.035565432,0.0004062222],"about_ca_topic_score_codex":0.000028676308,"about_ca_topic_score_gemma":0.0010322592,"teacher_disagreement_score":0.1631103,"about_ca_system_score_codex":0.00009750733,"about_ca_system_score_gemma":0.00004763093,"threshold_uncertainty_score":0.7565289},"labels":[],"label_agreement":null},{"id":"W2491125387","doi":"10.1007/978-3-319-33681-7_36","title":"Day-Ahead Electricity Spike Price Forecasting Using a Hybrid Neural Network-Based Method","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Spike (software development); Artificial neural network; Electricity price forecasting; Electricity; Electricity market; Computer science; Electricity price; Econometrics; Machine learning; Economics; Engineering","score_opus":0.03488837847052782,"score_gpt":0.2454426598362291,"score_spread":0.21055428136570126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2491125387","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00063974573,0.0006101842,0.9940957,0.00009121614,0.0008202912,0.00036135525,0.00008473491,0.00014426153,0.00315252],"genre_scores_gemma":[0.038095776,0.000042617983,0.96112895,0.00010516805,0.0004992433,0.000014438116,0.0000337356,0.000053420463,0.000026640766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848384,0.000015645399,0.00069575745,0.00015030116,0.00022530391,0.00042914168],"domain_scores_gemma":[0.99843115,0.0006281527,0.00036279086,0.00039511017,0.00011934943,0.00006343811],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005580805,0.00038553582,0.00045994335,0.00020098369,0.0008267817,0.00013231268,0.00093265943,0.00019699377,0.0000016704056],"category_scores_gemma":[0.00006537045,0.00030426888,0.00024157506,0.00023782955,0.00020574527,0.0002550817,0.00027387496,0.00049029995,2.1899363e-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":[0.0000012936193,0.0000029407324,0.0000033166498,0.00018524368,0.0000606977,1.4914659e-7,0.00015567627,0.97036964,0.00002894679,0.003453034,0.00002859832,0.025710467],"study_design_scores_gemma":[0.0001771473,0.00003379924,0.0000042521574,0.0004642396,0.00006337897,0.000021275138,5.42205e-7,0.9868486,0.00026134614,0.0004620541,0.011307408,0.00035594596],"about_ca_topic_score_codex":0.000014698279,"about_ca_topic_score_gemma":0.00003165344,"teacher_disagreement_score":0.037456032,"about_ca_system_score_codex":0.00012047525,"about_ca_system_score_gemma":0.00011601827,"threshold_uncertainty_score":0.99994093},"labels":[],"label_agreement":null},{"id":"W2507202755","doi":"10.5281/zenodo.61494","title":"Decision Support for Urban Wind Energy Extraction","year":2007,"lang":"en","type":"article","venue":"INFM-OAR (INFN Catania)","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"McMaster University","funders":"","keywords":"Extraction (chemistry); Wind power; Environmental science; Computer science; Meteorology; Geography; Engineering","score_opus":0.012727580194061986,"score_gpt":0.2464148120885012,"score_spread":0.2336872318944392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507202755","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":[0.51060146,0.00097350375,0.41843134,0.00004319219,0.006645873,0.00030472898,0.00009987754,0.0011857959,0.061714217],"genre_scores_gemma":[0.9909433,0.00004497882,0.006728903,0.000118588214,0.0009164502,0.000011118319,0.00013109212,0.00008363748,0.0010218925],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986062,0.0000062370573,0.000414795,0.00024176227,0.00022988774,0.0005011346],"domain_scores_gemma":[0.9991124,0.0002838103,0.000062142724,0.00030707158,0.00006932252,0.00016523749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003915386,0.0002257577,0.00019792118,0.00017130606,0.00012155253,0.00005387643,0.00018300064,0.00019894166,0.000121229],"category_scores_gemma":[0.00007004044,0.00024286058,0.00011234753,0.00021524802,0.000025008008,0.00031140767,0.000027605558,0.00016122154,0.000058395726],"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":[0.00032342743,0.00013377743,0.0036167367,0.00018250581,0.00020090208,0.0001653337,0.0012833575,0.041459017,0.09576899,0.011406503,0.08209332,0.7633661],"study_design_scores_gemma":[0.00062987395,0.000117332536,0.0015766609,0.000060700037,0.000029900899,0.000078820725,0.000059549508,0.009447855,0.05896448,0.00044729043,0.9281439,0.00044364],"about_ca_topic_score_codex":0.000050633003,"about_ca_topic_score_gemma":0.0002665303,"teacher_disagreement_score":0.84605056,"about_ca_system_score_codex":0.00012254227,"about_ca_system_score_gemma":0.000028474728,"threshold_uncertainty_score":0.99035674},"labels":[],"label_agreement":null},{"id":"W2508746991","doi":"","title":"PREDICTING EXHAUST SOUND POWER LEVELS OF GENERAL PURPOSE BOILERS","year":2016,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"York University","keywords":"Boiler (water heating); Sound power; Environmental science; Acoustics; Ranging; Meteorology; Engineering; Sound (geography); Waste management; Physics; Telecommunications","score_opus":0.014444217797622606,"score_gpt":0.19827853503657403,"score_spread":0.1838343172389514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2508746991","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":[0.9666871,0.00018034295,0.00899453,0.000034979803,0.0012008093,0.00006488632,0.00043643356,0.00011870776,0.022282254],"genre_scores_gemma":[0.99773556,0.00001692183,0.0009583618,0.00006277823,0.0002291327,0.0000031387124,0.0000041339126,0.000049203056,0.00094077963],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.999082,0.000007854429,0.00022241878,0.00012433412,0.00011183706,0.00045153967],"domain_scores_gemma":[0.9992898,0.000056767454,0.000031759995,0.00020420493,0.000053690834,0.00036379573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000084515195,0.00014564091,0.00014818863,0.00014640276,0.00005907532,0.000018412078,0.00015206571,0.00011431953,0.00029700875],"category_scores_gemma":[0.00008395767,0.00012649068,0.000045065095,0.00012568345,0.00005822478,0.00008846039,0.000013621937,0.00009612604,0.00001984399],"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":[0.000009752999,0.000020229643,0.04102802,0.00026219635,0.00030292274,0.00025000508,0.0017540173,0.20030916,0.68581164,0.0049653277,0.028040858,0.037245892],"study_design_scores_gemma":[0.007964134,0.0008416872,0.39415446,0.0036736536,0.00077822094,0.000566529,0.002223459,0.34533846,0.09392141,0.006947918,0.13520129,0.008388773],"about_ca_topic_score_codex":0.0017253347,"about_ca_topic_score_gemma":0.004740503,"teacher_disagreement_score":0.5918902,"about_ca_system_score_codex":0.00019232176,"about_ca_system_score_gemma":0.00012653183,"threshold_uncertainty_score":0.51581395},"labels":[],"label_agreement":null},{"id":"W2510164385","doi":"10.1109/tsg.2016.2603421","title":"Assessing Benefits of Volt-Var Control Schemes Using AMI Data Analytics","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"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":"Volt; Computer science; Analytics; Data analysis; Control (management); Reliability engineering; Electrical engineering; Data mining; Engineering; Voltage; Artificial intelligence","score_opus":0.06352765120851782,"score_gpt":0.27008660071369606,"score_spread":0.20655894950517822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2510164385","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":[0.21634342,0.00012629353,0.78116614,0.000028254488,0.0014559184,0.000053158914,0.0003140452,0.00015367514,0.00035906586],"genre_scores_gemma":[0.9952904,0.00006652556,0.0043213293,0.000021797288,0.00019980264,0.0000027624753,0.000006915931,0.000044977314,0.000045498142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989643,0.000022764929,0.00032818996,0.00022465682,0.00019195655,0.000268122],"domain_scores_gemma":[0.99906915,0.00017945033,0.000056979017,0.00055611593,0.000058072423,0.000080215905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001674246,0.00018252622,0.0002480056,0.00016004458,0.000105414285,0.000039737704,0.000250061,0.00009368105,0.000065894914],"category_scores_gemma":[0.0000101243095,0.00014935141,0.00007835194,0.00022526595,0.000050994804,0.00059615064,0.0000022185197,0.00014614688,0.0000121999055],"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":[0.000022528468,0.00008293587,0.00055097614,0.00008116214,0.0003141438,0.0000045994416,0.00005662932,0.83870894,0.08448748,0.000092514536,0.00018441348,0.07541365],"study_design_scores_gemma":[0.0017467435,0.00007049424,0.00059070345,0.0009051918,0.00034381248,0.000030765525,0.0000522923,0.707244,0.28140572,0.000028154765,0.0069665075,0.00061562954],"about_ca_topic_score_codex":0.000032864573,"about_ca_topic_score_gemma":0.00004985211,"teacher_disagreement_score":0.778947,"about_ca_system_score_codex":0.000052626237,"about_ca_system_score_gemma":0.000031392847,"threshold_uncertainty_score":0.6090374},"labels":[],"label_agreement":null},{"id":"W2511296364","doi":"10.1016/j.epsr.2016.08.005","title":"Forecasting day-ahead price spikes for the Ontario electricity market","year":2016,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electricity price forecasting; Electricity; Artificial neural network; Spike (software development); Electricity market; Electricity price; Econometrics; Computer science; Economics; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.05089965579623061,"score_gpt":0.27581244461580917,"score_spread":0.22491278881957855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2511296364","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":[0.29947022,0.025751188,0.36741248,0.00073688244,0.006350351,0.0053602387,0.000059928807,0.0014152884,0.29344344],"genre_scores_gemma":[0.9773772,0.00013608263,0.00009769123,0.0000117987465,0.00040449834,0.00044501305,0.0000023086366,0.00009218697,0.021433257],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9962019,0.00025970888,0.00050593156,0.0004388125,0.0008424093,0.001751274],"domain_scores_gemma":[0.9942404,0.004570464,0.00006923011,0.00056712213,0.00035977058,0.00019298513],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004757407,0.00029943677,0.0003395851,0.00043843527,0.0005938161,0.00021470159,0.0007398121,0.00018808799,0.0001567682],"category_scores_gemma":[0.0009524749,0.00017729569,0.00014186621,0.0013258354,0.00005801286,0.00023634352,0.00007953992,0.00065483607,0.000044886918],"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":[0.00072478957,0.00030426527,0.015635405,0.0011708875,0.0014533754,0.00015600392,0.00608394,0.0061949966,0.09341335,0.009677022,0.64329803,0.22188796],"study_design_scores_gemma":[0.0012253148,0.0006066968,0.002088318,0.0004921341,0.000030799685,0.000159933,0.00009598141,0.19212753,0.010640535,0.0003326974,0.79142916,0.00077090703],"about_ca_topic_score_codex":0.0030631984,"about_ca_topic_score_gemma":0.002590795,"teacher_disagreement_score":0.67790693,"about_ca_system_score_codex":0.0010056085,"about_ca_system_score_gemma":0.00025259325,"threshold_uncertainty_score":0.7229909},"labels":[],"label_agreement":null},{"id":"W2521080030","doi":"10.1007/s40095-016-0220-6","title":"Short-term wind speed forecasting using artificial neural networks for Tehran, Iran","year":2016,"lang":"en","type":"article","venue":"International journal of energy and environmental engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":73,"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":"Wind speed; Wind power; Particle swarm optimization; Artificial neural network; Mean squared error; Renewable energy; Turbine; Adaptive neuro fuzzy inference system; Computer science; Engineering; Artificial intelligence; Meteorology; Fuzzy logic; Machine learning; Fuzzy control system; Statistics; Mathematics","score_opus":0.02127306420834795,"score_gpt":0.20852331784211323,"score_spread":0.18725025363376527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2521080030","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":[0.8024212,0.00036776494,0.1952418,0.000016537742,0.0018573024,0.000021808,0.000014066656,0.000022290456,0.000037212703],"genre_scores_gemma":[0.99751973,0.00012408437,0.0010851669,0.000012547691,0.0011965084,5.653568e-7,0.000007807183,0.000039985993,0.000013623674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914175,0.0000056694134,0.00036891858,0.000100903024,0.0001679508,0.00021478716],"domain_scores_gemma":[0.9996793,0.00009951631,0.00006321351,0.00005005546,0.000012278163,0.00009563864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010202984,0.00016544007,0.00016232264,0.000125919,0.000039912673,0.000036864098,0.00014285326,0.000069133726,0.000017577255],"category_scores_gemma":[0.000015563346,0.00013579741,0.00010440826,0.000028150362,0.000028300698,0.0002923759,0.000033916094,0.00007903657,1.1107619e-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":[0.00003591142,0.000010852555,0.0011058073,0.0000054469656,0.00011279057,0.000030368794,0.000025481195,0.88131136,0.07999024,0.00017440521,0.0000068792438,0.03719048],"study_design_scores_gemma":[0.00041561003,0.00004788148,0.000674766,0.0001450976,0.000025781394,0.0004033039,0.00001821017,0.98949033,0.007864902,0.00005922977,0.00065736315,0.00019753767],"about_ca_topic_score_codex":0.0000013605907,"about_ca_topic_score_gemma":0.0000015073758,"teacher_disagreement_score":0.19509849,"about_ca_system_score_codex":0.00010088775,"about_ca_system_score_gemma":0.000002506771,"threshold_uncertainty_score":0.5537658},"labels":[],"label_agreement":null},{"id":"W2522092224","doi":"10.1109/eeeic.2016.7555531","title":"Long- and short-term electric load forecasting on quarter-hour data: A 3-torus approach","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"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":"Term (time); Quarter (Canadian coin); Econometrics; Computer science; Detrended fluctuation analysis; Statistics; Mathematics; Scaling; Geography","score_opus":0.037859956542858356,"score_gpt":0.2266519874531924,"score_spread":0.18879203091033403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2522092224","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":[0.84023976,0.0005710111,0.07778686,0.00003574735,0.00037333855,0.00015642481,0.000019405436,0.0006922218,0.08012525],"genre_scores_gemma":[0.9979008,0.000065718545,0.0012559249,0.000023614179,0.00029040436,0.000010287562,0.00001479109,0.00004754225,0.00039094914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987785,0.00001557506,0.00022105424,0.0003640812,0.00018532245,0.00043545876],"domain_scores_gemma":[0.9992673,0.0001230263,0.000020030338,0.0004478164,0.000021081745,0.00012076028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020378761,0.0002248935,0.0001909124,0.00009498389,0.00007122385,0.000061728264,0.0002463689,0.00008982941,0.00002955564],"category_scores_gemma":[0.000036897276,0.0001521469,0.000030509533,0.0001591412,0.000018437755,0.00030286342,0.000074532545,0.0001311069,0.000019800545],"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":[0.0000744664,0.00015108874,0.030114902,0.00031251225,0.00028694668,0.000110227404,0.00086494716,0.001692395,0.029597457,0.0020333654,0.0074843382,0.9272773],"study_design_scores_gemma":[0.0025865408,0.0007115261,0.013429932,0.00076925365,0.00012821556,0.0006227117,0.00018599881,0.95889395,0.011359426,0.00023983489,0.008653427,0.002419187],"about_ca_topic_score_codex":0.000011643196,"about_ca_topic_score_gemma":0.000035781493,"teacher_disagreement_score":0.95720154,"about_ca_system_score_codex":0.00007035563,"about_ca_system_score_gemma":0.000017705124,"threshold_uncertainty_score":0.620437},"labels":[],"label_agreement":null},{"id":"W2527543035","doi":"10.1007/978-3-319-47096-2_24","title":"Predicting the Electricity Consumption of Buildings: An Improved CBR Approach","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Computer science; Electricity; Similarity (geometry); Case-based reasoning; Consumption (sociology); Adaptation (eye); Energy consumption; Machine learning; Artificial intelligence; Data mining; Engineering","score_opus":0.014167154149723716,"score_gpt":0.21335776438421822,"score_spread":0.1991906102344945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2527543035","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":[0.007243595,0.0004327229,0.9866346,0.000017559454,0.0007199579,0.00017497373,0.000007776182,0.00015443149,0.004614357],"genre_scores_gemma":[0.9595253,0.00006858296,0.03967116,0.00007408515,0.00055118,0.0000047162575,0.0000043073264,0.00004413582,0.00005655947],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985381,0.000014717405,0.00032971546,0.00044006613,0.00030915914,0.00036827472],"domain_scores_gemma":[0.9990241,0.00026160004,0.00013660907,0.00043896932,0.00007571063,0.0000629978],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005538215,0.0002800316,0.00027387965,0.00024529977,0.00013644935,0.00007192391,0.0007527464,0.00020875776,0.000007953725],"category_scores_gemma":[0.000045421435,0.00018649538,0.00006392615,0.00017479807,0.00034609553,0.00020016594,0.000131929,0.00050122244,0.0000015839711],"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":[0.0000091981,0.000015273596,0.00082675106,0.00019626756,0.000031781743,0.0000030536728,0.0010351103,0.28673056,0.011394954,0.00403572,0.000004200429,0.6957171],"study_design_scores_gemma":[0.00013600958,0.00007785642,0.0001052331,0.00032797846,0.000012438306,0.00002647411,1.6956845e-7,0.9829693,0.009211224,0.006645265,0.00018620859,0.00030188015],"about_ca_topic_score_codex":0.000013275799,"about_ca_topic_score_gemma":0.000017480023,"teacher_disagreement_score":0.95228165,"about_ca_system_score_codex":0.000109712455,"about_ca_system_score_gemma":0.00006361656,"threshold_uncertainty_score":0.7605061},"labels":[],"label_agreement":null},{"id":"W2527961433","doi":"10.1109/icpes.2016.7584044","title":"Short-term load forecasting of Toronto Canada by using different ANN algorithms","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":31,"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":"Term (time); Computer science; Electric power system; Electricity; Conjugate gradient method; Probabilistic forecasting; Demand forecasting; Demand response; Relation (database); Bayesian probability; MATLAB; Electrical load; Algorithm; Power (physics); Operations research; Data mining; Artificial intelligence; Engineering","score_opus":0.018745937834873074,"score_gpt":0.20855485570584398,"score_spread":0.1898089178709709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2527961433","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":[0.97559726,0.0008798466,0.011546798,0.000008738628,0.0006293791,0.00005419437,0.00004970021,0.00009899515,0.011135111],"genre_scores_gemma":[0.9987634,0.00003874584,0.0006673653,0.000008686601,0.000103566425,0.000002736204,0.000004591623,0.000032961972,0.0003779651],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99903387,0.000008229283,0.00027154386,0.00014151969,0.00021930848,0.0003255184],"domain_scores_gemma":[0.99960494,0.00006386222,0.000026903013,0.00015801235,0.00004069578,0.00010561468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000051647276,0.0001762574,0.00020140875,0.000009785306,0.000042528474,0.000010212547,0.00011926428,0.000052017553,0.0002059838],"category_scores_gemma":[0.000013230757,0.00011922153,0.00004284463,0.00002907896,0.000016583886,0.00014109876,0.000035463127,0.000042016647,3.6914514e-7],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001389095,0.00004479605,0.022660092,0.00021400182,0.00023953734,0.000030468416,0.00038982095,0.006198947,0.36169896,0.00014122782,0.008115295,0.600253],"study_design_scores_gemma":[0.0011534266,0.000105892206,0.003592044,0.0010031435,0.00007800986,0.000066210814,0.00023397212,0.44104365,0.5415753,0.000034159882,0.009624507,0.0014896706],"about_ca_topic_score_codex":0.23365098,"about_ca_topic_score_gemma":0.5748488,"teacher_disagreement_score":0.5987633,"about_ca_system_score_codex":0.00052478974,"about_ca_system_score_gemma":0.000049327315,"threshold_uncertainty_score":0.7714522},"labels":[],"label_agreement":null},{"id":"W2532427494","doi":"10.1109/iceas.2011.6147178","title":"Mining of electricity prices in energy markets using a computationally efficient neural network","year":2011,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"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":"Artificial neural network; Computer science; Trigonometry; Energy (signal processing); Electricity; Electricity market; Mode (computer interface); Block (permutation group theory); Layer (electronics); Artificial intelligence; Engineering; Mathematics; Electrical engineering; Statistics","score_opus":0.020459402978322862,"score_gpt":0.2020809953118201,"score_spread":0.18162159233349726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2532427494","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":[0.9394128,0.00028024442,0.041028135,8.5137236e-7,0.00016030655,0.000020392872,3.21352e-7,0.00006510959,0.019031856],"genre_scores_gemma":[0.9746323,0.000004473031,0.025277415,0.000017139602,0.000043596472,0.0000013070648,0.0000020619143,0.000012985348,0.000008690999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936426,0.0000142505005,0.00022540383,0.00008972719,0.00008999929,0.00021637123],"domain_scores_gemma":[0.9997981,0.00006386831,0.00003793631,0.000054176202,0.000015843572,0.000030035446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000117703734,0.0000889981,0.00012411042,0.000092511524,0.00002158617,0.000005798189,0.00007361639,0.00003693206,0.000034423305],"category_scores_gemma":[0.000007657755,0.00008722343,0.000029448538,0.00033967488,0.000011610161,0.000040585724,0.00001912407,0.000050020524,2.7101237e-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":[0.000009159809,0.000015236384,0.004124482,0.000011682037,0.0000109216635,0.0000042112697,0.0002659553,0.9909655,0.00048004213,0.001197122,0.000020028427,0.0028956945],"study_design_scores_gemma":[0.00011391751,0.000013267855,0.01265582,0.000037175643,0.000003822056,0.000005370059,0.000015207086,0.98509014,0.0018671136,0.00006248964,0.00004126881,0.00009442129],"about_ca_topic_score_codex":0.00013623865,"about_ca_topic_score_gemma":0.00007355699,"teacher_disagreement_score":0.035219546,"about_ca_system_score_codex":0.000022594566,"about_ca_system_score_gemma":0.000010940432,"threshold_uncertainty_score":0.35568684},"labels":[],"label_agreement":null},{"id":"W2533706881","doi":"10.1109/iceas.2011.6147107","title":"Price forecasting using computational intelligence techniques: A comparative analysis","year":2011,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Computer science; Support vector machine; Particle swarm optimization; Volatility (finance); Electricity market; Computational intelligence; Robustness (evolution); Electricity price forecasting; Electricity; Mathematical optimization; Swarm intelligence; Interpretability; Artificial intelligence; Machine learning; Econometrics; Economics; Engineering; Mathematics","score_opus":0.11609603582929293,"score_gpt":0.28118319704044337,"score_spread":0.16508716121115044,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2533706881","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":[0.06869609,0.000039117338,0.797833,6.6988974e-7,0.00005174619,0.000050254148,0.000002542877,0.00036743225,0.13295913],"genre_scores_gemma":[0.74402016,0.0000015724913,0.255892,0.000013660233,0.000024572557,0.000004088076,0.000006921245,0.000008287164,0.000028732784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992928,0.000014134403,0.0002469618,0.00013968363,0.000106200336,0.00020021178],"domain_scores_gemma":[0.9996767,0.00006778934,0.00003939746,0.000095574884,0.00006222147,0.00005830322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011313037,0.00013319135,0.00019161192,0.00023303917,0.00006994568,0.00002085981,0.0001137534,0.0000451456,0.00029004933],"category_scores_gemma":[0.0000073180972,0.00012730506,0.00008293634,0.0008321671,0.000031551466,0.00015213157,0.00002787178,0.00010391276,0.0000088312345],"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":[0.0000035223418,0.00001728789,0.0018296732,0.00001615945,0.00033430243,0.000005908533,0.002911998,0.98657745,0.00015375172,0.004443968,0.000024413632,0.003681563],"study_design_scores_gemma":[0.000017950728,0.00001393872,0.0002182856,0.000021507693,0.00007369553,0.000010690993,0.00023790867,0.986777,0.011587553,0.0007609491,0.00011295919,0.00016757856],"about_ca_topic_score_codex":0.00008050435,"about_ca_topic_score_gemma":0.000034891622,"teacher_disagreement_score":0.6753241,"about_ca_system_score_codex":0.00004283323,"about_ca_system_score_gemma":0.000010379721,"threshold_uncertainty_score":0.519135},"labels":[],"label_agreement":null},{"id":"W2537623639","doi":"10.1109/isgteurope.2013.6695249","title":"Mid-term electricity market clearing price forecasting using multiple support vector machine","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Saskatchewan","funders":"","keywords":"Electricity market; Bidding; Electricity; Support vector machine; Electricity price forecasting; Computer science; Term (time); Scheduling (production processes); Market clearing; Operations research; Economics; Artificial intelligence; Engineering; Microeconomics; Operations management","score_opus":0.020155566920009683,"score_gpt":0.20537262287682626,"score_spread":0.18521705595681656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2537623639","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":[0.90357375,0.00010269134,0.020165086,0.000010833368,0.0004692745,0.0002061367,0.000004979746,0.00072757574,0.074739665],"genre_scores_gemma":[0.9882807,0.000011301822,0.010392726,0.00004995129,0.0002121674,0.000014796663,0.000012868264,0.0000868725,0.00093863014],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847287,0.000024531651,0.00036989932,0.00026179475,0.00018470343,0.0006862153],"domain_scores_gemma":[0.999322,0.00016859916,0.000057397145,0.00022769421,0.000053578002,0.00017072003],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00019793477,0.00028047018,0.00025272006,0.00014321221,0.00015701649,0.00012146107,0.00019809164,0.000110692155,0.0021562637],"category_scores_gemma":[0.00009890242,0.00026935668,0.00009580108,0.00033244546,0.000018462744,0.00042099954,0.00008158545,0.00029186063,0.00006269799],"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":[0.00006859454,0.00019636619,0.27998745,0.0010017671,0.0005363998,0.00013773375,0.0014103663,0.14257544,0.3788781,0.00025424623,0.008965953,0.18598759],"study_design_scores_gemma":[0.00033133413,0.000029913417,0.0066927294,0.000046500656,0.000015522894,0.000071038885,0.000018373143,0.9732285,0.018152079,0.00002129972,0.0010158232,0.00037684588],"about_ca_topic_score_codex":0.00049108226,"about_ca_topic_score_gemma":0.000081982296,"teacher_disagreement_score":0.8306531,"about_ca_system_score_codex":0.00012162245,"about_ca_system_score_gemma":0.000018035455,"threshold_uncertainty_score":0.99997586},"labels":[],"label_agreement":null},{"id":"W2539840639","doi":"10.1155/2016/5790464","title":"The Impact of Variable Wind Shear Coefficients on Risk Reduction of Wind Energy Projects","year":2016,"lang":"en","type":"article","venue":"International Scholarly Research Notices","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wind shear; Wind power; Wind speed; Environmental science; Wind gradient; Coefficient of variation; Ranging; Wind profile power law; Mean squared error; Meteorology; Standard deviation; Statistics; Mathematics; Geology; Geodesy; Physics; Engineering","score_opus":0.040390751955719534,"score_gpt":0.32852608713108317,"score_spread":0.2881353351753636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2539840639","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":[0.9809262,0.00016086295,0.0003827923,0.000059924296,0.00047173622,0.00007505607,0.00006235233,0.000026499902,0.017834555],"genre_scores_gemma":[0.9987322,0.00015971091,0.000130634,0.0000010254748,0.0002035782,0.000004033444,0.0000045145675,0.000017776047,0.0007465386],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99829847,0.00016045308,0.00025703938,0.00015514153,0.0008521031,0.00027678985],"domain_scores_gemma":[0.9983687,0.00068517466,0.0000887283,0.00020775995,0.00058443524,0.00006520457],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014777943,0.000096142874,0.000103581515,0.00024238403,0.00014085865,0.00015152963,0.00046824862,0.0000692109,0.00008795588],"category_scores_gemma":[0.0010713967,0.000053740234,0.0000678135,0.00032446132,0.0001367346,0.00069055945,0.000068585534,0.0002884483,0.000012185914],"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":[0.0007812229,0.00039688643,0.027659163,0.000051366245,0.0010687001,0.000006670769,0.00087027584,0.5285339,0.31866494,0.021345787,0.0025713483,0.09804976],"study_design_scores_gemma":[0.0056417272,0.0045987684,0.1788051,0.0039975126,0.00007267103,0.000039748324,0.000832425,0.13946113,0.5967616,0.010645503,0.05792571,0.0012181595],"about_ca_topic_score_codex":0.0010106125,"about_ca_topic_score_gemma":0.000012703362,"teacher_disagreement_score":0.38907278,"about_ca_system_score_codex":0.00016746069,"about_ca_system_score_gemma":0.000080919555,"threshold_uncertainty_score":0.21914631},"labels":[],"label_agreement":null},{"id":"W2543203081","doi":"10.1109/iceas.2011.6147149","title":"Particle Swarm Optimization based Local Linear Wavelet Neural Network for forecasting electricity prices","year":2011,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"Particle swarm optimization; Mean squared error; Artificial neural network; Mean absolute percentage error; Backpropagation; Wavelet; Computer science; Multilayer perceptron; Electricity; Perceptron; Artificial intelligence; Mathematical optimization; Algorithm; Mathematics; Statistics; Engineering","score_opus":0.03716221081466586,"score_gpt":0.20915456424397233,"score_spread":0.17199235342930647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2543203081","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":[0.06773395,0.00009040227,0.9255337,0.000010802674,0.00027141976,0.00015281743,0.000002952154,0.0004664377,0.0057375287],"genre_scores_gemma":[0.85391366,0.0000034878146,0.1456216,0.00009382023,0.00024388246,0.000017256374,0.000021569378,0.000038686907,0.000046026194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903405,0.0000121175735,0.0002428426,0.00015942841,0.00008569261,0.00046585017],"domain_scores_gemma":[0.99959236,0.00011569172,0.00003865075,0.00011309477,0.000052093063,0.00008810205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017208666,0.00015370513,0.00014153551,0.000031314077,0.00011464615,0.00002135336,0.00010164385,0.00007400397,0.00007558844],"category_scores_gemma":[0.000035356283,0.0001423744,0.000066008964,0.00027168973,0.00001848906,0.00015721645,0.000013807698,0.000097302545,0.0000034242535],"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":[0.000023993443,0.000015245809,0.00049402344,0.0000300838,0.00001392301,0.0000017353274,0.000075411386,0.9913166,0.000032796583,0.00035972882,0.00018281865,0.007453608],"study_design_scores_gemma":[0.00031507167,0.00009433726,0.000060325896,0.000016065485,0.000016775663,0.0000030350861,0.00001297457,0.98362887,0.0151989665,0.000050601466,0.00041670297,0.00018625964],"about_ca_topic_score_codex":0.000032184613,"about_ca_topic_score_gemma":0.00003023747,"teacher_disagreement_score":0.7861797,"about_ca_system_score_codex":0.000028258699,"about_ca_system_score_gemma":0.000012018709,"threshold_uncertainty_score":0.58058596},"labels":[],"label_agreement":null},{"id":"W2543726833","doi":"","title":"Advanced distribution automation (ADA) applications and power quality in Smart Grids","year":2010,"lang":"en","type":"article","venue":"China International Conference on Electricity Distribution","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Smart grid; Computer science; Renewable energy; Reliability (semiconductor); Automation; Grid; Distributed generation; Sustainability; Quality of service; Reliability engineering; Distributed computing; Systems engineering; Power (physics); Telecommunications; Electrical engineering; Engineering","score_opus":0.010398430145045571,"score_gpt":0.2605363207497796,"score_spread":0.25013789060473407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2543726833","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":[0.8964235,0.00002364059,0.094813064,0.00028284237,0.0004440508,0.0002088365,0.00040341326,0.00024595868,0.007154648],"genre_scores_gemma":[0.9967073,0.00006400872,0.000107107124,0.000017179023,0.00006823587,0.000094764284,0.0028987955,0.000011214616,0.00003135115],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","domain_scores_codex":[0.9988779,0.00003094212,0.0003341234,0.0002584135,0.00025806326,0.00024059322],"domain_scores_gemma":[0.99951005,0.000052501477,0.00008311821,0.00015435858,0.0001255324,0.00007446025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026471962,0.00017959111,0.00015191465,0.00008502084,0.00010594603,0.0000843393,0.00016923495,0.00014031956,0.00006780541],"category_scores_gemma":[0.00014292754,0.00019375862,0.00004031548,0.0003019824,0.000046614015,0.0003523942,0.000022477792,0.00048778934,0.000013167988],"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":[0.00006623236,0.0001887482,0.011397444,0.000035814304,0.00003107596,0.000001971881,0.000097831704,0.0020235952,0.056441292,0.8749765,0.00023421332,0.05450528],"study_design_scores_gemma":[0.0010310246,0.00011382203,0.72828,0.00007988534,0.000011430144,0.00001785143,0.00003767096,0.22940499,0.017881975,0.014207354,0.008333755,0.00060026604],"about_ca_topic_score_codex":0.000040881725,"about_ca_topic_score_gemma":0.0001496688,"teacher_disagreement_score":0.86076915,"about_ca_system_score_codex":0.00020993242,"about_ca_system_score_gemma":0.00003321815,"threshold_uncertainty_score":0.7901247},"labels":[],"label_agreement":null},{"id":"W2544551605","doi":"10.1109/epecs.2013.6713061","title":"Multi-Gene Genetic Programming for Short Term Load Forecasting","year":2013,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Waterloo","funders":"","keywords":"Genetic programming; Computer science; Term (time); Genetic algorithm; Scheduling (production processes); Artificial intelligence; Electric power system; Gene expression programming; Mathematical optimization; Machine learning; Power (physics); Mathematics","score_opus":0.03010095143649088,"score_gpt":0.22785287162871942,"score_spread":0.19775192019222854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2544551605","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":[0.747658,0.00047171072,0.24418123,0.00001440868,0.00060532615,0.0007367687,0.0000047228677,0.0009278587,0.005399974],"genre_scores_gemma":[0.711592,0.0000069703055,0.2873511,0.000019082257,0.00018846508,0.00025194147,0.000010791855,0.000055407523,0.00052426074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891657,0.0000048175125,0.00026891998,0.00019905894,0.00011262969,0.00049801916],"domain_scores_gemma":[0.9995649,0.00005525709,0.000016446627,0.00016920039,0.000076696575,0.00011749966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008449374,0.00019705476,0.00015995315,0.000050462397,0.000092188624,0.00009584289,0.00013902958,0.00008265229,0.0000850179],"category_scores_gemma":[0.00003228977,0.00017995414,0.00008809908,0.00010129478,0.00001839968,0.00015006826,0.00003076341,0.0000879169,0.000033038174],"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":[0.0000029579521,0.000038593495,0.008670909,0.00021432785,0.000088476416,0.0000068867225,0.00042832806,0.030776057,0.06329858,0.000037951704,0.0008006794,0.89563626],"study_design_scores_gemma":[0.00043344856,0.0000624591,0.0031649596,0.00006632115,0.000023866474,0.000039230727,0.00006391138,0.9571094,0.030477144,0.000037519618,0.008035601,0.00048614628],"about_ca_topic_score_codex":0.000047061734,"about_ca_topic_score_gemma":0.000065232074,"teacher_disagreement_score":0.9263333,"about_ca_system_score_codex":0.00005547481,"about_ca_system_score_gemma":0.000013655975,"threshold_uncertainty_score":0.73383164},"labels":[],"label_agreement":null},{"id":"W2545570492","doi":"10.1109/iceas.2011.6147110","title":"A hybrid approach for short term electricity price and load forecasting","year":2011,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"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":"Bidding; Electricity market; Computer science; Electricity price forecasting; Electricity; Electric power system; Electric power industry; Term (time); Artificial neural network; Electricity generation; Electrical load; Electric power; Econometrics; Operations research; Power (physics); Economics; Artificial intelligence; Engineering; Microeconomics; Electrical engineering","score_opus":0.037033615972867535,"score_gpt":0.20375105932931395,"score_spread":0.16671744335644642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2545570492","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":[0.37828973,0.00022299064,0.3577194,0.0000010708035,0.00009209562,0.00018562312,0.000004181129,0.00035141417,0.26313353],"genre_scores_gemma":[0.9492088,0.000013934572,0.0503628,0.000017486616,0.000087051565,0.000040129722,0.0000072228327,0.000031698786,0.0002309072],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992655,0.000004545909,0.0001572009,0.0001745236,0.000075976466,0.00032227818],"domain_scores_gemma":[0.9997317,0.000041564534,0.000013008706,0.00010353957,0.000031922416,0.00007825587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015881055,0.00014246999,0.0001387364,0.000047396585,0.00006873392,0.00002260568,0.00008393729,0.000042529384,0.000016728938],"category_scores_gemma":[0.00002872295,0.00012879634,0.000041114705,0.00007683522,0.000015139457,0.00011950148,0.000023884957,0.00009204205,8.448202e-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":[0.00026504294,0.00044984755,0.051862817,0.0027413806,0.0008259392,0.00006423776,0.007954547,0.011035665,0.03140164,0.017602123,0.0071886014,0.8686082],"study_design_scores_gemma":[0.00034719653,0.00010855841,0.0011622861,0.000026520036,0.0000337368,0.00013308936,0.000037788115,0.9456616,0.050080087,0.0003882488,0.0015640866,0.0004568075],"about_ca_topic_score_codex":0.000018391474,"about_ca_topic_score_gemma":0.00000464217,"teacher_disagreement_score":0.9346259,"about_ca_system_score_codex":0.000032244603,"about_ca_system_score_gemma":0.000010759639,"threshold_uncertainty_score":0.5252162},"labels":[],"label_agreement":null},{"id":"W2546717368","doi":"10.1109/ccece.2016.7726768","title":"A novel approach in household electricity consumption forecasting","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"École de Technologie Supérieure; Toronto Metropolitan University","funders":"","keywords":"Akaike information criterion; Consumption (sociology); Energy consumption; Computer science; Time series; Regression analysis; Econometrics; Electricity; Energy (signal processing); Model selection; Predictive modelling; Set (abstract data type); Statistics; Artificial intelligence; Economics; Machine learning; Mathematics; Engineering","score_opus":0.05299925538746456,"score_gpt":0.20750618654119868,"score_spread":0.15450693115373412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546717368","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":[0.7027577,0.00006859011,0.2680697,0.0000080018735,0.00009764939,0.00005611461,0.000003813787,0.00036800661,0.028570423],"genre_scores_gemma":[0.99272275,0.000024822677,0.0069594304,0.000018016677,0.000056969577,0.0000124898015,0.0000018788546,0.000024388526,0.00017925375],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993488,0.000006283774,0.00016645975,0.00012883692,0.00007532379,0.0002742571],"domain_scores_gemma":[0.9997676,0.00007412267,0.000015145663,0.000094397015,0.000007235835,0.000041512674],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001322213,0.000105363375,0.00010470511,0.00010936676,0.000021664986,0.000013218056,0.00007156176,0.000063668886,0.000023937804],"category_scores_gemma":[0.000021816644,0.0000743649,0.000027302583,0.00015789467,0.000012726366,0.00013367207,0.000014369181,0.000082621445,0.0000089372315],"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":[0.00003704898,0.00021774233,0.12495117,0.0002867369,0.000074742224,0.000017366227,0.0004831551,0.12870523,0.45218652,0.015697869,0.0012245221,0.2761179],"study_design_scores_gemma":[0.0030444942,0.000060146016,0.023167547,0.00035198685,0.000015418873,0.00017279544,0.000029183251,0.9089614,0.059823677,0.000349869,0.0030094576,0.0010139921],"about_ca_topic_score_codex":0.00002849636,"about_ca_topic_score_gemma":0.000046703597,"teacher_disagreement_score":0.7802562,"about_ca_system_score_codex":0.000063319836,"about_ca_system_score_gemma":0.000005328418,"threshold_uncertainty_score":0.30325127},"labels":[],"label_agreement":null},{"id":"W2546773963","doi":"","title":"Machine learning for wind power prediction","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Wind power; Machine learning; Artificial intelligence; Computer science; Support vector machine; Power (physics); Renewable energy; Deep learning; Wind speed; Empirical research; Engineering; Meteorology; Statistics; Mathematics; Geography","score_opus":0.008090316227456004,"score_gpt":0.18972607633592617,"score_spread":0.18163576010847016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546773963","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":[0.24938323,0.0004629739,0.4742585,0.0002128838,0.0018853056,0.00018685508,0.0000363385,0.0021152545,0.27145863],"genre_scores_gemma":[0.9933971,0.000019169414,0.0007667367,0.000012762971,0.00009991904,0.0000042144293,0.000005447214,0.000020491836,0.0056741643],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99968547,0.000003261377,0.000078161866,0.00006545063,0.00003922533,0.00012845699],"domain_scores_gemma":[0.9998511,0.00005021072,0.000007044053,0.00004983542,0.000011118767,0.000030672214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005731011,0.00005918461,0.000049469934,0.000027882541,0.000035128425,0.0000078774965,0.000031193184,0.00003442379,0.00027697367],"category_scores_gemma":[0.000023257751,0.000038116537,0.00002843669,0.000030180654,0.000005648493,0.00008284963,0.000006076242,0.000035248446,0.00002301556],"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":[0.00008467017,0.000050858234,0.080866404,0.00015869648,0.000298293,0.0000063027787,0.0009558562,0.21833278,0.26991862,0.027874736,0.016471291,0.38498148],"study_design_scores_gemma":[0.0011018006,0.0001674874,0.002481159,0.000096859694,0.000013202835,0.000010926122,0.000021229413,0.15133609,0.02752303,0.0005150264,0.8164501,0.00028307128],"about_ca_topic_score_codex":0.0000029105365,"about_ca_topic_score_gemma":0.000004754194,"teacher_disagreement_score":0.79997885,"about_ca_system_score_codex":0.000015001819,"about_ca_system_score_gemma":0.0000019398417,"threshold_uncertainty_score":0.30326673},"labels":[],"label_agreement":null},{"id":"W2546896941","doi":"10.1109/ccece.2016.7726626","title":"Effective input features selection for electricity price forecasting","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Feature selection; Computer science; Selection (genetic algorithm); Mean absolute percentage error; Preprocessor; Data pre-processing; Artificial intelligence; Feature (linguistics); Data mining; Filter (signal processing); Machine learning; Electricity; Pattern recognition (psychology); Artificial neural network; Engineering","score_opus":0.008776482630540169,"score_gpt":0.2061775400065583,"score_spread":0.19740105737601812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546896941","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":[0.34950712,0.00015684743,0.6099869,0.00004739072,0.00048188193,0.0004008539,0.0000058592436,0.0009596853,0.038453422],"genre_scores_gemma":[0.99388444,0.000010157251,0.004257283,0.000030277579,0.00032357252,0.000089721754,0.0000021851397,0.000033876848,0.0013684815],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99933356,0.000012355637,0.000116326395,0.00015005427,0.00006972421,0.00031800682],"domain_scores_gemma":[0.9994054,0.00040664608,0.000022643548,0.00006242616,0.00005311496,0.000049767077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013647249,0.00013301929,0.000116540534,0.000080376296,0.000089229,0.000020907508,0.000062895815,0.0000811003,0.000023581051],"category_scores_gemma":[0.00014936905,0.0000892263,0.000059357502,0.00020466396,0.000008784008,0.00014680275,0.000010286208,0.00007480633,0.0000066306416],"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":[0.000081646416,0.000028860779,0.0040107463,0.00019413841,0.00017295072,0.0000013453566,0.00029052427,0.010377459,0.2800113,0.007521905,0.010279324,0.6870298],"study_design_scores_gemma":[0.0012267595,0.00034029127,0.003964166,0.00017560409,0.000033112818,0.000064639935,0.00001506133,0.12185459,0.8517869,0.0016360211,0.01830471,0.000598149],"about_ca_topic_score_codex":0.000012779666,"about_ca_topic_score_gemma":0.000041947907,"teacher_disagreement_score":0.68643165,"about_ca_system_score_codex":0.00009878253,"about_ca_system_score_gemma":0.000007973537,"threshold_uncertainty_score":0.36385426},"labels":[],"label_agreement":null},{"id":"W2548151574","doi":"10.1109/ccece.2016.7726765","title":"Mid-term electricity price forecasting using SVM","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Electricity market; Electricity price forecasting; Electricity; Purchasing; Term (time); Computer science; Time horizon; Support vector machine; Electricity price; Scheduling (production processes); Medium term; Operations research; Economics; Econometrics; Artificial intelligence; Engineering; Finance; Operations management","score_opus":0.028639339229052582,"score_gpt":0.2173522173576202,"score_spread":0.18871287812856763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2548151574","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":[0.825644,0.00012030783,0.11722995,0.000014060761,0.0003553294,0.000046690056,0.0000020660523,0.0005021876,0.056085356],"genre_scores_gemma":[0.9943489,0.000017264587,0.0047452752,0.00002586132,0.00022115701,0.0000026682944,8.9217326e-7,0.000037766153,0.00060025067],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999134,0.000010539395,0.00019205186,0.00014892132,0.000108639004,0.00040584907],"domain_scores_gemma":[0.99961555,0.00010692659,0.00002692376,0.00014134253,0.00002688042,0.00008234838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000110804205,0.00014810827,0.00012956167,0.00008351441,0.000073700816,0.000026317863,0.00011423114,0.00006642052,0.00019371626],"category_scores_gemma":[0.000046594163,0.00010246636,0.000053669773,0.00021583392,0.000014722206,0.00020234157,0.000028655331,0.00007562051,0.000026822745],"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":[0.000012177989,0.000025647036,0.013393554,0.00010569014,0.00010091366,0.000039624392,0.00023154968,0.012896088,0.77590173,0.0014916365,0.0006707031,0.1951307],"study_design_scores_gemma":[0.0010228881,0.00006711621,0.0014271863,0.0004194908,0.000039817503,0.00020253405,0.000021529126,0.34515205,0.64112496,0.0005817118,0.008908815,0.0010319309],"about_ca_topic_score_codex":0.000016329086,"about_ca_topic_score_gemma":0.000012526863,"teacher_disagreement_score":0.33225596,"about_ca_system_score_codex":0.000089036905,"about_ca_system_score_gemma":0.000012338088,"threshold_uncertainty_score":0.41784567},"labels":[],"label_agreement":null},{"id":"W2552022494","doi":"10.1109/pesgm.2016.7741767","title":"A methodology for ensemble wind power scenarios generation from numerical weather predictions","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Polytechnique Montréal","funders":"","keywords":"Wind power; Intermittency; Downscaling; Wind speed; Turbine; Probabilistic logic; Computer science; Wind power forecasting; Meteorology; Environmental science; Electricity generation; Probabilistic forecasting; Electric power system; Power (physics); Reliability engineering; Engineering; Aerospace engineering; Electrical engineering; Artificial intelligence","score_opus":0.050739447072304336,"score_gpt":0.25991196954217893,"score_spread":0.2091725224698746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552022494","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":[0.07323732,0.00008110488,0.91832244,0.00022265699,0.00082836114,0.00007977211,0.000028593957,0.0002596719,0.006940054],"genre_scores_gemma":[0.90839934,0.00000915625,0.08885809,0.00008505544,0.00045679955,0.000025459512,0.000015172,0.000031699772,0.002119201],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994512,0.000028387984,0.00014208636,0.00014300275,0.00005109657,0.00018421118],"domain_scores_gemma":[0.9995306,0.00025258405,0.000013802335,0.00012566957,0.000023388786,0.000053930988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000984878,0.000096912925,0.00011618592,0.00003369357,0.000051247276,0.000012130667,0.000054019998,0.000094471856,0.00060580514],"category_scores_gemma":[0.000055905315,0.0000657025,0.000055568584,0.00004732113,0.000012052685,0.00009049297,0.000010313002,0.000041163516,0.000038851973],"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":[0.000029290632,0.000039755738,0.00090437586,0.00000582732,0.0002185717,0.0000023418063,0.0007307914,0.038939744,0.8754412,0.016459646,0.035429686,0.031798765],"study_design_scores_gemma":[0.0022455575,0.00029552457,0.0013508274,0.00007045751,0.000101253296,0.00002324778,0.000092652816,0.36348358,0.20827894,0.00501242,0.41826278,0.00078277447],"about_ca_topic_score_codex":0.0000302574,"about_ca_topic_score_gemma":0.000035364257,"teacher_disagreement_score":0.83516204,"about_ca_system_score_codex":0.000028742084,"about_ca_system_score_gemma":0.000008818165,"threshold_uncertainty_score":0.66331416},"labels":[],"label_agreement":null},{"id":"W2552583675","doi":"10.1111/rssa.12251","title":"Forecasting Daily Political Opinion Polls Using the Fractionally Cointegrated Vector Auto-Regressive Model","year":2016,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series A (Statistics in Society)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":25,"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":"Social Sciences and Humanities Research Council of Canada; Canada Research Chairs; Danmarks Grundforskningsfond; National Research Foundation","keywords":"Econometrics; Autoregressive model; Vector autoregression; Statistics; Economics; Mathematics","score_opus":0.024242944658253267,"score_gpt":0.25903472392434623,"score_spread":0.23479177926609296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2552583675","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":[0.011538011,0.0002746117,0.9839573,0.0010780387,0.0015203878,0.00015006073,0.0010437241,0.000046338617,0.00039153878],"genre_scores_gemma":[0.72262704,0.00016913775,0.2755198,0.0004197794,0.00076399377,0.00000767702,0.000012392264,0.000099461446,0.0003807032],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973342,0.00012505848,0.0009220764,0.0002001362,0.00068457914,0.00073397096],"domain_scores_gemma":[0.99711144,0.0017754437,0.0003539169,0.00021565161,0.00033098538,0.00021256808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005789914,0.0003416874,0.0004445787,0.000022556911,0.0004068698,0.00012163356,0.00046588582,0.00021753409,0.00008400813],"category_scores_gemma":[0.0007852634,0.00018119936,0.0003932924,0.00029009144,0.0005925958,0.0002275684,0.00012818996,0.00091474305,0.0000018127881],"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":[0.00019233851,0.0001791456,0.0022480076,0.000350684,0.0010072943,0.000058408066,0.0036037024,0.5734607,0.0031906175,0.31178224,0.09753989,0.0063870004],"study_design_scores_gemma":[0.00070389773,0.00007857822,0.0007150293,0.00049790594,0.000085170555,0.00010068654,0.000966815,0.97229004,0.0002519867,0.022336502,0.0016784852,0.0002949232],"about_ca_topic_score_codex":0.00004184068,"about_ca_topic_score_gemma":0.000026210364,"teacher_disagreement_score":0.7110891,"about_ca_system_score_codex":0.00075782684,"about_ca_system_score_gemma":0.00030618443,"threshold_uncertainty_score":0.73890954},"labels":[],"label_agreement":null},{"id":"W2557028472","doi":"10.1016/j.energy.2016.11.064","title":"Cascade-based short-term forecasting method of the electric demand of HVAC system","year":2016,"lang":"en","type":"article","venue":"Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Akershus Universitetssykehus; Concordia University","keywords":"Chiller; HVAC; Demand forecasting; Context (archaeology); Cooling load; Peak demand; Water cooling; Demand response; Cascade; Engineering; Electrical load; Automotive engineering; Simulation; Air conditioning; Operations research; Electricity; Voltage; Mechanical engineering; Electrical engineering","score_opus":0.014336612235361769,"score_gpt":0.21320579030071068,"score_spread":0.19886917806534893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557028472","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":[0.676305,0.0009917307,0.30871037,0.000017881099,0.0006666105,0.00006542641,0.000014152112,0.00018157155,0.013047266],"genre_scores_gemma":[0.99816114,0.000011883666,0.001523211,0.0000056887084,0.000102477665,0.000009650699,0.0000011004346,0.000034638942,0.00015023268],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99906915,0.0000675688,0.00033402973,0.00012602012,0.0001626262,0.00024058174],"domain_scores_gemma":[0.99930966,0.00026708035,0.00007916828,0.00025882438,0.000041806736,0.000043452925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022118592,0.00014114163,0.00023639794,0.00007614929,0.000039880655,0.000004833581,0.00021128402,0.00008448602,0.0000109398125],"category_scores_gemma":[0.000039036368,0.00008293431,0.00012320523,0.0002886594,0.000020522259,0.00004683401,0.000029081957,0.000058032627,4.408302e-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":[0.000024165256,0.000025166308,0.0047728466,0.00051993126,0.00016349151,0.000010653612,0.00015255607,0.15333392,0.64992946,0.012706505,0.0002082352,0.17815308],"study_design_scores_gemma":[0.00021283461,0.000029681729,0.00044161043,0.00047069901,0.000029685716,0.000024522818,0.000009136131,0.14862493,0.84929556,0.00004139112,0.00069022796,0.00012970765],"about_ca_topic_score_codex":0.00006567262,"about_ca_topic_score_gemma":0.00003363619,"teacher_disagreement_score":0.3218561,"about_ca_system_score_codex":0.00005226306,"about_ca_system_score_gemma":0.000025116853,"threshold_uncertainty_score":0.3381963},"labels":[],"label_agreement":null},{"id":"W2560125060","doi":"10.1109/cjece.2016.2586939","title":"Hourly Electricity Price Forecasting for the Next Month Using Multilayer Neural Network","year":2016,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Electricity price forecasting; Electricity; Network topology; Artificial neural network; Electricity market; Mean absolute percentage error; Computer science; Mean squared error; Profit (economics); Econometrics; Operations research; Environmental economics; Economics; Artificial intelligence; Engineering; Microeconomics; Statistics; Mathematics; Electrical engineering","score_opus":0.023412030405353026,"score_gpt":0.18420132147776,"score_spread":0.16078929107240697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560125060","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":[0.2709353,0.0053356793,0.7226766,0.000062264655,0.0008294659,0.00009546911,0.0000034497518,0.000035776487,0.000026039355],"genre_scores_gemma":[0.99022853,0.00005163935,0.008066229,0.00006759178,0.0015372813,0.0000026923403,3.1825022e-7,0.000037798465,0.000007891987],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988561,0.00001130622,0.0003109704,0.000106032385,0.00008887668,0.00062667555],"domain_scores_gemma":[0.9987916,0.00062233483,0.000064796586,0.00007910504,0.000080268466,0.00036189862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020100908,0.00018146347,0.00022251137,0.00014933546,0.00015994755,0.00010026906,0.00018769287,0.00007003218,0.000004069976],"category_scores_gemma":[0.00007738983,0.00011500307,0.00010540516,0.0003054366,0.00001653274,0.00020610385,0.0000105047675,0.00023902413,2.2854664e-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":[0.000008703063,0.0000022320517,0.0008495853,0.000019520992,0.0000858597,0.000030309444,0.000062514824,0.8884775,0.000843343,0.00036360318,0.0006274876,0.10862934],"study_design_scores_gemma":[0.000308757,0.00011227083,0.0011630015,0.00010802999,0.000028904056,0.00032865815,0.0000012765548,0.99127007,0.00020552962,0.00004077396,0.0062580905,0.00017464932],"about_ca_topic_score_codex":0.00013190266,"about_ca_topic_score_gemma":0.00015057166,"teacher_disagreement_score":0.71929324,"about_ca_system_score_codex":0.00011788897,"about_ca_system_score_gemma":0.000077645345,"threshold_uncertainty_score":0.46896893},"labels":[],"label_agreement":null},{"id":"W2560831972","doi":"10.1109/epec.2016.7771773","title":"On optimization of SVMs kernels and parameters for electricity price forecasting","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Support vector machine; Computer science; Kernel (algebra); Convergence (economics); Quadratic programming; Electricity; Electricity market; Machine learning; Electricity price forecasting; Regression; Artificial intelligence; Set (abstract data type); Mathematical optimization; Data mining; Engineering; Mathematics; Statistics; Economics","score_opus":0.01875445810611271,"score_gpt":0.2035722082969501,"score_spread":0.1848177501908374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2560831972","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":[0.32473207,0.000024462533,0.6698494,0.000012372074,0.000083202816,0.000078746954,0.0000035477951,0.00007864016,0.0051375995],"genre_scores_gemma":[0.9695358,0.00002236192,0.03025215,0.000015435664,0.000019391266,0.000009134808,0.0000015547257,0.000016346306,0.00012780796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958766,0.000004738141,0.00012822899,0.000088042194,0.000048232985,0.00014311091],"domain_scores_gemma":[0.9995078,0.0003514292,0.000028138145,0.00005840524,0.00002345627,0.000030757677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008528408,0.00007429347,0.00009192972,0.00004981788,0.000022539018,0.000007166211,0.000035080975,0.00003801479,0.000012692693],"category_scores_gemma":[0.000115178824,0.00005089568,0.000023789857,0.00007520883,0.000011145184,0.00007217921,0.000006425505,0.000022263193,4.389415e-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":[0.000028352335,0.000011404647,0.0005634534,0.000081554484,0.00003372313,3.469614e-7,0.0000824851,0.93398786,0.005866954,0.0049377466,0.00022099135,0.054185115],"study_design_scores_gemma":[0.00041274773,0.00013069718,0.000056383353,0.000091973634,0.000008067218,0.0000021010874,0.0000058312085,0.9231477,0.075213306,0.0006740592,0.00013323686,0.00012393692],"about_ca_topic_score_codex":0.000003416562,"about_ca_topic_score_gemma":0.000001429587,"teacher_disagreement_score":0.64480376,"about_ca_system_score_codex":0.000016617874,"about_ca_system_score_gemma":0.0000033090585,"threshold_uncertainty_score":0.20754656},"labels":[],"label_agreement":null},{"id":"W2570685097","doi":"10.1109/iecon.2016.7793935","title":"Estimation of temperature correlation with household electricity demand for forecasting application","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"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 du Québec à Trois-Rivières","funders":"","keywords":"Nonparametric statistics; Autoregressive model; Mean absolute percentage error; Kernel density estimation; Econometrics; Time series; Demand forecasting; Residual; Electricity; Computer science; Demand response; Statistics; Probability distribution; Kernel (algebra); Mean squared error; Mathematics; Engineering; Algorithm; Operations research","score_opus":0.010647579615625121,"score_gpt":0.1897611028322594,"score_spread":0.17911352321663426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2570685097","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":[0.362102,0.000022403097,0.6368234,0.00001352729,0.000027430735,0.0001272476,0.0000057426687,0.00012551204,0.0007527458],"genre_scores_gemma":[0.9888696,0.0000050094077,0.010912437,0.0000072623925,0.00004117909,0.000041587373,0.000016303295,0.000020505211,0.00008609725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995755,0.000004378838,0.00014532216,0.000091778085,0.00006743304,0.000115599636],"domain_scores_gemma":[0.9996833,0.00012577605,0.000046684363,0.000082645616,0.000033140972,0.000028423085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009375304,0.00008056061,0.00008749588,0.000051822437,0.000041418614,0.00000774857,0.000038041984,0.000061010258,0.0000031895797],"category_scores_gemma":[0.00003590845,0.000050547496,0.000020405361,0.00013354796,0.000010344785,0.00014374773,0.0000031292539,0.000033219418,5.822086e-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":[0.000030203062,0.000008862018,0.0014941738,0.000091421556,0.000020256399,1.2931201e-7,0.000059812486,0.8208121,0.043854073,0.0030985698,0.00022031918,0.1303101],"study_design_scores_gemma":[0.0004507264,0.00006745441,0.0005451868,0.00007507955,0.000015896181,0.0000071890518,0.0000047643844,0.88062775,0.117457695,0.00039572513,0.00023837184,0.00011418171],"about_ca_topic_score_codex":0.0000050365493,"about_ca_topic_score_gemma":0.00001944391,"teacher_disagreement_score":0.62676764,"about_ca_system_score_codex":0.000030058363,"about_ca_system_score_gemma":0.00000909914,"threshold_uncertainty_score":0.20612672},"labels":[],"label_agreement":null},{"id":"W2585140149","doi":"10.1109/icmla.2016.0135","title":"An Empirical Study on Machine Learning Models for Wind Power Predictions","year":2016,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Wind power; Computer science; Machine learning; Artificial intelligence; Empirical research; Support vector machine; Deep learning; Renewable energy; Power (physics); Wind speed; Wind power forecasting; Predictive modelling; Empirical modelling; Data modeling; Electric power system; Simulation; Engineering; Meteorology; Statistics; Mathematics","score_opus":0.03446853891311322,"score_gpt":0.27303174274164693,"score_spread":0.23856320382853372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2585140149","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":[0.69576865,0.000018512246,0.2781284,0.000069130176,0.00029576165,0.00017404689,0.000017026508,0.00071397854,0.024814518],"genre_scores_gemma":[0.9981609,0.0000022268682,0.00050957676,0.000028315537,0.00009591826,0.000017134827,0.000004932561,0.000035472658,0.001145534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994268,0.000017511788,0.00012772999,0.0001487019,0.00009014523,0.00018910582],"domain_scores_gemma":[0.9996545,0.00009628563,0.000009834487,0.00014073215,0.000019099487,0.00007957119],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000106250816,0.00010873504,0.00009554334,0.00006043499,0.00008890605,0.000018365537,0.00007416274,0.000044886896,0.00010458236],"category_scores_gemma":[0.000017063472,0.00006994916,0.000037494585,0.000058222668,0.000008058656,0.00016671351,0.0000096245585,0.00008776537,0.000013333208],"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":[0.000028343313,0.00020296469,0.018723559,0.0000048100283,0.00006723125,0.000002335773,0.0012117927,0.97272414,0.0014587943,0.00083376886,0.0008367429,0.00390553],"study_design_scores_gemma":[0.0010954721,0.0013168909,0.00347564,0.000030646876,0.000018718707,0.0000033185822,0.00025151527,0.9843834,0.00062968564,0.000390398,0.008152339,0.00025197692],"about_ca_topic_score_codex":0.0000056064346,"about_ca_topic_score_gemma":0.000020133117,"teacher_disagreement_score":0.30239224,"about_ca_system_score_codex":0.000025338897,"about_ca_system_score_gemma":0.0000047826247,"threshold_uncertainty_score":0.28524438},"labels":[],"label_agreement":null},{"id":"W2590483131","doi":"10.1049/iet-rpg.2017.0101","title":"Guest Editorial: Selected Papers from the Wind and Solar Integration Workshop 2015","year":2017,"lang":"en","type":"editorial","venue":"IET Renewable Power Generation","topic":"Energy Load and Power Forecasting","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":"Renewable energy; Wind power; Variable renewable energy; Solar power; Telecommunications; Grid; Computer science; Electric power system; Engineering; Power (physics); Operations research; Electrical engineering; Geography","score_opus":0.010309409940417817,"score_gpt":0.22828699726218094,"score_spread":0.21797758732176312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2590483131","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006800447,0.0024481593,0.000619776,0.00009091815,0.99287784,0.00021021841,0.00023745421,0.00026725818,0.0025683031],"genre_scores_gemma":[0.0056553306,0.0013068027,0.0002242788,0.000016522245,0.9847794,0.00003279529,0.0059826165,0.00013329464,0.0018689439],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9978625,0.00010557526,0.00043857796,0.0005228398,0.0006753223,0.00039514617],"domain_scores_gemma":[0.998156,0.00044670745,0.00021992686,0.00070105994,0.0003602977,0.00011597204],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00044232685,0.0005286997,0.00041632145,0.00007422635,0.00052776834,0.0009364742,0.00040104723,0.0013786141,0.00005053737],"category_scores_gemma":[0.0010013824,0.00042838976,0.00008296272,0.00014044266,0.0000697663,0.00040364868,0.00006841358,0.0008982145,0.00001562342],"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":[0.000014284286,0.000007638175,0.000009307719,0.000009734705,0.000105478015,0.0000020603234,0.00040971287,0.010463115,0.0115096485,0.000002403224,0.9769649,0.0005017151],"study_design_scores_gemma":[0.00036356883,0.00003873545,0.000009769503,0.00019650107,0.000102594626,8.182665e-7,0.00003669031,0.004312551,0.0042369543,0.000024249754,0.99019825,0.00047930278],"about_ca_topic_score_codex":0.0013683795,"about_ca_topic_score_gemma":0.0074859867,"teacher_disagreement_score":0.013233362,"about_ca_system_score_codex":0.000117058866,"about_ca_system_score_gemma":0.00020508673,"threshold_uncertainty_score":0.9999178},"labels":[],"label_agreement":null},{"id":"W2598409353","doi":"10.1016/j.epsr.2017.03.002","title":"Optimization of neural network parameters by Stochastic Fractal Search for dynamic state estimation under communication failure","year":2017,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Artificial neural network; Maxima and minima; Perceptron; Computer science; Backpropagation; Convergence (economics); Multilayer perceptron; Support vector machine; Artificial intelligence; Algorithm; Mathematics","score_opus":0.02750545566999878,"score_gpt":0.30698682956737716,"score_spread":0.27948137389737837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2598409353","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":[0.17644449,0.0018613399,0.82009554,0.00008901225,0.00028135823,0.0007420155,0.000027396356,0.0000967148,0.00036213093],"genre_scores_gemma":[0.99729896,0.000059955728,0.0021503833,0.000002265361,0.000015782529,0.00011872666,0.00011617652,0.000058412308,0.00017935161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980282,0.00021686085,0.00037589122,0.00021302288,0.00050912274,0.0006568913],"domain_scores_gemma":[0.99819463,0.0006187076,0.0001233981,0.0006618611,0.0003023074,0.000099100456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014348013,0.00015887231,0.0002487046,0.00022393247,0.00059023546,0.00028893788,0.00056941295,0.00012586452,0.000006055928],"category_scores_gemma":[0.0001776054,0.0001627027,0.00005637637,0.00034257464,0.00008397132,0.0003594539,0.00007119599,0.00045350214,0.0000044567064],"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":[0.000029829727,0.000015252068,0.000045604003,0.00009813336,0.000052655367,4.145695e-7,0.00015518187,0.9953158,0.0010887433,0.00015588102,0.0014840013,0.0015585023],"study_design_scores_gemma":[0.00033005007,0.00013594727,0.00012692633,0.0001346794,0.000007062247,0.000005382137,0.000056588284,0.99845594,0.0004464812,0.00009037903,0.000064400054,0.00014613722],"about_ca_topic_score_codex":0.00023690422,"about_ca_topic_score_gemma":0.000028776567,"teacher_disagreement_score":0.8208545,"about_ca_system_score_codex":0.00020540437,"about_ca_system_score_gemma":0.000058779435,"threshold_uncertainty_score":0.6634823},"labels":[],"label_agreement":null},{"id":"W2603763990","doi":"10.1109/tgrs.2017.2659538","title":"A Combined Prognostic Model Based on Machine Learning for Tidal Current Prediction","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":50,"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":"Support vector machine; Robustness (evolution); Computer science; Univariate; Wavelet transform; Wavelet; Machine learning; Algorithm; Artificial intelligence; Data mining; Multivariate statistics","score_opus":0.020272058795628878,"score_gpt":0.2395697800418151,"score_spread":0.2192977212461862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2603763990","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":[0.0722377,0.000017955073,0.9258011,0.00006395301,0.0011594463,0.0001496392,0.000016126893,0.00017944768,0.00037460876],"genre_scores_gemma":[0.9866869,0.00005516042,0.013041519,0.000018063502,0.00004931529,6.891938e-7,0.0000035679134,0.000021515058,0.00012326379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999218,0.0000126064215,0.00013732283,0.00022967326,0.00015131294,0.0002510804],"domain_scores_gemma":[0.9995657,0.00009684734,0.00004189834,0.00018083528,0.000031729352,0.00008294297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015742808,0.00014870791,0.00012142191,0.00011558293,0.0011474865,0.00012777542,0.00007709082,0.00005536957,9.3042286e-7],"category_scores_gemma":[0.000028359389,0.00013669806,0.000057698977,0.00006421111,0.00008359285,0.00014759245,0.0000010080718,0.00025855066,0.0000014602774],"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":[0.000020748705,0.000011315827,0.0000065568324,0.000034973702,0.0000033632246,8.100921e-7,0.00006725896,0.6084624,0.0011113596,0.0000035617961,0.0000020532275,0.39027557],"study_design_scores_gemma":[0.0004659717,0.00015387211,0.00012750433,0.00033140558,0.000023427814,0.0000039525635,0.000008458945,0.9949324,0.0036015122,0.00006815623,0.00014144757,0.00014188045],"about_ca_topic_score_codex":0.000029251973,"about_ca_topic_score_gemma":0.000038413724,"teacher_disagreement_score":0.9144492,"about_ca_system_score_codex":0.000030382285,"about_ca_system_score_gemma":0.00002582626,"threshold_uncertainty_score":0.88256544},"labels":[],"label_agreement":null},{"id":"W2607141557","doi":"10.1149/ma2009-01/3/144","title":"The Effect of Magnetohydrodynamics on Convective Transport to an Electrode","year":2009,"lang":"en","type":"article","venue":"ECS Meeting Abstracts","topic":"Energy Load and Power Forecasting","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 Victoria","funders":"","keywords":"Magnetohydrodynamics; Convection; Mechanics; Electrode; Physics; Plasma; Nuclear physics","score_opus":0.0038794799432328437,"score_gpt":0.20622820356159272,"score_spread":0.20234872361835987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607141557","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":[0.89522165,0.000061737635,0.000016227523,0.000041841668,0.00022227177,0.00010744008,0.000003202965,0.00016895375,0.10415667],"genre_scores_gemma":[0.99958897,0.00000764323,0.00016588616,0.00002895237,0.00011974589,0.000004690916,0.000007322335,0.000025682333,0.000051108454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99908483,0.00003038195,0.00024789915,0.00015119235,0.00016139526,0.000324297],"domain_scores_gemma":[0.99936736,0.0002647273,0.000046227593,0.00020178704,0.00002070413,0.0000991894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047834739,0.00016822026,0.00017068785,0.00005384565,0.000096003445,0.00001769628,0.0001608058,0.000065430584,0.0000019805912],"category_scores_gemma":[0.00008630905,0.00012878771,0.000054110908,0.0001454353,0.00001550389,0.000053586395,0.000002961617,0.00021282944,0.000009238332],"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":[0.000060223316,0.000010369105,0.00034822721,0.000015749712,0.000013760178,0.000007734821,0.00025977253,0.9031451,0.09120841,0.000037617967,0.000031960637,0.0048610824],"study_design_scores_gemma":[0.00037093845,0.002223154,0.033666156,0.00018727843,0.00002912038,0.000009048184,0.000018613866,0.03168979,0.9302569,0.00009929775,0.0011404094,0.00030930183],"about_ca_topic_score_codex":0.000026978241,"about_ca_topic_score_gemma":0.000054132433,"teacher_disagreement_score":0.8714553,"about_ca_system_score_codex":0.000037912712,"about_ca_system_score_gemma":0.000008063636,"threshold_uncertainty_score":0.52518106},"labels":[],"label_agreement":null},{"id":"W2621510229","doi":"","title":"Impact of wind forecast errors with optimal and suboptimal generation redispatch policies","year":2016,"lang":"en","type":"dissertation","venue":"TSpace (University of Toronto)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"University of Toronto","keywords":"Operations research; Economics; Environmental science; Computer science; Engineering","score_opus":0.010760509485764128,"score_gpt":0.2267992790000538,"score_spread":0.21603876951428969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621510229","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":[0.9644067,0.00058079476,0.0003303864,0.000009970864,0.00012391114,0.0000912186,0.00006641708,0.000048029236,0.03434252],"genre_scores_gemma":[0.99493927,0.00039752657,0.0016942917,3.9672446e-7,0.0000757139,1.5647514e-7,0.00018594331,0.00003402927,0.002672704],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9993473,0.0000131686265,0.00010243114,0.00017126447,0.0001590965,0.00020668724],"domain_scores_gemma":[0.99948984,0.000018209204,0.00015409353,0.00015895846,0.00009532958,0.000083584695],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000048430324,0.00022989043,0.0003393567,0.00007166082,0.00008083573,0.000008812263,0.00010630877,0.00020133451,0.00093723566],"category_scores_gemma":[0.000004118066,0.0002166356,0.00010817742,0.00004648239,0.000067332025,0.00036400146,0.000018483106,0.000091421694,8.856214e-7],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0041942755,0.00030803247,0.036392607,0.003364273,0.0056045586,0.00014006671,0.27394986,0.20222738,0.26544178,0.0013506579,0.004959334,0.20206718],"study_design_scores_gemma":[0.0029876896,0.0020582622,0.9209462,0.0015022187,0.00065881456,0.00004028869,0.021043133,0.04236114,0.0065384908,0.000005586925,0.000280904,0.0015772632],"about_ca_topic_score_codex":0.039292824,"about_ca_topic_score_gemma":0.052550443,"teacher_disagreement_score":0.8845536,"about_ca_system_score_codex":0.0001650267,"about_ca_system_score_gemma":0.00006052884,"threshold_uncertainty_score":0.99997604},"labels":[],"label_agreement":null},{"id":"W2621541245","doi":"10.5829/idosi.wasj.2013.22.11.2891","title":"Electricity load demand forecasting using exponential smoothing methods","year":2013,"lang":"en","type":"article","venue":"World Applied Sciences Journal","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University","funders":"","keywords":"Exponential smoothing; Mean absolute percentage error; Demand forecasting; Electricity demand; Econometrics; Smoothing; Electricity; Statistics; Exponential function; Mathematics; Mean squared error; Electricity generation; Operations research; Engineering","score_opus":0.04241544722307348,"score_gpt":0.28155677172071786,"score_spread":0.2391413244976444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2621541245","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":[0.61890334,0.0006758283,0.31859103,0.000032287353,0.0011854129,0.00012031732,3.650771e-7,0.00017291954,0.06031854],"genre_scores_gemma":[0.8131887,0.000011188977,0.18606001,0.00005868544,0.000573832,0.0000056508934,2.3878704e-7,0.000022748127,0.000078972735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99803436,0.000060408176,0.00045352968,0.00023606785,0.00047376152,0.00074189546],"domain_scores_gemma":[0.9992905,0.00018677815,0.00014409382,0.0001042851,0.00006263431,0.00021166568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018520922,0.00022618247,0.0002453173,0.0003367683,0.0009851136,0.00062900496,0.00040213592,0.000056209657,0.00029732063],"category_scores_gemma":[0.000050388913,0.00019344229,0.00009193805,0.001049628,0.0001284687,0.0005578231,0.00006325089,0.0005820506,0.000016137265],"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":[0.000006426418,0.00001777782,0.0006426557,0.000026768885,0.00004908663,0.000012171687,0.0009369596,0.56510603,0.29589015,0.0015270651,0.0007680544,0.13501684],"study_design_scores_gemma":[0.00031024442,0.00002486617,0.00011454681,0.00009057708,0.000025301779,0.00031835784,0.00016978875,0.94877666,0.043256324,0.0040102354,0.002478545,0.000424521],"about_ca_topic_score_codex":0.000030705618,"about_ca_topic_score_gemma":0.00002554906,"teacher_disagreement_score":0.38367066,"about_ca_system_score_codex":0.00017785758,"about_ca_system_score_gemma":0.000084688945,"threshold_uncertainty_score":0.7888348},"labels":[],"label_agreement":null},{"id":"W2625261653","doi":"10.1109/tia.2017.2716343","title":"Determine Q–V Characteristics of Grid-Connected Wind Farms for Voltage Control Using a Data-Driven Analytics Approach","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Industry Applications","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Wind power; Grid; Computer science; Controller (irrigation); Gaussian; Voltage; Curve fitting; Control theory (sociology); Data mining; Engineering; Control (management); Electrical engineering; Mathematics; Artificial intelligence","score_opus":0.06615796373336587,"score_gpt":0.2843109853691736,"score_spread":0.21815302163580774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2625261653","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":[0.07685365,0.000007865259,0.9183276,0.00001980516,0.00026369203,0.000511243,0.0035300285,0.000116736985,0.00036934853],"genre_scores_gemma":[0.9933971,0.000008689011,0.005852669,0.000015542211,0.00028201943,0.0001555061,0.00015372585,0.000050366278,0.000084377905],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893415,0.000009863045,0.00040362118,0.00028465322,0.00011721617,0.00025050624],"domain_scores_gemma":[0.9982407,0.0001245509,0.00019310413,0.0012390568,0.00009720173,0.000105394654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000109945424,0.00020594272,0.00030243775,0.00011648872,0.0004859123,0.0000731082,0.0006107711,0.0003174738,0.000018847382],"category_scores_gemma":[0.00001406875,0.00022364985,0.00008485288,0.00013714236,0.00010907136,0.00022137901,0.000003820457,0.00046773476,0.0000026529344],"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":[0.00005355404,0.0003419816,0.0007793917,0.00025341872,0.00046178748,0.0000015602816,0.000114777795,0.9430398,0.022142747,0.00018765485,0.00011508818,0.032508284],"study_design_scores_gemma":[0.000816621,0.000036312005,0.00069496344,0.000056799727,0.0003049329,0.000012256158,0.000043727083,0.98829436,0.00686457,0.000022642658,0.0025667583,0.00028603917],"about_ca_topic_score_codex":0.00002247779,"about_ca_topic_score_gemma":0.000014036322,"teacher_disagreement_score":0.9165435,"about_ca_system_score_codex":0.00004718706,"about_ca_system_score_gemma":0.00004593045,"threshold_uncertainty_score":0.91201764},"labels":[],"label_agreement":null},{"id":"W2736056558","doi":"","title":"Multi-objective Genetic Algorithm Optimization of a Neural Network for Estimating Wind Speed Prediction Intervals","year":2013,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"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":"Wind speed; Artificial neural network; Sorting; Genetic algorithm; Computer science; Algorithm; Wind direction; Meteorology; Artificial intelligence; Machine learning; Geography","score_opus":0.01531310572095142,"score_gpt":0.2224065669799941,"score_spread":0.20709346125904268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736056558","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.038956184,0.00071559515,0.9566162,0.000089032335,0.0012064229,0.0006748188,0.00014978569,0.00033509504,0.0012568475],"genre_scores_gemma":[0.14212005,0.00009859953,0.85644263,0.0000091330885,0.00012302866,0.00004662897,0.0006699121,0.00008157984,0.00040841533],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975321,0.00081128214,0.0006819026,0.0004363972,0.00020183636,0.00033646033],"domain_scores_gemma":[0.99682057,0.00070758193,0.00041511594,0.0007421927,0.0012103746,0.000104161416],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014115879,0.0003236979,0.00039776688,0.00013928606,0.0001645738,0.00016689298,0.0004821133,0.0002781752,0.000040393665],"category_scores_gemma":[0.0005382824,0.00036797993,0.00019640327,0.00023092235,0.00008731921,0.00014118817,0.00035181048,0.00039644918,0.0000025940556],"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":[0.000002748046,0.000057859565,0.0002484244,0.00023118727,0.000087103566,3.6509883e-7,0.002407528,0.95378625,0.00036694208,0.00009174993,0.00021197113,0.042507842],"study_design_scores_gemma":[0.00040841216,8.6421824e-7,0.000762338,0.0018929522,0.000059251994,0.000004689856,0.000039179315,0.99235636,0.003851907,0.00027009874,0.000092316855,0.00026162065],"about_ca_topic_score_codex":0.00032506156,"about_ca_topic_score_gemma":0.00007372702,"teacher_disagreement_score":0.10316386,"about_ca_system_score_codex":0.00010095004,"about_ca_system_score_gemma":0.000056227156,"threshold_uncertainty_score":0.9998772},"labels":[],"label_agreement":null},{"id":"W2740480589","doi":"10.15676/ijeei.2015.7.2.6","title":"Hybrid WT-PSO based Neural Networks for Single Step-Ahead Wind Power Prediction for Ontario Electricity Market","year":2015,"lang":"en","type":"article","venue":"International Journal on Electrical Engineering and Informatics","topic":"Energy Load and Power Forecasting","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":"Artificial neural network; Two step; Electricity market; Electricity; Wind power; Computer science; Power (physics); Engineering; Artificial intelligence; Electrical engineering; Mathematics; Physics; Applied mathematics","score_opus":0.010603381835934492,"score_gpt":0.19892674353586481,"score_spread":0.18832336169993033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2740480589","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":[0.117396004,0.00016981165,0.87769926,0.000068106085,0.002562315,0.00021472003,0.000036125588,0.00018519038,0.0016684618],"genre_scores_gemma":[0.99288315,0.000016057802,0.0061662146,0.00020119338,0.0005425253,0.0000135060145,0.000048143593,0.00003684859,0.00009235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872905,0.000007269002,0.0005076505,0.00007867708,0.00030989177,0.00036749066],"domain_scores_gemma":[0.9991403,0.00024128449,0.000093510826,0.00006747526,0.00023423292,0.00022314645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032775875,0.00020484127,0.00019388461,0.00025344692,0.00006532205,0.00019299038,0.00017121981,0.000092972296,0.00000857812],"category_scores_gemma":[0.00021704775,0.00019255526,0.000107142194,0.000096185264,0.000008730735,0.000337044,0.000011411135,0.00043887383,6.324505e-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":[0.00016434261,0.00003497613,0.00015457181,0.000015292278,0.00009399056,0.0000036763786,0.000094195326,0.9842883,0.000045189034,0.0001767119,0.0052561723,0.009672592],"study_design_scores_gemma":[0.0012699615,0.0006523293,0.0001618428,0.000054493645,0.000019456715,0.00018004226,0.0000051297293,0.9566399,0.0002759796,0.000045809178,0.040505175,0.00018987406],"about_ca_topic_score_codex":0.000010452876,"about_ca_topic_score_gemma":0.0000066439466,"teacher_disagreement_score":0.87548715,"about_ca_system_score_codex":0.00049464626,"about_ca_system_score_gemma":0.00005789264,"threshold_uncertainty_score":0.7852176},"labels":[],"label_agreement":null},{"id":"W2745162628","doi":"10.1142/s2345748117500105","title":"Co-movement and Forecasting Analysis of Major Real Estate Markets by Wavelet Coherence and Multiple Wavelet Coherence","year":2017,"lang":"en","type":"article","venue":"Chinese Journal of Urban and Environmental Studies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"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":"Wavelet; Coherence (philosophical gambling strategy); Real estate; Econometrics; Wavelet transform; Financial economics; China; Economics; Computer science; Geography; Statistics; Mathematics; Artificial intelligence; Finance","score_opus":0.014962014531216688,"score_gpt":0.22874951168163357,"score_spread":0.2137874971504169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2745162628","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":[0.9944292,0.0044421507,0.000026638945,0.00002143885,0.00007861268,0.000054979384,0.00011319436,0.0000060710527,0.00082775374],"genre_scores_gemma":[0.98620504,0.01324338,0.00041023537,0.000010533353,0.000038763083,0.0000016357194,0.000009520861,0.000013698131,0.0000671957],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9990676,0.000020947984,0.00041367632,0.0001421789,0.00017598581,0.00017958866],"domain_scores_gemma":[0.99927026,0.00016286805,0.00032982952,0.00011932039,0.000011298053,0.00010643942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025884184,0.00021826992,0.0005393074,0.00008235638,0.0002520686,0.000047573743,0.00010353294,0.00003858387,0.000011212091],"category_scores_gemma":[0.00006739665,0.00015934483,0.00006650658,0.00004393933,0.0003010631,0.00020406011,0.000101107544,0.00012892192,1.3145993e-7],"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":[0.00006666011,0.000038385428,0.9406091,0.00013066446,0.0017651131,0.000028083694,0.0022347174,0.00037844523,0.021197343,0.0000016188922,0.00042477998,0.033125076],"study_design_scores_gemma":[0.0014569949,0.00019752212,0.9715788,0.00017021628,0.0004419484,0.000029538147,0.0012628743,0.021512471,0.0026677824,0.000046190627,0.00031844614,0.0003172276],"about_ca_topic_score_codex":0.000058200294,"about_ca_topic_score_gemma":0.000071718474,"teacher_disagreement_score":0.03280785,"about_ca_system_score_codex":0.00003562053,"about_ca_system_score_gemma":0.000002353124,"threshold_uncertainty_score":0.64978933},"labels":[],"label_agreement":null},{"id":"W2750520037","doi":"10.1007/978-3-319-56994-9_47","title":"Pricing European Options Using a Novel Multi-objective Firefly Algorithm","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Profitability index; Firefly algorithm; Computer science; Pareto principle; Mathematical optimization; Firefly protocol; Multi-objective optimization; Computation; Valuation of options; Economics; Algorithm; Econometrics; Finance; Mathematics; Machine learning; Particle swarm optimization","score_opus":0.02931558620285641,"score_gpt":0.23433995836539068,"score_spread":0.20502437216253427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2750520037","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":[0.0000925275,0.019102067,0.9159705,0.0000024161866,0.002365426,0.00028795004,0.00003154789,0.0001734289,0.06197415],"genre_scores_gemma":[0.9589667,0.003417051,0.022494905,0.00005474453,0.0070605627,0.000023220922,0.00014513513,0.00067851663,0.0071591814],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986698,0.00002875468,0.00041659758,0.0003753046,0.00013196567,0.0003775799],"domain_scores_gemma":[0.9991531,0.00019077919,0.00018180524,0.000348685,0.000035742498,0.00008992813],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002967619,0.00047554667,0.00054911437,0.00015644894,0.00024544937,0.0002180111,0.00018210679,0.00044428336,0.000005805324],"category_scores_gemma":[0.000031328826,0.0004471547,0.00010120391,0.000030957755,0.0000579734,0.00008407684,0.00007651226,0.0008464611,0.000002418798],"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":[0.0000017154525,0.000004216934,0.000026693826,0.000105867315,0.00009330273,0.000042084892,0.0003325281,0.95137125,0.000039558116,0.00024957975,0.0000055782702,0.04772762],"study_design_scores_gemma":[0.00030274535,0.000017679598,0.000032603857,0.0025546763,0.00004723679,0.00011205191,0.000004460302,0.98611355,0.0000028933366,0.00007105545,0.010243162,0.0004978649],"about_ca_topic_score_codex":0.00018355301,"about_ca_topic_score_gemma":0.00020529625,"teacher_disagreement_score":0.95887417,"about_ca_system_score_codex":0.00012023109,"about_ca_system_score_gemma":0.000015548443,"threshold_uncertainty_score":0.999798},"labels":[],"label_agreement":null},{"id":"W2754989814","doi":"10.20944/preprints201709.0053.v1","title":"Spatial Dependence Modeling of Wind Resource under Uncertainty Using C-Vine Copulas and Its Impact on Solar-Wind Energy Co-Generation","year":2017,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wind power; Renewable energy; Environmental science; Meteorology; Solar power; Vine copula; Offshore wind power; Wind speed; Electricity generation; Computer science; Power (physics); Econometrics; Engineering; Mathematics; Geography; Electrical engineering; Physics","score_opus":0.12914493508544933,"score_gpt":0.33876253280741675,"score_spread":0.20961759772196742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2754989814","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":[0.9860962,0.00042240683,0.009809684,0.000014881602,0.0005595977,0.00019946581,0.000076498494,0.00013626766,0.002684984],"genre_scores_gemma":[0.9987045,0.00020121998,0.00011893766,0.000018920648,0.00058532914,0.0000071919944,0.00017011088,0.00010561367,0.00008819034],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99750733,0.00011628457,0.0006553774,0.00079970754,0.00044999592,0.00047128598],"domain_scores_gemma":[0.9982203,0.00007494917,0.00035159223,0.0010232192,0.00013568903,0.00019422252],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005705689,0.0005876906,0.00065780553,0.00022618832,0.0002451749,0.0000766696,0.00050742633,0.00056009344,0.00010843362],"category_scores_gemma":[0.00014180563,0.0005975073,0.00020032842,0.00006138829,0.000060102095,0.00017619778,0.00061668624,0.00080796593,0.000011368518],"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":[0.00006289925,0.000027113967,0.00889493,0.00015311387,0.00018114044,0.000008477123,0.00033022778,0.9614089,0.028125001,0.00007884214,0.0000044002813,0.00072493823],"study_design_scores_gemma":[0.00033114184,0.000026193335,0.003398946,0.0006293717,0.00007386652,0.000016356777,0.000020914698,0.9234746,0.07122186,0.00024466158,0.000054265656,0.00050781196],"about_ca_topic_score_codex":0.0024874525,"about_ca_topic_score_gemma":0.00023148792,"teacher_disagreement_score":0.043096855,"about_ca_system_score_codex":0.0002896296,"about_ca_system_score_gemma":0.00014646607,"threshold_uncertainty_score":0.9996476},"labels":[],"label_agreement":null},{"id":"W2764720463","doi":"10.1109/pes.2006.1709392","title":"Investigating distributed generation systems performance using Monte Carlo simulation","year":2006,"lang":"en","type":"article","venue":"2006 IEEE Power Engineering Society General Meeting","topic":"Energy Load and Power Forecasting","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","funders":"","keywords":"Monte Carlo method; Randomness; Computer science; Electric power system; Distributed generation; Mathematical optimization; Simulation; Power (physics); Algorithm; Mathematics; Statistics","score_opus":0.014687511780507017,"score_gpt":0.20524477074484182,"score_spread":0.1905572589643348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2764720463","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":[0.9082331,0.0010105743,0.08727157,0.0000047336357,0.002142948,0.00016598469,0.00004004745,0.0008998206,0.00023120474],"genre_scores_gemma":[0.9812522,0.000018252696,0.0164451,0.000017056209,0.0019540126,0.000023354245,0.00007786069,0.000139456,0.000072714785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788725,0.00003185766,0.0006627195,0.00034800396,0.00035565658,0.00071451627],"domain_scores_gemma":[0.9993286,0.000074901574,0.00012375871,0.00024362374,0.00011217462,0.000116929696],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041183762,0.00044488322,0.0003298928,0.00007853008,0.00029364874,0.00019587409,0.0001562044,0.00023674534,0.000002923229],"category_scores_gemma":[0.00003362133,0.0005104894,0.00018337893,0.00046631406,0.000028080432,0.0004134175,0.000028643064,0.00034201704,0.000002671789],"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":[3.9438e-7,0.0000057890893,0.001293793,0.0001126796,0.000035460056,0.0000011661092,0.0001564146,0.83237857,0.16539457,0.000022252862,0.0005343513,0.000064576794],"study_design_scores_gemma":[0.00023774829,0.000012737575,0.00025910573,0.00023066383,0.000030603635,0.000010376736,0.00003060797,0.98077357,0.016884483,7.9006884e-7,0.0009760709,0.00055323605],"about_ca_topic_score_codex":0.00031017966,"about_ca_topic_score_gemma":0.000007304124,"teacher_disagreement_score":0.1485101,"about_ca_system_score_codex":0.00034695223,"about_ca_system_score_gemma":0.000024279072,"threshold_uncertainty_score":0.9997347},"labels":[],"label_agreement":null},{"id":"W2765801076","doi":"10.1007/978-3-319-70096-0_91","title":"Ten-Quarter Projection for Spanish Central Government Debt via WASD Neuronet","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Projection (relational algebra); Debt; Government (linguistics); Government debt; Debt ratio; Debt crisis; Financial crisis; Debt-to-GDP ratio; Financial system; Economy; Economics; External debt; Finance; Computer science; Geography; Macroeconomics; Algorithm","score_opus":0.011099624211147251,"score_gpt":0.207008269204749,"score_spread":0.19590864499360175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765801076","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":[0.0003253876,0.0001246161,0.98636925,0.000062532614,0.004365381,0.00033676845,0.000019822686,0.00014057473,0.008255672],"genre_scores_gemma":[0.91980755,0.00006211834,0.07537779,0.0004088004,0.0030900864,0.000039698636,0.000026168553,0.00015025554,0.0010375058],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818176,0.000005340207,0.00025667434,0.0005548491,0.00043845855,0.0005629348],"domain_scores_gemma":[0.9991884,0.00010454626,0.0000975163,0.000474554,0.000039108894,0.000095833755],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017940796,0.00035755464,0.0002914358,0.00010004209,0.0002134217,0.00024188186,0.00060853676,0.00021086393,0.00001256094],"category_scores_gemma":[0.000025653888,0.00033296127,0.00010344012,0.0000415628,0.00015493015,0.00021765352,0.00011496683,0.00039284644,0.000005817581],"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":[0.000014183061,0.000014325006,0.00014588718,0.00015859763,0.00002583755,0.000028562137,0.0005210573,0.24275304,0.00093964144,0.0006621113,0.00021010755,0.7545266],"study_design_scores_gemma":[0.00028892182,0.00019677106,0.00027130998,0.0003376624,0.00002055151,0.000053970685,1.4993248e-7,0.97080237,0.0041235853,0.007113008,0.016167752,0.00062395266],"about_ca_topic_score_codex":0.000012693087,"about_ca_topic_score_gemma":0.00013875472,"teacher_disagreement_score":0.9194822,"about_ca_system_score_codex":0.00028287419,"about_ca_system_score_gemma":0.000055474684,"threshold_uncertainty_score":0.99991226},"labels":[],"label_agreement":null},{"id":"W2768390055","doi":"10.3390/en10111868","title":"An Integrated Modeling Approach for Forecasting Long-Term Energy Demand in Pakistan","year":2017,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":108,"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 Regina","funders":"","keywords":"Autoregressive integrated moving average; Demand forecasting; Fossil fuel; Economics; Renewable energy; Coal; Natural resource economics; Energy policy; Energy planning; Environmental economics; Time series; Engineering; Operations management; Computer science","score_opus":0.03449239613601321,"score_gpt":0.273608796486045,"score_spread":0.23911640035003182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768390055","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":[0.71192485,0.00044882798,0.28310418,0.0000033634778,0.0002633105,0.00005523308,0.000009555545,0.00021830066,0.003972374],"genre_scores_gemma":[0.9858301,0.000063215404,0.013469934,0.00000991778,0.00023934619,0.00007475583,0.00012209428,0.00007145764,0.00011917678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988818,0.000018482773,0.00030418133,0.0002697606,0.00010005194,0.00042568424],"domain_scores_gemma":[0.99934465,0.00004077996,0.000060820326,0.00043081466,0.000041921063,0.000081012666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021598199,0.00024133512,0.00025627005,0.00013113566,0.00029693567,0.0002687344,0.00039879297,0.00012754924,0.0000052442633],"category_scores_gemma":[0.000030973955,0.00023178739,0.00006926807,0.00006614007,0.000040523482,0.0004915609,0.000042791915,0.00012922382,2.860004e-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":[0.000016916503,0.000021159682,0.0030969873,0.00006479547,0.000020953796,0.000008395073,0.00029299554,0.9678965,0.0008797422,0.001538934,0.000014895015,0.026147692],"study_design_scores_gemma":[0.0004271978,0.000030462503,0.00033875016,0.00010783151,0.0000102138465,0.0000074303093,0.00013846389,0.99425083,0.004004565,0.00021225694,0.00017387647,0.00029811534],"about_ca_topic_score_codex":0.0002838786,"about_ca_topic_score_gemma":0.000829906,"teacher_disagreement_score":0.27390525,"about_ca_system_score_codex":0.000055061817,"about_ca_system_score_gemma":0.000017802011,"threshold_uncertainty_score":0.9452015},"labels":[],"label_agreement":null},{"id":"W2768891253","doi":"10.1109/tste.2017.2774195","title":"Direct Interval Forecast of Uncertain Wind Power Based on Recurrent Neural Networks","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Sustainable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":204,"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":"Wind power; Artificial neural network; Interval (graph theory); Computer science; Power (physics); Control theory (sociology); Engineering; Artificial intelligence; Electrical engineering; Mathematics; Control (management); Physics","score_opus":0.01212446752455444,"score_gpt":0.22558462243875108,"score_spread":0.21346015491419665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768891253","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":[0.08216647,0.00020972847,0.83295774,0.000146225,0.004777697,0.00023463718,0.000050019687,0.00048552128,0.07897194],"genre_scores_gemma":[0.9974693,0.00003110141,0.0000876008,0.00005827648,0.00009806328,0.000030885585,0.0000069203065,0.000072413175,0.0021454056],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984834,0.000052404106,0.00034640898,0.0002881701,0.00023321113,0.0005963718],"domain_scores_gemma":[0.9988185,0.00015632348,0.000111223155,0.000669353,0.00010455168,0.00014001929],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019228547,0.00031824014,0.00035804423,0.00024821778,0.00042095614,0.00010375714,0.00036228873,0.00015638146,0.00014514792],"category_scores_gemma":[0.000016932201,0.00031068988,0.00024168288,0.00017331316,0.000093689785,0.0002422407,0.000003518903,0.00031283675,0.0000017848524],"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":[0.00012670657,0.00009912829,0.00001530015,0.00005571484,0.000052904248,0.000047003814,0.000073675634,0.9699926,0.000058347345,0.00042945103,0.00026775713,0.028781418],"study_design_scores_gemma":[0.00069207314,0.00045452462,0.00006194471,0.00017186784,0.000035308654,0.000004776926,0.00017639673,0.9730222,0.016461289,0.000040351602,0.00850818,0.00037106534],"about_ca_topic_score_codex":0.000379137,"about_ca_topic_score_gemma":0.00013116882,"teacher_disagreement_score":0.9153029,"about_ca_system_score_codex":0.00017951365,"about_ca_system_score_gemma":0.000036514204,"threshold_uncertainty_score":0.9999345},"labels":[],"label_agreement":null},{"id":"W2770618637","doi":"10.3390/su9112104","title":"Short Term Wind Power Prediction Based on Improved Kriging Interpolation, Empirical Mode Decomposition, and Closed-Loop Forecasting Engine","year":2017,"lang":"en","type":"article","venue":"Sustainability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":53,"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":"Wind power; Kriging; Hilbert–Huang transform; Interpolation (computer graphics); Wind power forecasting; Artificial neural network; Feature selection; Mathematical optimization; Computer science; Power (physics); Electric power system; Engineering; Mathematics; Machine learning; Artificial intelligence; Statistics; Energy (signal processing)","score_opus":0.01276037352711002,"score_gpt":0.30401786876139353,"score_spread":0.2912574952342835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2770618637","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":[0.97716194,0.00002670193,0.019564006,0.00021333815,0.00038008267,0.00029922868,0.000023970299,0.0003033636,0.0020273915],"genre_scores_gemma":[0.9988393,0.0000018211833,0.00085043424,0.000031103762,0.00014781057,0.00002026348,0.000037594677,0.00004034755,0.000031333508],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987032,0.000038599665,0.00034503272,0.00036584047,0.00015046693,0.0003968681],"domain_scores_gemma":[0.9988916,0.00012460904,0.000059938546,0.0005497438,0.00023097129,0.00014315074],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00038090578,0.000251374,0.00021856927,0.00011662434,0.0005175733,0.00018991741,0.00016725936,0.00013797611,0.000023298577],"category_scores_gemma":[0.00048314524,0.00025204927,0.000075971075,0.000070902955,0.00010427192,0.00041828296,0.00006588956,0.00030088445,5.8531054e-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":[0.00017697975,0.00012772536,0.81136715,0.00049151026,0.00006147308,0.000024455141,0.0014320696,0.13427073,0.0014323132,0.00015696396,0.00019823006,0.050260413],"study_design_scores_gemma":[0.00035527392,0.00009736521,0.16953926,0.00007351348,0.00001767723,0.000006561403,0.00009905976,0.82816523,0.00067786867,0.0005893434,0.00016671704,0.00021214261],"about_ca_topic_score_codex":0.000036933627,"about_ca_topic_score_gemma":0.000025852007,"teacher_disagreement_score":0.6938945,"about_ca_system_score_codex":0.00034216064,"about_ca_system_score_gemma":0.00006306819,"threshold_uncertainty_score":0.99999315},"labels":[],"label_agreement":null},{"id":"W2772543410","doi":"10.3390/en10122148","title":"Estimation of Conservation Voltage Reduction Factors Using Measurement Data of KEPCO System","year":2017,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"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; Korea Electric Power Corporation; U.S. Department of Energy","keywords":"AC power; Power factor; Voltage; Electric power system; Transformer; Power (physics); Standard deviation; Environmental science; Computer science; Statistics; Control theory (sociology); Engineering; Mathematics; Electrical engineering; Physics","score_opus":0.11497206632814855,"score_gpt":0.2712054122916212,"score_spread":0.15623334596347266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772543410","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":[0.9868472,0.00015848219,0.011387306,0.0000031686625,0.00066483475,0.00003578529,0.00001882284,0.00007119218,0.00081318326],"genre_scores_gemma":[0.9980917,0.000008650272,0.0017948471,2.5433835e-7,0.00004729916,9.888563e-7,0.000034619767,0.0000121316525,0.000009538147],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942243,0.00001005635,0.0002223336,0.000087083994,0.00018345479,0.00007466097],"domain_scores_gemma":[0.99924916,0.0000119669,0.00016933682,0.0004923988,0.00006261788,0.000014511257],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002219643,0.00007656279,0.00013217705,0.00005215018,0.000089449924,0.000023779241,0.00019778422,0.00003918532,0.0000022962308],"category_scores_gemma":[0.00008992075,0.00007421888,0.000019759325,0.00003392354,0.000035897945,0.00039056962,0.00004791574,0.000032965338,2.594893e-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":[0.000003131787,0.000005058947,0.0012696744,0.00030097069,0.00003671265,2.4617023e-7,0.0002504476,0.85353017,0.14089386,0.00095874985,0.00007337428,0.002677586],"study_design_scores_gemma":[0.00009719523,0.000008447361,0.0032730997,0.00035638627,0.000028653827,0.0000018374742,0.0003080876,0.6618434,0.33388624,0.000016714423,0.00010278686,0.000077108896],"about_ca_topic_score_codex":0.0006357036,"about_ca_topic_score_gemma":0.000030146006,"teacher_disagreement_score":0.19299237,"about_ca_system_score_codex":0.000049374547,"about_ca_system_score_gemma":0.00001705732,"threshold_uncertainty_score":0.3026558},"labels":[],"label_agreement":null},{"id":"W2773038010","doi":"10.1109/icrera.2017.8191206","title":"Solar irradiance forecasting using deep recurrent neural networks","year":2017,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":66,"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":"Artificial neural network; Solar irradiance; Computer science; Irradiance; Artificial intelligence; Recurrent neural network; Autoregressive model; Process (computing); Machine learning; Scalability; Data modeling; Deep learning; Data mining; Meteorology","score_opus":0.043749531547829915,"score_gpt":0.24290139623396453,"score_spread":0.1991518646861346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2773038010","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":[0.68735504,0.0015826679,0.24677728,0.000044072305,0.0057750926,0.00014321982,0.000003157609,0.00066409213,0.05765535],"genre_scores_gemma":[0.9937293,0.00003033887,0.005466005,0.000030165133,0.0006202262,0.0000034369946,0.0000035452674,0.000043894117,0.00007309786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909264,0.000009555994,0.00019489511,0.00016819511,0.00009063628,0.00044409354],"domain_scores_gemma":[0.9994025,0.000032670032,0.00006252738,0.0003663291,0.000021999294,0.000113943635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011461528,0.0001767634,0.00015734372,0.000036844085,0.00049279,0.00021619778,0.00028764113,0.00007960166,0.000054409367],"category_scores_gemma":[0.000047129502,0.00017314903,0.00006898812,0.000045127494,0.000037307273,0.00036112097,0.00006743163,0.00023071458,0.0000055067503],"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":[0.0000021036567,0.0000035829069,0.0035370092,0.00002045712,0.000016578937,0.000017716358,0.00007004351,0.8451897,0.00011604543,0.00015641988,0.000104993116,0.15076537],"study_design_scores_gemma":[0.00013138243,0.000010357046,0.00070040906,0.00006356208,0.000009380299,0.000030841005,0.00001207223,0.9974041,0.00020795087,0.000034170742,0.0011741102,0.00022163684],"about_ca_topic_score_codex":0.000048844027,"about_ca_topic_score_gemma":0.00013778915,"teacher_disagreement_score":0.30637422,"about_ca_system_score_codex":0.000041054514,"about_ca_system_score_gemma":0.0000047865296,"threshold_uncertainty_score":0.7060813},"labels":[],"label_agreement":null},{"id":"W2775854174","doi":"10.1007/978-3-319-71273-4_4","title":"Boosting Based Multiple Kernel Learning and Transfer Regression for Electricity Load Forecasting","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Boosting (machine learning); Smart grid; Electricity; Kernel (algebra); Machine learning; Scheduling (production processes); Ensemble learning; Multiple kernel learning; Probabilistic forecasting; Computation; Artificial intelligence; Demand response; Support vector machine; Gradient boosting; Mathematical optimization; Kernel method; Random forest; Engineering; Algorithm","score_opus":0.02242800543985167,"score_gpt":0.23150138896025196,"score_spread":0.20907338352040028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775854174","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":[0.0037568328,0.0009575432,0.99056304,0.000028078295,0.0007080535,0.00027018864,0.0000068908957,0.00020796906,0.0035013873],"genre_scores_gemma":[0.93190634,0.000041145726,0.067171894,0.00007253416,0.00044503566,0.000011778654,0.000011745499,0.00008819543,0.00025132738],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99800843,0.000011490388,0.0003329022,0.0006770643,0.0003691182,0.00060100615],"domain_scores_gemma":[0.9983987,0.0009759585,0.00009719153,0.00028532703,0.0001260461,0.00011674242],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00066176144,0.00045042034,0.00042737016,0.00028268303,0.00059689087,0.00026003743,0.00044944225,0.00030669454,0.000005529483],"category_scores_gemma":[0.0004274197,0.00041234907,0.00010137269,0.000082266815,0.00021533182,0.00020712447,0.000099629666,0.00081737246,9.66707e-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":[0.000015350784,0.000003585159,0.000450566,0.0001906793,0.00000883061,0.00001775694,0.00033565448,0.35556418,0.0014238509,0.00005716547,0.0000033861538,0.64192903],"study_design_scores_gemma":[0.00044407876,0.00009059061,0.000026322066,0.0015494162,0.000012591601,0.000022214097,1.4886865e-7,0.9867271,0.0066189617,0.0012946983,0.00273235,0.00048149802],"about_ca_topic_score_codex":0.000017716799,"about_ca_topic_score_gemma":0.00012546113,"teacher_disagreement_score":0.9281495,"about_ca_system_score_codex":0.0001855419,"about_ca_system_score_gemma":0.00014025517,"threshold_uncertainty_score":0.9998328},"labels":[],"label_agreement":null},{"id":"W2785711227","doi":"10.1109/epec.2017.8286163","title":"A spatiotemporal wind power prediction based on wavelet decomposition, feature selection, and localized prediction","year":2017,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; SaskPower","keywords":"Wind power; Superposition principle; Wavelet; Wavelet transform; Feature selection; Computer science; Computation; Selection (genetic algorithm); Component (thermodynamics); Feature (linguistics); Power (physics); Algorithm; Data mining; Pattern recognition (psychology); Artificial intelligence; Mathematics; Engineering","score_opus":0.006373048596128853,"score_gpt":0.21445256901948362,"score_spread":0.20807952042335476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2785711227","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":[0.8481717,0.000078126155,0.08636257,0.00078165735,0.0020071599,0.00038266898,0.00011967874,0.0014670962,0.060629338],"genre_scores_gemma":[0.99693197,0.000010657993,0.0023147664,0.00008170011,0.0002072569,0.000008119942,0.00008263411,0.000030204945,0.00033267686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928504,0.00001882706,0.00015440937,0.00020489386,0.00015570494,0.00018109864],"domain_scores_gemma":[0.99956113,0.000022826287,0.000049942188,0.00022131838,0.000054964523,0.000089838075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011849883,0.00016712699,0.0001268758,0.00009556248,0.00042468015,0.00015737515,0.00007390085,0.00016045004,0.00010362239],"category_scores_gemma":[0.000027504648,0.00015681358,0.000038005855,0.000059611648,0.000030713545,0.0002941635,0.000012282372,0.00019065631,0.0000067188857],"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":[0.00058087765,0.00027881688,0.28274602,0.00030932302,0.00035315886,0.000029465533,0.00075546035,0.59143573,0.015382993,0.0027239851,0.08016898,0.025235219],"study_design_scores_gemma":[0.0010475946,0.00019564413,0.09920164,0.00011252542,0.000021393575,0.0000202368,0.0000095935875,0.8806178,0.006109532,0.0000970243,0.012379559,0.00018742509],"about_ca_topic_score_codex":0.000041690742,"about_ca_topic_score_gemma":0.00007691835,"teacher_disagreement_score":0.2891821,"about_ca_system_score_codex":0.00006337708,"about_ca_system_score_gemma":0.000014505227,"threshold_uncertainty_score":0.63946724},"labels":[],"label_agreement":null},{"id":"W2786165902","doi":"10.1109/pesgm.2017.8274667","title":"A novel decomposition-based localized short-term tidal current speed and direction prediction model","year":2017,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Saskatchewan","funders":"","keywords":"Predictability; Autoregressive integrated moving average; Hilbert–Huang transform; Time series; Computer science; Term (time); Autoregressive model; Long-term prediction; Tidal power; Series (stratigraphy); Volatility (finance); Current (fluid); Energy (signal processing); Algorithm; Mathematics; Engineering; Geology; Statistics; Machine learning; Econometrics","score_opus":0.024181512923267784,"score_gpt":0.26764631046767295,"score_spread":0.24346479754440517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786165902","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":[0.5119364,0.00009333653,0.47693834,0.000021336471,0.0007141734,0.0000822721,0.000022724125,0.00038012967,0.009811242],"genre_scores_gemma":[0.99715346,0.000028638357,0.0025594933,0.0000073992924,0.00012890763,0.0000069091907,0.00003374714,0.0000195326,0.00006189317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994861,0.0000038379208,0.00013816849,0.00013886864,0.00009063949,0.00014241226],"domain_scores_gemma":[0.9996966,0.0000141703795,0.000021047927,0.00017356635,0.000022171138,0.00007245981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006140789,0.00011765695,0.00010304741,0.000050839488,0.00021204803,0.00010741849,0.0000660631,0.00005334684,0.000014506653],"category_scores_gemma":[0.0000078405665,0.00011345998,0.00003596966,0.000020783935,0.000028004742,0.00018925952,0.0000176291,0.000094229785,0.0000024883047],"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":[0.00003210537,0.00006604853,0.010460189,0.00009798295,0.000034449586,0.0000013844054,0.00008576801,0.87160176,0.070338555,0.0003902112,0.00028699927,0.046604536],"study_design_scores_gemma":[0.0004406782,0.000014402121,0.0054208147,0.000073386036,0.00001700252,0.0000050001004,0.000001688228,0.9856741,0.007922063,0.000031477794,0.00027726972,0.00012213713],"about_ca_topic_score_codex":0.000026124679,"about_ca_topic_score_gemma":0.000050778715,"teacher_disagreement_score":0.48521703,"about_ca_system_score_codex":0.00003635268,"about_ca_system_score_gemma":0.000009207534,"threshold_uncertainty_score":0.46267638},"labels":[],"label_agreement":null},{"id":"W2786670643","doi":"10.1109/icemis.2017.8272993","title":"Probabilistic day-ahead load forecast using quantile regression forests","year":2017,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"Quantile regression; Probabilistic logic; Quantile; Probabilistic forecasting; Prediction interval; Computer science; Random forest; Econometrics; Statistics; Regression; Machine learning; Artificial intelligence; Mathematics","score_opus":0.04156749920661118,"score_gpt":0.2775417557559345,"score_spread":0.2359742565493233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786670643","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":[0.91564274,0.00023659455,0.008403384,0.000026697888,0.0012894749,0.0001438356,0.000006725306,0.0004101746,0.07384039],"genre_scores_gemma":[0.99606913,0.00001058973,0.0028326572,0.000009751109,0.00023416031,0.0000075939747,0.000005749985,0.000049933864,0.0007804414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989181,0.000013563023,0.00022555818,0.00021127591,0.00020170173,0.00042975537],"domain_scores_gemma":[0.9990941,0.000045591885,0.00007070841,0.0006141269,0.000052599917,0.00012285386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024101368,0.00021014114,0.00020141045,0.000046271947,0.00041879006,0.00018007368,0.00031810935,0.00010840259,0.00011965529],"category_scores_gemma":[0.00018368522,0.00016888847,0.00007426805,0.000046922,0.00006663668,0.00035908906,0.00009582352,0.00014821651,0.00003894992],"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":[0.00010707199,0.00022587054,0.07505443,0.0014671837,0.00028601347,0.00032727266,0.0025815072,0.532262,0.039619435,0.03815291,0.017087836,0.2928284],"study_design_scores_gemma":[0.00042738894,0.000036182028,0.0065029846,0.00050093356,0.000024224408,0.00003100816,0.000026962647,0.9749997,0.007885475,0.0014549331,0.0076688183,0.0004413647],"about_ca_topic_score_codex":0.00016708268,"about_ca_topic_score_gemma":0.0011157752,"teacher_disagreement_score":0.44273767,"about_ca_system_score_codex":0.00010214142,"about_ca_system_score_gemma":0.000035529607,"threshold_uncertainty_score":0.6887072},"labels":[],"label_agreement":null},{"id":"W2787076137","doi":"","title":"Weather and Cardiovascular Diseases in Quebec Using Empirical Mode Decomposition","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Mode (computer interface); Hilbert–Huang transform; Climatology; Meteorology; Geography; Computer science; Geology; Telecommunications","score_opus":0.01247068365146716,"score_gpt":0.27064933450209827,"score_spread":0.25817865085063113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787076137","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":[0.94682676,0.00065132824,0.047847573,0.0000070589826,0.00006561929,0.000023668188,9.720001e-7,0.00009879861,0.0044782516],"genre_scores_gemma":[0.9982808,0.00001253947,0.001561841,0.000023315239,0.00006687675,0.0000019171976,0.0000028256577,0.00001391229,0.000035949102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996514,0.000019315943,0.000075887765,0.000088097164,0.000055622037,0.0001096793],"domain_scores_gemma":[0.9998413,0.000021695103,0.0000031806555,0.000086475746,0.000004333466,0.00004303996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004281462,0.000068448404,0.00011214409,0.000042996526,0.000019432438,0.000021246027,0.00002367158,0.000032649576,0.000013101309],"category_scores_gemma":[0.0000049311216,0.00006263478,0.000045561548,0.000051528932,0.000009805436,0.0000838423,0.000011111906,0.000040116534,0.00000191279],"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":[0.000002574356,0.000010791186,0.035501357,0.000032868094,0.000047812202,0.0000042286665,0.00020724251,0.9418779,0.0008101543,0.000360619,0.000061523286,0.021082923],"study_design_scores_gemma":[0.00017643056,0.0000048280963,0.005612159,0.00003365252,0.00001968906,0.0000049989185,0.000018072495,0.9913886,0.00049265067,0.00014311691,0.00198712,0.00011866968],"about_ca_topic_score_codex":0.0018792024,"about_ca_topic_score_gemma":0.0014470269,"teacher_disagreement_score":0.051454093,"about_ca_system_score_codex":0.000022434724,"about_ca_system_score_gemma":0.0000031914058,"threshold_uncertainty_score":0.28408045},"labels":[],"label_agreement":null},{"id":"W2787443788","doi":"10.1109/epec.2017.8286192","title":"An overview of forecasting techniques for load, wind and solar powers","year":2017,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Wind power; Solar wind; Computer science; Photovoltaic system; Meteorology; Environmental science; Electrical engineering; Engineering; Physics; Plasma","score_opus":0.054701844809313394,"score_gpt":0.288822325608389,"score_spread":0.2341204807990756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787443788","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":[0.85055363,0.003251156,0.03741981,0.00006900484,0.00055873406,0.00051222945,0.000048014317,0.00073666824,0.10685073],"genre_scores_gemma":[0.9782009,0.00014683757,0.021440826,0.00001591118,0.00007432496,0.0000053178214,0.0000030576055,0.000026448506,0.00008636511],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950707,0.000003968545,0.00014924214,0.000108921886,0.00006629883,0.0001645148],"domain_scores_gemma":[0.99956053,0.000035603152,0.000052352672,0.00025607797,0.000042006337,0.00005343173],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020810797,0.00010352747,0.00015331674,0.000030066396,0.00013012528,0.000058380523,0.00014790992,0.00006227549,0.000016092938],"category_scores_gemma":[0.000052482377,0.00009480331,0.000038739596,0.000016865644,0.00003862677,0.0002861961,0.00002770547,0.000050743773,3.020727e-7],"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":[0.000045723482,0.000057120604,0.0138943745,0.0019781683,0.0001740107,0.000009560247,0.0014841035,0.0023255353,0.060468614,0.013627809,0.0013397167,0.90459526],"study_design_scores_gemma":[0.0012752229,0.000697172,0.003698245,0.0012634334,0.00009893315,0.00005707171,0.00030351785,0.3706334,0.5449921,0.004807001,0.07100072,0.0011731914],"about_ca_topic_score_codex":0.000055067223,"about_ca_topic_score_gemma":0.0000505045,"teacher_disagreement_score":0.90342206,"about_ca_system_score_codex":0.000012653557,"about_ca_system_score_gemma":0.000009428128,"threshold_uncertainty_score":0.38659668},"labels":[],"label_agreement":null},{"id":"W2787603037","doi":"10.1109/pesgm.2017.8274271","title":"Reliable adaptive optimization demonstration using big data","year":2017,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Health Sciences North","funders":"New York State Energy Research and Development Authority","keywords":"Synchronizing; Computer science; Data set; Set (abstract data type); Data transmission; Optimization problem; Real-time computing; Transmission (telecommunications); Data mining; Artificial intelligence; Algorithm; Computer hardware","score_opus":0.10671693385843184,"score_gpt":0.26090891569905394,"score_spread":0.1541919818406221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787603037","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":[0.011384961,0.00005932718,0.8786824,0.000013742597,0.00078801025,0.000038793565,0.0000078799685,0.00017033149,0.10885458],"genre_scores_gemma":[0.8961377,0.000027945982,0.10333914,0.0000075895778,0.00023427664,7.479759e-7,0.000041369607,0.000015688356,0.00019554878],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996296,0.0000032301994,0.000092257695,0.00010799883,0.00005783854,0.00010904787],"domain_scores_gemma":[0.9994023,0.000008299735,0.000030466263,0.0005122532,0.000018419763,0.000028274657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007988802,0.00006517532,0.000057558023,0.000024188006,0.00021837733,0.000112630805,0.00023128107,0.000042798933,0.000030220726],"category_scores_gemma":[0.000025930221,0.00006395397,0.000009092701,0.000026236316,0.000016222875,0.00053666456,0.00006691468,0.000050655748,0.00000585091],"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":[0.0000012360603,0.0000021250305,0.00031976667,0.00000407914,0.00000853756,0.0000010785806,0.000013389895,0.9914163,0.0005146313,0.0003656933,0.0002845828,0.0070685926],"study_design_scores_gemma":[0.00007725896,0.000004812506,0.000084192485,0.000026346255,0.00000821748,0.000003138019,0.000014567219,0.9972944,0.0016015724,0.00003548835,0.0007644899,0.00008547448],"about_ca_topic_score_codex":0.00012663554,"about_ca_topic_score_gemma":0.0000753348,"teacher_disagreement_score":0.88475275,"about_ca_system_score_codex":0.000016222792,"about_ca_system_score_gemma":0.000012276259,"threshold_uncertainty_score":0.26079676},"labels":[],"label_agreement":null},{"id":"W2789056280","doi":"10.11591/ijeecs.v10.i2.pp748-755","title":"An Hour Ahead Electricity Price Forecasting with Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm","year":2018,"lang":"en","type":"article","venue":"Indonesian Journal of Electrical Engineering and Computer Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"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":"Universiti Teknikal Malaysia Melaka","keywords":"Bidding; Computer science; Electricity market; Foraging; Electricity; Support vector machine; Optimization algorithm; Electricity price forecasting; Mathematical optimization; Artificial intelligence; Machine learning; Economics; Engineering; Mathematics; Microeconomics","score_opus":0.006091968829835364,"score_gpt":0.18867098275342203,"score_spread":0.18257901392358666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789056280","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":[0.3621768,0.00011144099,0.63736385,0.000009127408,0.00022499631,0.000036923077,0.0000010054539,0.000059751572,0.000016087773],"genre_scores_gemma":[0.8940704,0.000016263853,0.105338335,0.000012236439,0.0005350881,0.0000010755882,0.0000011126842,0.000024711584,7.9435665e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867404,0.000016640442,0.00032215856,0.00022420919,0.0002984256,0.00046452796],"domain_scores_gemma":[0.99928075,0.00006131712,0.00010241894,0.00010290981,0.00018743193,0.000265161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004355539,0.00020956334,0.00025619572,0.00039089442,0.0001897373,0.00024760503,0.0002296958,0.000054426135,0.0000033453737],"category_scores_gemma":[0.000030499215,0.00017589075,0.000027360797,0.0009316873,0.000093095565,0.00072354084,0.00002997366,0.0002957265,1.7955144e-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":[0.00008311115,0.000049683076,0.0011081707,0.000058380545,0.000047664955,0.00010594608,0.00064571394,0.62409085,0.008250931,0.00013044152,0.000009238721,0.36541986],"study_design_scores_gemma":[0.00040818664,0.0012944855,0.0018244095,0.000108045264,0.000014566916,0.0017564994,0.0000034920117,0.9911479,0.0031653158,0.0000036296772,0.00004994887,0.00022355693],"about_ca_topic_score_codex":0.0000035418514,"about_ca_topic_score_gemma":6.590164e-7,"teacher_disagreement_score":0.5320255,"about_ca_system_score_codex":0.00006219202,"about_ca_system_score_gemma":0.000078278776,"threshold_uncertainty_score":0.7172617},"labels":[],"label_agreement":null},{"id":"W2792046648","doi":"10.1016/j.enbuild.2018.01.034","title":"Transfer learning with seasonal and trend adjustment for cross-building energy forecasting","year":2018,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":215,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transfer of learning; Time series; Energy (signal processing); Energy consumption; Feature (linguistics); Smart meter; Machine learning; Data mining; Electricity; Building automation; Artificial intelligence; Engineering; Statistics","score_opus":0.01035953064858992,"score_gpt":0.21568536509670583,"score_spread":0.20532583444811592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2792046648","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":[0.9011492,0.0018099952,0.09379099,0.000028451888,0.00031093138,0.00003774388,0.000009156465,0.0002443111,0.0026192067],"genre_scores_gemma":[0.9916377,0.00019298635,0.006457425,0.000107195134,0.00088060886,0.00003697448,0.000017674152,0.00007034844,0.0005990674],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988381,0.000013770546,0.00020371059,0.00034338783,0.00012877077,0.0004722565],"domain_scores_gemma":[0.9995651,0.00012913295,0.000027272696,0.00008143869,0.000042906682,0.00015414644],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014123294,0.00028101742,0.00023163877,0.00010427137,0.00039216474,0.00011431348,0.0000797842,0.0001185647,0.000023786535],"category_scores_gemma":[0.000014149748,0.00024541692,0.000048676902,0.00015178756,0.00015682497,0.00031576413,0.00003219748,0.00010639269,1.201725e-7],"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":[0.00044034605,0.000042529606,0.0049119154,0.00029902044,0.00042882792,0.000023429864,0.0016021881,0.079919,0.0386669,0.17740631,0.0006772785,0.6955823],"study_design_scores_gemma":[0.0027065077,0.0010842995,0.00063535984,0.00052992685,0.000122433,0.0002909694,0.00015485662,0.3727735,0.109055,0.0011226123,0.510366,0.0011585109],"about_ca_topic_score_codex":0.000053701697,"about_ca_topic_score_gemma":0.00015963259,"teacher_disagreement_score":0.69442374,"about_ca_system_score_codex":0.000025814883,"about_ca_system_score_gemma":0.000010489225,"threshold_uncertainty_score":0.9999998},"labels":[],"label_agreement":null},{"id":"W2793199562","doi":"10.30521/jes.338575","title":"An Experimental Fuzzy Inference System for Global Grid Electricity Peak Power Load Forecasting Third Core Module of First Console on G2P3S","year":2017,"lang":"en","type":"article","venue":"Journal of Energy Systems","topic":"Energy Load and Power Forecasting","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":"Goddard Space Flight Center; National Aeronautics and Space Administration","keywords":"Mean absolute percentage error; Adaptive neuro fuzzy inference system; Power (physics); Electric power system; Statistics; Meteorology; Computer science; Fuzzy logic; Mean squared error; Simulation; Mathematics; Geography; Fuzzy control system; Artificial intelligence","score_opus":0.032006299421851264,"score_gpt":0.26452150192498114,"score_spread":0.23251520250312988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793199562","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":[0.96571106,0.001366284,0.0054022903,0.000007838658,0.005075322,0.00012723054,0.00006946071,0.00007880961,0.022161681],"genre_scores_gemma":[0.9986572,0.000015555072,0.0003505441,0.000005928625,0.00086828356,0.000013374855,0.0000050128933,0.0000386386,0.000045448065],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980139,0.000040766638,0.0008878345,0.00019112816,0.000464482,0.00040185542],"domain_scores_gemma":[0.9978722,0.00014561843,0.0009540927,0.00043579447,0.0003884004,0.00020390938],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005094493,0.00029279778,0.00064503134,0.00009588997,0.0003161547,0.00016556973,0.00059614016,0.00017004495,0.0000022443405],"category_scores_gemma":[0.0001560285,0.0002539932,0.00022081326,0.00008761468,0.00005481985,0.00041668286,0.000037892118,0.00017411521,8.210304e-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":[0.0006602403,0.00038933617,0.015814247,0.0011561379,0.00065898546,0.00018439563,0.0008951677,0.91160434,0.023945741,0.04082009,0.0025802297,0.0012911143],"study_design_scores_gemma":[0.0051680743,0.0044823913,0.0017540534,0.0064982963,0.000159523,0.0014689306,0.0019532042,0.8524721,0.11376804,0.00018442821,0.01075132,0.0013396018],"about_ca_topic_score_codex":0.00028003776,"about_ca_topic_score_gemma":0.000115300536,"teacher_disagreement_score":0.0898223,"about_ca_system_score_codex":0.00045994032,"about_ca_system_score_gemma":0.00008931807,"threshold_uncertainty_score":0.99999124},"labels":[],"label_agreement":null},{"id":"W2793460685","doi":"10.4018/978-1-4666-5888-2.ch628","title":"Modeling and Forecasting Electricity Price Based on Multi Resolution Analysis and Dynamic Neural Networks","year":2014,"lang":"en","type":"book-chapter","venue":"Advances in information quality and management","topic":"Energy Load and Power Forecasting","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 du Québec à Montréal","funders":"","keywords":"Artificial neural network; Computer science; Electricity price forecasting; Electricity price; Electricity; Resolution (logic); Electricity market; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.01849173340785259,"score_gpt":0.24723023613433265,"score_spread":0.22873850272648005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793460685","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":[0.0013917036,0.00137261,0.9540211,0.000015087395,0.00010358584,0.0002200272,0.000008711443,0.00008960873,0.04277757],"genre_scores_gemma":[0.98098284,0.00959832,0.008214984,0.00030269657,0.00003339416,0.000029332072,0.00026879105,0.000023728802,0.00054591725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988562,0.000023096298,0.00058182067,0.00017615044,0.00016277433,0.00019996201],"domain_scores_gemma":[0.99954057,0.00008692419,0.00016133094,0.00013588402,0.000026705235,0.000048591417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055595126,0.00023239075,0.00030116428,0.0004867426,0.00010870432,0.00007094141,0.00005478396,0.000121458696,0.0000024501312],"category_scores_gemma":[0.000021031974,0.00024389758,0.000043378375,0.00012342539,0.00002792933,0.0005317892,0.000048798942,0.00023838601,3.772696e-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":[0.000017262219,0.0000016272435,0.000050137263,0.000491854,0.00003657258,4.4419704e-7,0.00006327463,0.8484125,5.7960168e-8,0.013305473,9.1530296e-7,0.1376199],"study_design_scores_gemma":[0.00035519063,0.000022838381,0.00031309942,0.00017544313,0.00007971477,5.9373355e-7,0.000020973075,0.9954325,2.2125508e-7,0.00038574045,0.0029497633,0.00026390626],"about_ca_topic_score_codex":0.000020923902,"about_ca_topic_score_gemma":0.0002468778,"teacher_disagreement_score":0.97959113,"about_ca_system_score_codex":0.00007175285,"about_ca_system_score_gemma":0.0000017332534,"threshold_uncertainty_score":0.99458545},"labels":[],"label_agreement":null},{"id":"W2794705868","doi":"","title":"Interannual Variability of Global Overturning Circulation Dominated by Pacific Variability","year":2017,"lang":"en","type":"article","venue":"21st Conference on Atmospheric and Oceanic Fluid Dynamics and the 19th Conference on Middle Atmosphere","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Climatology; Circulation (fluid dynamics); Shutdown of thermohaline circulation; Oceanography; Environmental science; Thermohaline circulation; Geology; North Atlantic Deep Water","score_opus":0.009642214456708487,"score_gpt":0.20844954282547073,"score_spread":0.19880732836876225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794705868","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":[0.88449454,0.00024413982,0.019480415,0.00028644275,0.0006042785,0.0004242674,0.00016331176,0.0001385464,0.09416405],"genre_scores_gemma":[0.9982073,0.00070731225,0.0006455976,0.000045539993,0.000041227227,0.000017360384,0.000036094945,0.000035148372,0.00026443473],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977257,0.00023000593,0.0006203248,0.0006315143,0.00031750902,0.00047493816],"domain_scores_gemma":[0.9980017,0.00035442776,0.00031071022,0.00092214515,0.00021358085,0.00019745386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00091921736,0.00053387193,0.0007161858,0.00000191829,0.00055031286,0.0003798169,0.000549428,0.00028402536,0.0002033104],"category_scores_gemma":[0.0003260799,0.00041048342,0.00012713077,0.00013745016,0.00077731593,0.00026891998,0.00016693972,0.00045740526,0.000005759941],"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":[0.0004716629,0.00012367645,0.02331517,0.00022805802,0.00019573212,0.0000041548865,0.00061366026,0.0023380967,0.00021622445,0.91537887,0.000061545354,0.05705312],"study_design_scores_gemma":[0.001937928,0.0001671756,0.014849345,0.00038910419,0.00006808577,0.000010212743,0.00062175636,0.96751547,0.00002699429,0.013869862,0.000091741575,0.00045231864],"about_ca_topic_score_codex":0.00022728852,"about_ca_topic_score_gemma":0.00008059003,"teacher_disagreement_score":0.96517736,"about_ca_system_score_codex":0.000157572,"about_ca_system_score_gemma":0.00009728853,"threshold_uncertainty_score":0.9998347},"labels":[],"label_agreement":null},{"id":"W2795160088","doi":"10.1049/pbpo130e_ch2","title":"Data-driven methods for prediction of small-to-medium wind turbines performance","year":2018,"lang":"en","type":"book-chapter","venue":"Institution of Engineering and Technology eBooks","topic":"Energy Load and Power Forecasting","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 Windsor","funders":"","keywords":"Wind power; Reliability (semiconductor); Profit margin; Environmental science; Renewable energy; Production (economics); Marine engineering; Profit (economics); Computer science; Engineering; Business; Economics; Power (physics); Microeconomics; Electrical engineering","score_opus":0.030383816193555728,"score_gpt":0.2446101826720096,"score_spread":0.21422636647845386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2795160088","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":[0.07533176,0.0032283626,0.7703122,0.00006339327,0.0070748925,0.001669428,0.0026474576,0.0030619204,0.13661058],"genre_scores_gemma":[0.50737005,0.00056911213,0.47194484,0.0000138151445,0.0011549938,0.000098416895,0.00063140265,0.00031212973,0.01790522],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99910694,0.0000019268334,0.00040051472,0.0002505131,0.000063336556,0.00017674467],"domain_scores_gemma":[0.99926883,0.000044429125,0.00009251769,0.00043133466,0.000116499716,0.00004637677],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016835984,0.0002557854,0.00039309543,0.00059789605,0.000041140232,0.000005464892,0.0002798182,0.0005223267,0.0000041100725],"category_scores_gemma":[0.000062945546,0.0002686693,0.000039612292,0.000046018253,0.00018489307,0.000047899845,0.00012476182,0.00023716522,0.0000010828321],"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":[0.00009859802,0.000031957912,0.00017825876,0.008244606,0.0017687409,0.000008033819,0.00055026804,0.2988771,0.115877464,0.22452989,0.0010621872,0.34877288],"study_design_scores_gemma":[0.0006340462,0.00054904865,0.000044906013,0.0027430528,0.00025087173,0.00006610539,0.000009252232,0.36264554,0.06996944,0.0011637497,0.5613165,0.00060751184],"about_ca_topic_score_codex":0.0000010477198,"about_ca_topic_score_gemma":0.0000029963096,"teacher_disagreement_score":0.5602543,"about_ca_system_score_codex":0.000025278012,"about_ca_system_score_gemma":0.00003471499,"threshold_uncertainty_score":0.9999766},"labels":[],"label_agreement":null},{"id":"W2797861675","doi":"10.7939/r36t0h190","title":"EM Algorithm for Electricity Pool Price Prediction and Errors-in-variables Process Identification","year":2016,"lang":"en","type":"article","venue":"University of Alberta Library","topic":"Energy Load and Power Forecasting","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":"Identification (biology); Process (computing); Algorithm; Computer science; Electricity; Econometrics; Mathematical optimization; Machine learning; Mathematics; Engineering","score_opus":0.004600025843404435,"score_gpt":0.16218105222995927,"score_spread":0.15758102638655483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2797861675","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":[0.94014394,0.0000826532,0.055666197,0.000082880586,0.00009645309,0.00014240983,0.000023041253,0.0000994587,0.003662968],"genre_scores_gemma":[0.99536645,0.00009604312,0.0025632358,0.0000049746395,0.000029225926,5.671675e-7,0.000025677844,0.000012239359,0.0019015695],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964637,0.000007951632,0.000083502375,0.00011133779,0.000043404747,0.00010741492],"domain_scores_gemma":[0.9997533,0.00010552127,0.00003283715,0.00006349451,0.000012525537,0.000032334312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034363875,0.00006096959,0.000079761616,0.00009492277,0.00003919656,0.0000082772995,0.00008136032,0.000057341622,0.000027399194],"category_scores_gemma":[0.000011666902,0.000060187132,0.000019042003,0.00013897894,0.000017140266,0.0007409976,0.000015359125,0.00003222689,0.0000011079416],"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":[0.0004696499,0.00045281317,0.12838112,0.0021422785,0.0005617366,0.000021353457,0.03483786,0.013639387,0.046296556,0.013109621,0.013040023,0.7470476],"study_design_scores_gemma":[0.0045076013,0.00027066938,0.07871476,0.0008716524,0.0001254956,0.000019797852,0.0021541116,0.75674355,0.12779453,0.004073361,0.023814915,0.00090956775],"about_ca_topic_score_codex":0.000041266005,"about_ca_topic_score_gemma":0.000030615705,"teacher_disagreement_score":0.74613804,"about_ca_system_score_codex":0.000014573427,"about_ca_system_score_gemma":0.000015008448,"threshold_uncertainty_score":0.245436},"labels":[],"label_agreement":null},{"id":"W2800492640","doi":"10.1109/tla.2018.8358661","title":"Estimation of residential natural gas consumption in Medellín-Antioquia","year":2018,"lang":"en","type":"article","venue":"IEEE Latin America Transactions","topic":"Energy Load and Power Forecasting","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":"Autoregressive integrated moving average; Gas consumption; Quarter (Canadian coin); Support vector machine; Natural gas; Consumption (sociology); Estimation; Statistics; Econometrics; Computer science; Time series; Engineering; Geography; Mathematics; Artificial intelligence; Petroleum engineering","score_opus":0.0132236395742077,"score_gpt":0.2453941101381197,"score_spread":0.23217047056391202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2800492640","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":[0.66431713,0.00006957051,0.3324957,0.00002468965,0.0009894394,0.000068063055,0.000014557642,0.00014856202,0.0018723209],"genre_scores_gemma":[0.99313784,0.000059220754,0.0066141533,0.000010043742,0.00007305953,0.0000092063165,0.000010756609,0.000018454732,0.00006726025],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993015,0.00002414773,0.00028305646,0.00010993225,0.00011726726,0.0001641028],"domain_scores_gemma":[0.9997221,0.00006239192,0.000045412573,0.00011371224,0.000024237539,0.00003209464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000053110645,0.00010139326,0.00014265132,0.00016194672,0.000057986912,0.000012366367,0.000063262996,0.0000514845,0.00027073844],"category_scores_gemma":[0.0000073025035,0.00011167417,0.00005192928,0.00032203773,0.0001287829,0.00016705175,0.0000011677054,0.00015318667,0.000039174516],"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":[0.000034655543,0.000043819575,0.00066983385,0.00008537504,0.000054264336,0.0000029731482,0.001625741,0.77130395,0.052209374,0.000058070207,0.00024009886,0.17367184],"study_design_scores_gemma":[0.00036766118,0.00005334516,0.0053737,0.000116817144,0.000032605385,0.000008831689,0.000054154778,0.9152388,0.07811468,0.00010151706,0.0003466188,0.00019126585],"about_ca_topic_score_codex":0.00014934292,"about_ca_topic_score_gemma":0.000300925,"teacher_disagreement_score":0.32882074,"about_ca_system_score_codex":0.000032234515,"about_ca_system_score_gemma":0.000010366161,"threshold_uncertainty_score":0.45539403},"labels":[],"label_agreement":null},{"id":"W2802924253","doi":"10.1016/j.seta.2018.04.010","title":"Statistical approach for improved wind speed forecasting for wind power production","year":2018,"lang":"en","type":"article","venue":"Sustainable Energy Technologies and Assessments","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":90,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Wind speed; Wind power; Meteorology; Environmental science; Wind power forecasting; Power (physics); Electric power system; Engineering; Geography","score_opus":0.014962264962493156,"score_gpt":0.25282759347863043,"score_spread":0.2378653285161373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802924253","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":[0.09236485,0.00028339966,0.89737463,0.00012265459,0.00079156714,0.00064778107,0.000050827366,0.0012881404,0.0070761596],"genre_scores_gemma":[0.931079,0.000027406772,0.06681278,0.000014333928,0.00016738584,0.00005891784,0.00008398255,0.00005807242,0.0016981643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986316,0.000006596351,0.0002310829,0.00037468676,0.000081462254,0.00067457353],"domain_scores_gemma":[0.99942684,0.00007660032,0.00006225121,0.00021474635,0.00017609553,0.00004345522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021486936,0.00022563826,0.00021177885,0.00014722868,0.00034898164,0.0001052291,0.00015769959,0.00020205085,0.0000050933445],"category_scores_gemma":[0.00023589042,0.00021652773,0.000043576827,0.00021029185,0.00016018143,0.00023482274,0.00010932743,0.00010996905,1.1529601e-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":[0.00038921172,0.00024403716,0.0005944284,0.0018193861,0.0006021365,0.000015509759,0.00028318967,0.021687636,0.0079737995,0.35953316,0.015918666,0.5909388],"study_design_scores_gemma":[0.0018435733,0.0017259415,0.0000733582,0.00008202829,0.00010220784,0.000035439418,0.016340697,0.7690931,0.040381134,0.04433978,0.12494974,0.0010329825],"about_ca_topic_score_codex":0.000022685394,"about_ca_topic_score_gemma":0.000003849349,"teacher_disagreement_score":0.8387141,"about_ca_system_score_codex":0.00008035454,"about_ca_system_score_gemma":0.000035141442,"threshold_uncertainty_score":0.88297445},"labels":[],"label_agreement":null},{"id":"W2807030657","doi":"10.1109/tste.2018.2841938","title":"Analytical Iterative Multistep Interval Forecasts of Wind Generation Based on TLGP","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Sustainable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":25,"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":"Engineering and Physical Sciences Research Council; Queen's University; National Natural Science Foundation of China; Queen's University Belfast; European Commission","keywords":"Probabilistic logic; Probabilistic forecasting; Wind power; Wind power forecasting; Interval (graph theory); Benchmark (surveying); Computer science; Mathematical optimization; Monte Carlo method; Power system simulation; Grid; Electric power system; Key (lock); Iterative method; Consensus forecast; Econometrics; Power (physics); Engineering; Algorithm; Mathematics; Artificial intelligence; Statistics","score_opus":0.015124768453612715,"score_gpt":0.23375542188111406,"score_spread":0.21863065342750135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807030657","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":[0.10482317,0.000019306586,0.8791361,0.000032836735,0.00072462595,0.00008327474,0.000021099451,0.00015505488,0.015004514],"genre_scores_gemma":[0.99587005,0.000005861717,0.0007027521,0.00010200586,0.0002526406,0.000021128215,0.0000137444995,0.000049791048,0.0029820292],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987632,0.000049518643,0.00032255374,0.00025193972,0.00022002772,0.00039275005],"domain_scores_gemma":[0.99926895,0.00009769868,0.000043369728,0.00026955933,0.00021378929,0.00010662998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011898991,0.00023912135,0.00023818403,0.00036353982,0.00017263608,0.000043454816,0.0001187613,0.00013606304,0.000253935],"category_scores_gemma":[0.000010011007,0.0002361382,0.0001343958,0.0004183633,0.00010027944,0.00019096043,0.0000012837619,0.00017284855,0.00000853681],"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":[0.00012125606,0.00012360736,0.0000051057186,0.000047210706,0.00006711773,0.000022044327,0.00028888503,0.9764651,0.0014146661,0.0026043518,0.00036454503,0.018476123],"study_design_scores_gemma":[0.00048251334,0.0006390226,0.000008589166,0.000054820364,0.000026418225,0.0000036701756,0.00014817424,0.8110234,0.1824413,0.000042742773,0.0049317805,0.00019756577],"about_ca_topic_score_codex":0.00013886743,"about_ca_topic_score_gemma":0.00015816466,"teacher_disagreement_score":0.8910469,"about_ca_system_score_codex":0.00019103287,"about_ca_system_score_gemma":0.000048418136,"threshold_uncertainty_score":0.9629437},"labels":[],"label_agreement":null},{"id":"W2807887237","doi":"10.1109/tpwrs.2018.2848207","title":"A Fuzzy Adaptive Probabilistic Wind Power Prediction Framework Using Diffusion Kernel Density Estimators","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":124,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; Kernel density estimation; Wind power; Computer science; Probabilistic logic; Wind power forecasting; Probability density function; Electric power system; Mathematical optimization; Prediction interval; Kernel (algebra); Engineering; Power (physics); Machine learning; Mathematics; Artificial intelligence; Statistics","score_opus":0.01651968419172244,"score_gpt":0.2222424718126623,"score_spread":0.20572278762093985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2807887237","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":[0.40151128,0.00006502683,0.588468,0.0000033776093,0.006974993,0.00026993066,0.00005088052,0.00050703716,0.0021494809],"genre_scores_gemma":[0.99814695,0.0000058286378,0.001438232,0.00001843114,0.00012997878,0.000024560159,0.000002713403,0.00009802035,0.00013531219],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981684,0.000078800105,0.0004809015,0.0004191202,0.0003794984,0.00047325765],"domain_scores_gemma":[0.99898195,0.00013141497,0.000085219304,0.00045399967,0.00014772784,0.00019967146],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020266903,0.0003819528,0.0003475646,0.0002322847,0.00040387077,0.00010077273,0.00015991954,0.0003477468,0.00006736072],"category_scores_gemma":[0.000015016854,0.00037563665,0.00014282145,0.00044505345,0.00012780381,0.00026587606,0.0000025472998,0.000494906,0.00010593697],"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":[0.000142303,0.0002198844,0.0004550547,0.00013871414,0.00027414874,0.00002601449,0.0038804305,0.98725104,0.006156486,0.0008646302,0.00022812623,0.00036319057],"study_design_scores_gemma":[0.0008998715,0.0008773489,0.0011385215,0.002409677,0.00020586667,0.00030722958,0.00079076824,0.9836498,0.0069154906,0.0005118587,0.0011530307,0.0011405557],"about_ca_topic_score_codex":0.0001563725,"about_ca_topic_score_gemma":0.000039890012,"teacher_disagreement_score":0.59663564,"about_ca_system_score_codex":0.0003127761,"about_ca_system_score_gemma":0.00004169085,"threshold_uncertainty_score":0.9998696},"labels":[],"label_agreement":null},{"id":"W2809317444","doi":"10.3390/en11071636","title":"Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †","year":2018,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":902,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"United Arab Emirates University","keywords":"Computer science; Machine learning; Artificial intelligence; Hyperparameter; Mean absolute percentage error; Mean squared error; Feature selection; Artificial neural network; Time series; Scheduling (production processes); Feature engineering; Feature (linguistics); Genetic algorithm; Model selection; Deep learning; Algorithm; Mathematical optimization; Mathematics; Statistics","score_opus":0.029822445641775386,"score_gpt":0.21862465280303991,"score_spread":0.18880220716126453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2809317444","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":[0.525897,0.0018907408,0.47156873,0.0000034884317,0.00005755206,0.00007594959,0.0000010388595,0.00027207047,0.00023340524],"genre_scores_gemma":[0.7195566,0.000038584963,0.27981493,0.0000035480584,0.00029749985,0.00001949266,0.00001453212,0.00007660784,0.00017816012],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987563,0.000033399123,0.00020752534,0.00031856186,0.00017845895,0.0005057717],"domain_scores_gemma":[0.99958205,0.000081830636,0.00008997529,0.00007837392,0.00009140149,0.00007635614],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014486143,0.00030521833,0.0002906687,0.00015060058,0.0005381189,0.00012275053,0.00008814233,0.00013906026,0.0000030142112],"category_scores_gemma":[0.000041460084,0.00028424652,0.000046870755,0.0003300228,0.00004637944,0.00019750014,0.00003666662,0.0004203962,5.203057e-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":[0.00003194001,0.000007453474,0.0018280492,0.00005833848,0.00006205313,0.0000014648771,0.0012575767,0.9374834,0.003081522,0.000024275436,0.000009477033,0.056154475],"study_design_scores_gemma":[0.00040480454,0.0002731001,0.00009165822,0.000064582084,0.00006773309,0.000108507564,0.00019014439,0.9907103,0.007282781,0.000015862317,0.00042528627,0.00036524524],"about_ca_topic_score_codex":0.000041365038,"about_ca_topic_score_gemma":0.00012332239,"teacher_disagreement_score":0.19365962,"about_ca_system_score_codex":0.00009980975,"about_ca_system_score_gemma":0.00003094204,"threshold_uncertainty_score":0.99996096},"labels":[],"label_agreement":null},{"id":"W2810149872","doi":"10.1016/j.enbuild.2018.06.017","title":"A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction","year":2018,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":118,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"China Postdoctoral Science Foundation; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Artificial neural network; Robustness (evolution); Convergence (economics); Computer science; Energy consumption; Artificial intelligence; Machine learning; Engineering","score_opus":0.009812603847818221,"score_gpt":0.21669889232791723,"score_spread":0.206886288480099,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810149872","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":[0.67307204,0.0006877175,0.32346794,0.000030449793,0.0011133353,0.00004131655,0.0000060284374,0.0006049967,0.0009761651],"genre_scores_gemma":[0.99091214,0.0001109604,0.005259035,0.00011358334,0.003306199,0.000031379004,0.00004216435,0.000054539036,0.00016997806],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987246,0.00004615404,0.00028616659,0.0003036507,0.00012350947,0.00051591],"domain_scores_gemma":[0.9995329,0.00015871278,0.00006043204,0.00009280676,0.000038863873,0.00011632013],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002770211,0.00022466786,0.00020382598,0.00011309331,0.0005888861,0.00009888499,0.00009292819,0.00012268545,0.000022418768],"category_scores_gemma":[0.00006257116,0.00023287255,0.00007512255,0.00010708866,0.00007066859,0.00020330632,0.000034682344,0.00021596348,9.074908e-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":[0.00025978519,0.00003861467,0.0021576623,0.000056043566,0.00015493036,0.00000920736,0.00023252444,0.22680381,0.040387817,0.24927057,0.010369995,0.47025904],"study_design_scores_gemma":[0.00026755946,0.0002595065,0.00011185399,0.000067098,0.000036871697,0.000060900977,0.0000047614585,0.7908732,0.021627644,0.003142299,0.18325736,0.00029092305],"about_ca_topic_score_codex":0.000050018414,"about_ca_topic_score_gemma":0.000021341095,"teacher_disagreement_score":0.5640694,"about_ca_system_score_codex":0.000043644137,"about_ca_system_score_gemma":0.000008318598,"threshold_uncertainty_score":0.9496267},"labels":[],"label_agreement":null},{"id":"W2883446184","doi":"10.1109/access.2018.2856768","title":"A Data Imputation Model in Phasor Measurement Units Based on Bagged Averaging of Multiple Linear Regression","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":20,"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":"University of Texas at Austin; Chulalongkorn University; Ryerson University","keywords":"Imputation (statistics); Missing data; Phasor measurement unit; Computer science; Phasor; Linear regression; Data mining; Units of measurement; Regression; Bootstrapping (finance); Statistics; Electric power system; Machine learning; Mathematics","score_opus":0.15513166538384515,"score_gpt":0.32386554349956725,"score_spread":0.1687338781157221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883446184","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":[0.7838395,0.000056282846,0.21393195,0.00001966273,0.0005112877,0.0001318902,0.000051317922,0.00012279289,0.0013353017],"genre_scores_gemma":[0.99774617,0.000005063899,0.0020017552,0.00004092707,0.00011757443,0.0000061725045,0.000051075993,0.000026197311,0.000005075419],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991156,0.000027337954,0.00023342628,0.0001839146,0.00027064953,0.00016908463],"domain_scores_gemma":[0.9993631,0.00005642329,0.000054497395,0.0003641017,0.00012347418,0.00003836366],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035792793,0.00011912545,0.00013157261,0.00015331905,0.000039621413,0.000022586408,0.0003738996,0.000051705094,0.000007959483],"category_scores_gemma":[0.00013452316,0.0001082286,0.00001362937,0.00032269544,0.000016610125,0.00032998656,0.000044102384,0.000112577225,0.0000028734717],"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":[0.000038440772,0.000027657492,0.0011497377,0.000057085268,0.0000072790654,0.0000019074319,0.00019058674,0.97922564,0.011969891,0.000003932191,0.0002795301,0.0070482967],"study_design_scores_gemma":[0.00065954117,0.000020736526,0.0002547896,0.0004387679,0.000005493519,2.4512715e-7,0.0000063344496,0.8925075,0.10586823,0.000020979538,0.000114107854,0.00010327411],"about_ca_topic_score_codex":0.00007458999,"about_ca_topic_score_gemma":0.00028042687,"teacher_disagreement_score":0.21390665,"about_ca_system_score_codex":0.00005411239,"about_ca_system_score_gemma":0.000043480148,"threshold_uncertainty_score":0.44134346},"labels":[],"label_agreement":null},{"id":"W2884217876","doi":"10.3386/w24855","title":"Emptying the Tank: Getting the most out of Limited Data","year":2018,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Energy Load and Power Forecasting","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 Calgary","funders":"","keywords":"Computer science; Statistics; Business; Environmental science; Mathematics","score_opus":0.44036776222048357,"score_gpt":0.4803400071897194,"score_spread":0.03997224496923585,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884217876","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005296258,0.0028615783,0.000034646597,0.0004979105,0.001986363,0.00038007798,0.00054917205,0.00004588568,0.9883481],"genre_scores_gemma":[0.98407924,0.0022743996,0.0003685785,0.000025011226,0.004749115,0.000047303027,0.001662039,0.00012879017,0.0066655353],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972467,0.0001225299,0.0007751606,0.00032645193,0.0011630347,0.00036613602],"domain_scores_gemma":[0.99520123,0.0024878895,0.00026115015,0.00096528634,0.0010392106,0.000045235563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008485672,0.00018847748,0.0003370584,0.00029807692,0.0002131037,0.00006964017,0.0018356212,0.00024100649,0.00020551171],"category_scores_gemma":[0.00227746,0.00012841969,0.0000918409,0.00020665389,0.000355766,0.00015072999,0.00060327566,0.0008805343,0.000041386338],"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":[0.000038322727,0.0000506483,0.0013487652,0.0011061606,0.0016462859,0.00000535174,0.0010595603,0.053167503,0.00096371124,0.031026712,0.8847734,0.024813589],"study_design_scores_gemma":[0.00039329025,0.0000761892,0.00048351032,0.0009972239,0.00006181025,0.000040046445,0.00032091883,0.1607373,0.0015876882,0.018744878,0.81603223,0.0005249309],"about_ca_topic_score_codex":0.0006004919,"about_ca_topic_score_gemma":0.0005301252,"teacher_disagreement_score":0.9816826,"about_ca_system_score_codex":0.00041136562,"about_ca_system_score_gemma":0.0012216084,"threshold_uncertainty_score":0.5236803},"labels":[],"label_agreement":null},{"id":"W2884342870","doi":"10.1007/978-981-13-1648-7_15","title":"Improving Energy Demand Estimation Using an Adaptive Firefly Algorithm","year":2018,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Firefly algorithm; Particle swarm optimization; Estimation; Computer science; Mathematical optimization; Energy demand; Weighting; Population; Energy (signal processing); Algorithm; Mathematics; Engineering; Economics; Statistics; Environmental economics","score_opus":0.036446996431429714,"score_gpt":0.2614611902246,"score_spread":0.2250141937931703,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884342870","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0002942708,0.00030106862,0.94319427,0.0000070960537,0.00029822814,0.000084458865,0.000015970347,0.00012324823,0.055681366],"genre_scores_gemma":[0.1257131,0.001151144,0.87213546,0.0002185994,0.00019049858,0.000015582524,0.00021466683,0.000038235718,0.00032269527],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901086,0.000012884403,0.0004469153,0.00014171422,0.00020764218,0.00017995472],"domain_scores_gemma":[0.99879426,0.000059417813,0.00014842988,0.0007292911,0.00018631155,0.00008227897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042168968,0.00018741877,0.00016386772,0.00055005465,0.00036628603,0.00026756123,0.0007020212,0.00012085802,0.0000112065045],"category_scores_gemma":[0.000009476702,0.00020193084,0.0000231611,0.00018582575,0.00043711893,0.0048275,0.00038751736,0.00020874513,0.0000079202955],"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":[0.000001199578,0.000005849921,0.0000018744657,0.000020774067,0.000006361657,2.1108633e-7,0.0015567432,0.059367932,0.000009720102,0.024407672,0.000029113917,0.91459256],"study_design_scores_gemma":[0.00009350135,0.00004148065,0.00001677277,0.00017038024,0.0000064573946,0.000016094891,0.000018465957,0.9893394,0.00004331575,0.0017388962,0.008293043,0.00022215796],"about_ca_topic_score_codex":0.00004006512,"about_ca_topic_score_gemma":0.000021253629,"teacher_disagreement_score":0.9299715,"about_ca_system_score_codex":0.00014406978,"about_ca_system_score_gemma":0.00008694028,"threshold_uncertainty_score":0.8234501},"labels":[],"label_agreement":null},{"id":"W2884724231","doi":"10.17775/cseejpes.2016.00970","title":"Wind power forecasting using wavelet transforms and neural networks with tapped delay","year":2018,"lang":"en","type":"article","venue":"CSEE Journal of Power and Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":43,"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":"Wavelet; Artificial neural network; Wavelet transform; Wind power; Power (physics); Computer science; Electrical engineering; Electronic engineering; Environmental science; Engineering; Artificial intelligence; Physics","score_opus":0.012229016742131437,"score_gpt":0.193312828189157,"score_spread":0.18108381144702554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2884724231","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":[0.91637343,0.005140365,0.07351042,0.000011140313,0.0017010781,0.000034949644,0.0000031440748,0.000041148327,0.0031843134],"genre_scores_gemma":[0.9986279,0.00008288404,0.00045926007,0.00002856605,0.0006753604,7.0559963e-7,0.0000011282423,0.00005954323,0.00006465484],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863875,0.000035976027,0.0005410814,0.00015341444,0.0002124532,0.00041835406],"domain_scores_gemma":[0.99928147,0.000057804617,0.00017145209,0.00011074753,0.00014068579,0.00023786699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003180504,0.00027633263,0.0004246971,0.0001623189,0.0001678458,0.00014168116,0.00010800387,0.00013921481,0.000011153015],"category_scores_gemma":[0.000008644445,0.0001965571,0.00006707202,0.00018152206,0.00011565977,0.00039164769,0.000018908793,0.00024103782,1.399345e-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":[0.00027656346,0.000031927404,0.0041238344,0.00015714836,0.00061758497,0.00039931072,0.00341801,0.9753613,0.0021480336,0.00060342334,0.00040054886,0.012462355],"study_design_scores_gemma":[0.0010694519,0.00066894764,0.00026883886,0.00061578164,0.0000763914,0.0068722744,0.0005385066,0.98294055,0.00021798685,0.000014525202,0.0063176816,0.00039903718],"about_ca_topic_score_codex":0.000049305247,"about_ca_topic_score_gemma":0.000028808676,"teacher_disagreement_score":0.08225446,"about_ca_system_score_codex":0.00003502043,"about_ca_system_score_gemma":0.000019335463,"threshold_uncertainty_score":0.80153656},"labels":[],"label_agreement":null},{"id":"W2885902739","doi":"10.1109/icc.2018.8423021","title":"Data Communication and Analytics for Smart Grid Systems","year":2018,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Smart grid; Support vector machine; Mean squared error; Energy consumption; Data mining; Cloud computing; Linear regression; Polynomial regression; Machine learning; Statistics; Engineering; Mathematics","score_opus":0.06061986730852808,"score_gpt":0.26570278002451403,"score_spread":0.20508291271598594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885902739","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":[0.15059139,0.0054422175,0.5243364,0.00029427826,0.0037271804,0.0005849341,0.000583743,0.0013262256,0.31311366],"genre_scores_gemma":[0.99141705,0.00009027951,0.0075652613,0.0000192193,0.0002409713,0.000004045131,0.00018301266,0.0000127825815,0.00046740178],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975455,0.0000044100457,0.00008171119,0.000059686554,0.000025967436,0.000073696734],"domain_scores_gemma":[0.9995191,0.00005202879,0.000008939386,0.00037530696,0.000021672922,0.000022957522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012589686,0.000041101128,0.000053206295,0.000017888973,0.000048981878,0.000033739685,0.0001434729,0.000023649709,0.000006734021],"category_scores_gemma":[0.00001528986,0.00003677919,0.0000050670833,0.000036319492,0.000019876667,0.0000918123,0.0000546321,0.00002463306,0.0000043051587],"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":[0.00003733134,0.00006282963,0.01314392,0.0013697211,0.0006513563,0.0000017291404,0.0015798814,0.038724836,0.0039512017,0.13388301,0.74773204,0.058862105],"study_design_scores_gemma":[0.000073589516,0.000010798057,0.00007464435,0.000020389898,0.000008414968,0.0000022628383,0.000034216744,0.777542,0.00016879727,0.000036022164,0.22197816,0.000050738592],"about_ca_topic_score_codex":0.000037872644,"about_ca_topic_score_gemma":0.00017143648,"teacher_disagreement_score":0.8408256,"about_ca_system_score_codex":0.00000521088,"about_ca_system_score_gemma":0.0000023174996,"threshold_uncertainty_score":0.14998119},"labels":[],"label_agreement":null},{"id":"W2888492830","doi":"10.1007/978-3-319-62575-1_72","title":"Data-Driven Modeling for Energy Consumption Estimation","year":2018,"lang":"en","type":"book-chapter","venue":"Green energy and technology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Estimation; Energy consumption; Consumption (sociology); Computer science; Econometrics; Environmental science; Mathematics; Economics; Engineering; Sociology","score_opus":0.03559117643788988,"score_gpt":0.23054639560632875,"score_spread":0.1949552191684389,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2888492830","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":[0.00050176476,0.0057951305,0.9221047,0.00016843021,0.0008258101,0.0001187711,0.0005378732,0.0018457053,0.06810182],"genre_scores_gemma":[0.54908967,0.021573653,0.09620918,0.0005644256,0.00442813,0.00041260727,0.021579528,0.0014724486,0.30467036],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891955,0.000003476638,0.00030597384,0.00042504436,0.00008256257,0.00026338795],"domain_scores_gemma":[0.99920946,0.000038397226,0.000081064594,0.0005557082,0.00006605611,0.00004930496],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00006237658,0.00032166365,0.00033790886,0.0004049671,0.00011613916,0.00001960556,0.00032285706,0.00092283223,0.000080682265],"category_scores_gemma":[0.000010390309,0.0003487758,0.000039378105,0.00003994929,0.00013872662,0.00011963891,0.00018202212,0.00015455365,0.000007107161],"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":[0.000012131859,0.000004179177,0.0000029854,0.0001323537,0.00021970963,0.0000075872767,0.000015448719,0.029997814,0.00007398054,0.69438785,0.0021047622,0.2730412],"study_design_scores_gemma":[0.00015685915,0.000042195137,6.636743e-8,0.00012606813,0.00006190139,0.000029449477,0.0000013390029,0.6954114,0.00011757944,0.071433805,0.23235045,0.00026890283],"about_ca_topic_score_codex":0.000040978797,"about_ca_topic_score_gemma":0.0006836672,"teacher_disagreement_score":0.8258955,"about_ca_system_score_codex":0.000032589855,"about_ca_system_score_gemma":0.000017389166,"threshold_uncertainty_score":0.9998964},"labels":[],"label_agreement":null},{"id":"W2889074333","doi":"10.1109/ccece.2018.8447641","title":"A Probabilistic Approach for Peak Load Demand Forecasting","year":2018,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Memorial University of Newfoundland","funders":"","keywords":"Probabilistic forecasting; Probabilistic logic; Computer science; Monte Carlo method; Demand forecasting; Contingency; Sensitivity (control systems); Toolbox; Electric power system; MATLAB; Mathematical optimization; Reliability engineering; Power (physics); Engineering; Operations research; Artificial intelligence; Statistics; Mathematics","score_opus":0.02957776723903773,"score_gpt":0.21888794125139524,"score_spread":0.1893101740123575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889074333","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":[0.072181806,0.00015118964,0.53113174,0.000009697409,0.0004166295,0.00032728835,0.0000067367437,0.00066205923,0.39511284],"genre_scores_gemma":[0.9418081,0.0000016456236,0.056413025,0.000030280024,0.00061175186,0.000071226415,0.000011803478,0.00004180722,0.0010103274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999202,0.0000061275546,0.0001812451,0.00017627621,0.000098561846,0.0003357957],"domain_scores_gemma":[0.99960345,0.00007499547,0.00001841546,0.0001467552,0.0000856461,0.000070711045],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020982168,0.00014280349,0.00013510512,0.00003630326,0.00010908719,0.000040020914,0.000110650864,0.0000639786,0.00004980115],"category_scores_gemma":[0.0001093487,0.00012378188,0.000056326146,0.00011969262,0.00004616725,0.00008823044,0.00002276667,0.00006370217,0.000011755978],"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":[0.00022814842,0.00033216388,0.0018290252,0.0045054317,0.00064155256,0.000014460727,0.009763767,0.62320894,0.014942612,0.07141662,0.062140692,0.2109766],"study_design_scores_gemma":[0.000296264,0.00007903974,0.000027003383,0.00002871747,0.000015839682,0.000019213687,0.0000483229,0.98615384,0.0021070212,0.0007336344,0.010282877,0.00020822894],"about_ca_topic_score_codex":0.000008192181,"about_ca_topic_score_gemma":0.000035499364,"teacher_disagreement_score":0.86962634,"about_ca_system_score_codex":0.000048583563,"about_ca_system_score_gemma":0.000018123515,"threshold_uncertainty_score":0.50476784},"labels":[],"label_agreement":null},{"id":"W2889084344","doi":"10.1109/ccece.2018.8447635","title":"Wind Speed Time Series Predicted by Neural Network","year":2018,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Memorial University of Newfoundland","funders":"","keywords":"Wind speed; Artificial neural network; Autoregressive–moving-average model; Time series; Series (stratigraphy); Wind power; Computer science; Autoregressive model; Conjugate gradient method; Moving average; Control theory (sociology); Algorithm; Meteorology; Artificial intelligence; Machine learning; Mathematics; Statistics; Engineering; Geography","score_opus":0.005667338990610832,"score_gpt":0.1787050738342382,"score_spread":0.17303773484362736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889084344","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":[0.72818255,0.00015406484,0.0004207668,0.00007472512,0.0016069018,0.00006291856,0.000021176455,0.0013502897,0.2681266],"genre_scores_gemma":[0.98018056,0.000006495245,0.0008686758,0.000106117295,0.0013707575,3.7163974e-7,0.000051900148,0.000037603462,0.017377503],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994596,0.0000075278554,0.000112675596,0.00009095477,0.000069035515,0.00026019127],"domain_scores_gemma":[0.9997842,0.000015796835,0.000009697201,0.000109639,0.000018199182,0.00006247831],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000039948503,0.00010564497,0.00009256271,0.000014568102,0.000059571063,0.000029798071,0.00008054902,0.000053893706,0.00121916],"category_scores_gemma":[0.0000061057053,0.00009471728,0.00002280544,0.0001308715,0.0000416147,0.00013900606,0.000021038997,0.00006992107,0.00012691983],"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":[0.000022288397,0.000009417537,0.0021149404,0.00001708848,0.00007557964,0.0000063229363,0.00026236614,0.11490449,0.012141974,0.00026302296,0.867115,0.0030674932],"study_design_scores_gemma":[0.00036392317,0.00019427694,0.0011255365,0.000042546013,0.00001831087,0.000028910135,0.000017244825,0.6862158,0.016678443,0.00015320686,0.29472095,0.00044081145],"about_ca_topic_score_codex":0.00000800278,"about_ca_topic_score_gemma":0.000007655964,"teacher_disagreement_score":0.5723941,"about_ca_system_score_codex":0.000008719394,"about_ca_system_score_gemma":0.0000030079402,"threshold_uncertainty_score":0.9996939},"labels":[],"label_agreement":null},{"id":"W2889205082","doi":"10.1109/ccece.2018.8447846","title":"Uncertainty Estimation in Wind Power Forecasts Using Monte Carlo Simulations","year":2018,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Monte Carlo method; Wind speed; Wind power; Realization (probability); Computer science; Gaussian; Stochastic process; Probability distribution; Meteorology; Simulation; Statistics; Algorithm; Mathematics; Engineering; Physics; Electrical engineering","score_opus":0.019650022639967846,"score_gpt":0.25265640765072944,"score_spread":0.2330063850107616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889205082","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":[0.969805,0.000027578188,0.016532982,0.000009191958,0.00031926166,0.00006230749,0.0000046326413,0.00014554795,0.013093479],"genre_scores_gemma":[0.9939038,6.415749e-7,0.0058873207,0.000022640965,0.00006527071,8.5681563e-7,0.000004548885,0.00002090891,0.00009398234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994254,0.0000091270285,0.00017776442,0.000104184626,0.00008054648,0.00020298781],"domain_scores_gemma":[0.99975103,0.00003974604,0.000016067093,0.00011681027,0.000035362904,0.000040965475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006377153,0.00010322996,0.00009349741,0.00011179031,0.000055487526,0.000026030673,0.000053503027,0.00006074025,0.00017148908],"category_scores_gemma":[0.000027247264,0.000101254904,0.000024219175,0.00023059106,0.000023966762,0.00020063236,0.000014752577,0.00007335615,0.000013819944],"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":[0.0000022640704,0.00000458757,0.0014418288,0.000004617065,0.00000653779,0.0000013407372,0.0004883566,0.9953137,0.00058426533,0.00017893792,0.000047598132,0.0019259527],"study_design_scores_gemma":[0.00016506747,0.000016209544,0.0009776499,0.00004101995,0.000003845171,0.0000031735142,0.000034705754,0.9972823,0.00068669004,0.0001538619,0.0005121674,0.0001232772],"about_ca_topic_score_codex":0.0002460231,"about_ca_topic_score_gemma":0.0008767055,"teacher_disagreement_score":0.02409881,"about_ca_system_score_codex":0.000080528764,"about_ca_system_score_gemma":0.0000113072265,"threshold_uncertainty_score":0.4129055},"labels":[],"label_agreement":null},{"id":"W2889355038","doi":"10.1109/ccece.2018.8447838","title":"A Nonparametric Probability Distribution Model for Short-Term Wind Power Prediction Error","year":2018,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Saskatchewan","funders":"","keywords":"Wind power; Kernel density estimation; Kurtosis; Nonparametric statistics; Computer science; Skewness; Parametric statistics; Wind power forecasting; Model selection; Probability distribution; Parametric model; Power (physics); Electric power system; Statistics; Mathematics; Artificial intelligence; Engineering","score_opus":0.028032182502849,"score_gpt":0.24523025343285457,"score_spread":0.21719807093000557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889355038","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":[0.5308902,0.000017073296,0.46364492,0.000008934155,0.0003441592,0.0002004324,0.0001174932,0.00032652245,0.0044502653],"genre_scores_gemma":[0.9948465,0.0000019257293,0.0045535015,0.00000989361,0.00015789173,0.00003276932,0.00013751863,0.000021120095,0.00023883482],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992356,0.00000586671,0.00020838737,0.00018882201,0.000102654834,0.00025869074],"domain_scores_gemma":[0.9996122,0.000033452434,0.000013641789,0.00018280986,0.00008764382,0.00007022149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015762,0.00012840207,0.00011258093,0.000049110073,0.000085941516,0.000028625882,0.00007771859,0.00010362757,0.000055205594],"category_scores_gemma":[0.000055454984,0.00011675273,0.00006596381,0.00022040575,0.000040110106,0.00017369192,0.000016892407,0.00007458272,0.000009351837],"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":[0.00019777202,0.0003489398,0.033289038,0.00042830026,0.00021946628,0.000001623604,0.0015865098,0.89750296,0.011413978,0.010123761,0.016544307,0.028343344],"study_design_scores_gemma":[0.00015842705,0.00009755754,0.003362916,0.000015838472,0.000014649767,0.000002643464,0.0000059632584,0.9916533,0.0028526818,0.0007147344,0.0009878427,0.0001334388],"about_ca_topic_score_codex":0.0000034645484,"about_ca_topic_score_gemma":0.000025319365,"teacher_disagreement_score":0.46395636,"about_ca_system_score_codex":0.000098183,"about_ca_system_score_gemma":0.000013849758,"threshold_uncertainty_score":0.4761038},"labels":[],"label_agreement":null},{"id":"W2889527493","doi":"10.1109/ccece.2018.8447739","title":"Optimizing Load Forecasting Configurations of Computational Neural Networks","year":2018,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Artificial neural network; Computer science; Evolutionary algorithm; Probabilistic logic; Artificial intelligence; Machine learning","score_opus":0.022408806831147328,"score_gpt":0.22289611119387787,"score_spread":0.20048730436273055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889527493","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":[0.1504432,0.00013559249,0.71145564,0.000020869556,0.00063399825,0.000058180984,0.0000040230466,0.00029023667,0.13695824],"genre_scores_gemma":[0.97803843,0.0000015686169,0.021566173,0.00003490883,0.00025735717,0.0000024264834,0.00001339952,0.000016581833,0.0000691452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942017,0.0000075471817,0.00021816073,0.00008107744,0.00010282992,0.00017023334],"domain_scores_gemma":[0.99964654,0.000090960166,0.000031119245,0.00007106503,0.00011832559,0.000041990355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007540089,0.000088609726,0.00010223501,0.000039128834,0.0000772366,0.000020100411,0.00006875756,0.000041271236,0.00019566808],"category_scores_gemma":[0.000018596618,0.00008688681,0.000037464742,0.00014534743,0.000054552573,0.00011029256,0.000015253772,0.00007548583,0.000004436495],"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":[0.0000020019831,0.0000032948503,0.00008506062,0.000007499215,0.00001521928,7.9150243e-7,0.00023169795,0.99107337,0.00026993794,0.0015091012,0.00033945238,0.0064625847],"study_design_scores_gemma":[0.00012921936,0.000027586048,0.00009548769,0.000023191586,0.000005522693,0.0000112979415,0.00003729804,0.99785584,0.0012432812,0.00012034671,0.0003574469,0.000093490526],"about_ca_topic_score_codex":0.000013878125,"about_ca_topic_score_gemma":0.000031923435,"teacher_disagreement_score":0.82759523,"about_ca_system_score_codex":0.000019205108,"about_ca_system_score_gemma":0.000011648811,"threshold_uncertainty_score":0.35431412},"labels":[],"label_agreement":null},{"id":"W2889801955","doi":"10.1115/1.4041413","title":"Short-Term Forecasting of Natural Gas Consumption Using Factor Selection Algorithm and Optimized Support Vector Regression","year":2018,"lang":"en","type":"article","venue":"Journal of Energy Resources Technology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":46,"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 Regina","funders":"Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Support vector machine; Consumption (sociology); Computer science; Genetic algorithm; Term (time); Selection (genetic algorithm); Regression analysis; Regression; Predictive modelling; Time series; Data mining; Machine learning; Artificial intelligence; Econometrics; Statistics; Mathematics","score_opus":0.019822132816913425,"score_gpt":0.24747086062854495,"score_spread":0.22764872781163153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2889801955","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":[0.9857594,0.0009121894,0.012587225,0.000013578014,0.0005036248,0.000025438758,0.0000032520888,0.00009569421,0.00009963332],"genre_scores_gemma":[0.9727966,0.0001689765,0.026638223,0.000004521937,0.00033370833,9.7731e-7,0.0000023883642,0.000032707656,0.000021851665],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998851,0.000029369472,0.0005483481,0.0001350417,0.00017150362,0.00026476334],"domain_scores_gemma":[0.9992722,0.000060205366,0.0003074841,0.00010197231,0.00019278654,0.000065306966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014573592,0.00018967687,0.00038816372,0.00063380145,0.000101719976,0.00002119274,0.00015234551,0.00026397855,0.000028196391],"category_scores_gemma":[0.00005929373,0.0001577936,0.00007974155,0.00029132355,0.0001952525,0.00017311747,0.000056973942,0.00032211503,2.3155178e-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":[0.00026350273,0.00005900582,0.02028429,0.00014340473,0.00043227756,0.00009490307,0.0007202067,0.012203619,0.50117266,0.0001705549,0.000109503235,0.46434608],"study_design_scores_gemma":[0.0012550496,0.0008498058,0.0013952055,0.00081865664,0.000120813864,0.0043189754,0.0001384271,0.5034722,0.48473448,0.00015665326,0.0023124712,0.00042729845],"about_ca_topic_score_codex":0.000005604116,"about_ca_topic_score_gemma":0.000009203041,"teacher_disagreement_score":0.49126858,"about_ca_system_score_codex":0.00006633973,"about_ca_system_score_gemma":0.000016007605,"threshold_uncertainty_score":0.6434636},"labels":[],"label_agreement":null},{"id":"W2891632433","doi":"10.5539/jas.v10n10p423","title":"Computational System for Sizing Wind Energy Generation Systems Using Artificial Neural Networks","year":2018,"lang":"en","type":"article","venue":"Journal of Agricultural Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Computer science; Wind power; Artificial neural network; Grid; Sizing; Java; Genetic algorithm; Electric power system; Process (computing); Distributed computing; Reliability engineering; Artificial intelligence; Power (physics); Machine learning; Operating system; Engineering; Electrical engineering","score_opus":0.026392120837786446,"score_gpt":0.22802053740911088,"score_spread":0.20162841657132444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891632433","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":[0.84005135,0.0002442805,0.15550752,0.000011983915,0.0039921487,0.000039839644,0.0000016397764,0.000029143124,0.00012209947],"genre_scores_gemma":[0.99249446,0.000001563581,0.0028556339,0.000008321708,0.0046257474,6.3184615e-7,0.000002532104,0.00000628004,0.000004849534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892545,0.000017152492,0.00039406357,0.00010041482,0.0003127751,0.00025015353],"domain_scores_gemma":[0.99912906,0.00004239336,0.00018664732,0.000039006685,0.00050135533,0.0001015563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037771807,0.00010362252,0.0001470458,0.00008106524,0.00036959196,0.00024073337,0.00017727684,0.00004252455,0.0000010535888],"category_scores_gemma":[0.000020970085,0.000067280984,0.00006294215,0.00040154348,0.00008319629,0.0005949567,0.000015607106,0.00007579564,3.2856775e-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":[0.0000028348843,0.0000028348588,0.00002475454,0.000008569454,0.000007956819,0.0000012273832,0.000059314698,0.9147298,0.08247627,0.0019086135,0.000063093656,0.00071473035],"study_design_scores_gemma":[0.00008620688,0.000074500844,0.0007178201,0.000081374405,0.000013830651,0.00034150353,0.00017009223,0.9929173,0.005408789,0.0000100067855,0.0000737213,0.00010486898],"about_ca_topic_score_codex":0.000013432861,"about_ca_topic_score_gemma":0.000007018898,"teacher_disagreement_score":0.15265189,"about_ca_system_score_codex":0.00018980219,"about_ca_system_score_gemma":0.000031358843,"threshold_uncertainty_score":0.28426397},"labels":[],"label_agreement":null},{"id":"W2894470954","doi":"10.1016/j.envsoft.2018.09.017","title":"Short-term air temperature forecasting using Nonparametric Functional Data Analysis and SARMA models","year":2018,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":62,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Masdar Institute of Science and Technology","keywords":"Pooling; Term (time); Autoregressive model; Econometrics; Nonparametric statistics; Parametric statistics; Bayesian probability; Nonparametric regression; Statistics; Computer science; Mathematics; Artificial intelligence","score_opus":0.056450681270344576,"score_gpt":0.22281269264002074,"score_spread":0.16636201136967615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894470954","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":[0.5354676,0.00060301565,0.4634234,0.0000013308979,0.00015014931,0.000052920386,0.00011587861,0.00013555077,0.000050147926],"genre_scores_gemma":[0.9360191,0.00011392026,0.06279741,0.000027459631,0.0003492256,0.000004326674,0.0005749751,0.00007297798,0.000040586452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983564,0.00002122756,0.00032661142,0.0005830804,0.00029544404,0.00041726296],"domain_scores_gemma":[0.99916476,0.00008547005,0.000043890876,0.0005435381,0.000009313288,0.00015300734],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016259881,0.00032846892,0.00029801155,0.00029178077,0.00034336137,0.00007106414,0.0002542427,0.00016112918,0.00005444729],"category_scores_gemma":[0.000007913482,0.0003438503,0.00009138919,0.00049922086,0.00012149475,0.0006408486,0.00024684807,0.00029467585,0.0000072596886],"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":[0.0000089325395,0.000019648523,0.013845682,0.000021283713,0.00026245526,0.000007803863,0.00018285056,0.9793414,0.0010373613,0.00000416053,0.00002235813,0.0052460944],"study_design_scores_gemma":[0.00014017113,0.00002040508,0.0013855757,0.00004292392,0.00032479616,0.00003500705,0.000037567937,0.99663234,0.00082719245,0.00011502054,0.00006230174,0.000376692],"about_ca_topic_score_codex":0.000020272475,"about_ca_topic_score_gemma":0.000007979472,"teacher_disagreement_score":0.40062597,"about_ca_system_score_codex":0.000111988826,"about_ca_system_score_gemma":0.0000090283465,"threshold_uncertainty_score":0.99990135},"labels":[],"label_agreement":null},{"id":"W2896064754","doi":"10.1016/j.eneco.2018.10.005","title":"The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts","year":2018,"lang":"en","type":"article","venue":"Energy Economics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Narodowe Centrum Nauki; Deutsche Forschungsgemeinschaft","keywords":"Economics; Electricity; Econometrics; Quarter (Canadian coin); Predictive power; Electricity price forecasting; Economic forecasting; Multivariate statistics; Electricity market; Consensus forecast; Spot contract; Novelty; Financial economics; Computer science; Futures contract","score_opus":0.022518534083833697,"score_gpt":0.23310745573157454,"score_spread":0.21058892164774085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896064754","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":[0.9762545,0.00051293493,0.0057385573,0.00006921691,0.0012512362,0.00008181417,0.000032950375,0.00007522324,0.015983533],"genre_scores_gemma":[0.9984297,0.0005863775,0.00031145272,0.000046169418,0.00045978604,0.000015110816,0.000008879608,0.000057102396,0.000085423715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985251,0.00005196767,0.0006923686,0.00021567765,0.000056895333,0.00045799656],"domain_scores_gemma":[0.9985635,0.00048486976,0.00032304318,0.00050979346,0.000053101227,0.00006567763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004820098,0.00024429915,0.00032518723,0.000089506924,0.00023114108,0.00005834831,0.00056742586,0.000110898836,0.00002201737],"category_scores_gemma":[0.000033356035,0.00017952426,0.00016949921,0.00013630562,0.00019189503,0.0002120125,0.00006818864,0.00015154316,0.0000074619024],"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":[0.0001628851,0.00004698875,0.0033562067,0.000095322655,0.00080188387,0.0000017306774,0.0023057018,0.6178085,0.0023198577,0.2976641,0.0042375466,0.07119924],"study_design_scores_gemma":[0.0002872506,0.00014843451,0.000336164,0.000034258104,0.000024513542,0.000012054294,0.00019636811,0.91243726,0.048927628,0.0020010679,0.035338223,0.00025677498],"about_ca_topic_score_codex":0.00040889424,"about_ca_topic_score_gemma":0.0025409684,"teacher_disagreement_score":0.29566306,"about_ca_system_score_codex":0.00017802526,"about_ca_system_score_gemma":0.000082294406,"threshold_uncertainty_score":0.7320787},"labels":[],"label_agreement":null},{"id":"W2897479513","doi":"10.1109/innovate-data.2018.00014","title":"Towards Hybrid Energy Consumption Prediction in Smart Grids with Machine Learning","year":2018,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":15,"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":"Computer science; Energy consumption; Smart grid; Thunder; Implementation; Machine learning; Artificial intelligence; Data modeling; Multivariate statistics; Extreme learning machine; Consumption (sociology); Data mining; Real-time computing; Artificial neural network; Engineering","score_opus":0.009188912074757472,"score_gpt":0.1902586320650487,"score_spread":0.18106971999029123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897479513","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":[0.8243251,0.00018028943,0.06464601,0.000017715898,0.0005007817,0.000036459627,0.000006160631,0.00071612827,0.10957134],"genre_scores_gemma":[0.9979101,0.000058829763,0.0008619211,0.000026073103,0.00018266997,0.000007846192,0.0000436594,0.00002496524,0.00088394294],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994395,0.000016726844,0.00013683477,0.00012192577,0.00009692887,0.00018810647],"domain_scores_gemma":[0.9998273,0.0000136966355,0.000014458149,0.00007901218,0.00002111643,0.00004439441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009367438,0.000109741515,0.00009468823,0.000100837795,0.00004811899,0.000020347636,0.000046209956,0.000036765956,0.00046036096],"category_scores_gemma":[0.000007716567,0.00009295372,0.000015748948,0.00010925822,0.000032610096,0.00012759517,0.000013757031,0.0001255894,0.000027987728],"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":[0.0001893524,0.000085410924,0.45787716,0.00017716775,0.00017201246,0.00007530217,0.0010682356,0.280745,0.011231495,0.00667847,0.0029495736,0.23875083],"study_design_scores_gemma":[0.00070329494,0.00027946933,0.013675638,0.00011941073,0.000011689756,0.00007444013,0.000019335921,0.90579736,0.034450594,0.0000617019,0.044536497,0.0002705879],"about_ca_topic_score_codex":0.0003386452,"about_ca_topic_score_gemma":0.0008893661,"teacher_disagreement_score":0.62505233,"about_ca_system_score_codex":0.00004276649,"about_ca_system_score_gemma":0.00000804172,"threshold_uncertainty_score":0.50406295},"labels":[],"label_agreement":null},{"id":"W2898519272","doi":"10.1109/access.2018.2877735","title":"Wavelet Neural Network Based Multiobjective Interval Prediction for Short-Term Wind Speed","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Benchmark (surveying); Wind speed; Computer science; Wind power; Evolutionary algorithm; Artificial neural network; Interval (graph theory); Pareto principle; Mathematical optimization; Set (abstract data type); Artificial intelligence; Mathematics; Meteorology; Engineering","score_opus":0.03349807956869812,"score_gpt":0.28078066283879616,"score_spread":0.24728258327009803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898519272","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":[0.949207,0.000030988198,0.041430358,0.000012678843,0.006646323,0.00023754222,0.00004796036,0.00036053293,0.0020265826],"genre_scores_gemma":[0.99568087,0.0000018789584,0.00058079767,0.000080154045,0.003500679,0.0000099186855,0.00004238184,0.000047214933,0.00005609908],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914974,0.00001596658,0.00020592166,0.0001959008,0.000097633776,0.0003348248],"domain_scores_gemma":[0.9995905,0.00007834438,0.000027119537,0.0001598429,0.0000766599,0.00006749793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000107597756,0.0001635328,0.00015322612,0.00005871578,0.000114279836,0.00009992377,0.00021743373,0.000089812966,0.0000463552],"category_scores_gemma":[0.00001596583,0.00016171832,0.0000790049,0.0001529702,0.000042236305,0.0003588166,0.00002281873,0.000121596786,0.000005944627],"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":[0.00018763724,0.000040417,0.024386158,0.00012540501,0.00013793251,0.00000867304,0.00050769054,0.9256596,0.0067351656,0.000020200376,0.012909739,0.029281374],"study_design_scores_gemma":[0.00046090342,0.00013043835,0.012881066,0.00008582637,0.000026824528,0.000004205791,0.0000076451515,0.96101075,0.023780981,0.00004908264,0.0013652315,0.00019705565],"about_ca_topic_score_codex":0.0000144828555,"about_ca_topic_score_gemma":0.00006777858,"teacher_disagreement_score":0.046473842,"about_ca_system_score_codex":0.00005101808,"about_ca_system_score_gemma":0.000010251796,"threshold_uncertainty_score":0.6594681},"labels":[],"label_agreement":null},{"id":"W2898993596","doi":"10.1016/j.ecolind.2018.10.022","title":"A stochastic multi-objective optimization model for renewable energy structure adjustment management – A case study for the city of Dalian, China","year":2018,"lang":"en","type":"article","venue":"Ecological Indicators","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Research and Development; Times Higher Education","keywords":"Renewable energy; Environmental economics; Wind power; Hydropower; Electricity generation; Computer science; Environmental science; Solar power; Environmental resource management; Power (physics); Economics; Engineering","score_opus":0.022548375454375064,"score_gpt":0.25693045759881744,"score_spread":0.23438208214444237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898993596","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":[0.1878398,0.000047625705,0.81066126,0.000007365079,0.00028904036,0.0008887733,0.00007025951,0.0000749255,0.00012094395],"genre_scores_gemma":[0.9777239,0.000004623438,0.021433236,0.000028670796,0.00010437413,0.00048949715,0.000016273292,0.000023040311,0.00017637222],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911237,0.00002328481,0.0002600218,0.00023660169,0.000099793186,0.00026793993],"domain_scores_gemma":[0.9994677,0.0001463705,0.00009465733,0.00018761588,0.00004185269,0.0000618133],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016655239,0.00017412903,0.00020312425,0.00010187869,0.00023931892,0.00001501357,0.00017183636,0.000103592414,0.000049380546],"category_scores_gemma":[0.000048274393,0.00011734134,0.00007426622,0.00021363524,0.000071599825,0.000047541733,0.00006495541,0.00006753524,1.6418839e-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":[0.000033899865,0.00017254704,0.00015381782,0.000023974788,0.00018741874,0.0000060429534,0.0010753798,0.99681485,0.000008968246,0.0002685883,0.00017668941,0.0010778349],"study_design_scores_gemma":[0.00088977895,0.0004544928,0.0009008856,0.000009224033,0.00012413785,0.000010521445,0.00072683697,0.99626756,0.00017237646,0.00025146172,0.000051336963,0.00014138086],"about_ca_topic_score_codex":0.00010529878,"about_ca_topic_score_gemma":0.00087740313,"teacher_disagreement_score":0.7898841,"about_ca_system_score_codex":0.00008518009,"about_ca_system_score_gemma":0.000013063508,"threshold_uncertainty_score":0.4785041},"labels":[],"label_agreement":null},{"id":"W2899935980","doi":"","title":"Dynamic Bayesian smooth transition autoregressive models applied to hourly electricity load in southern Brazil","year":2018,"lang":"en","type":"book-chapter","venue":"Open Research Online (The Open University)","topic":"Energy Load and Power Forecasting","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":"Autoregressive model; STAR model; SETAR; Series (stratigraphy); Applied mathematics; Mathematics; Bayesian probability; Time series; Nonlinear autoregressive exogenous model; Econometrics; Star (game theory); Autoregressive integrated moving average; Statistics; Mathematical analysis","score_opus":0.042940908886780614,"score_gpt":0.290687832355491,"score_spread":0.24774692346871038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899935980","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002261483,0.00006896294,0.0039170766,0.00053723285,0.000085124775,0.0023469303,0.00054071523,0.000091067304,0.9901514],"genre_scores_gemma":[0.107956335,0.00042234742,0.003554431,0.00018163839,0.0002494799,0.000025060888,0.000754175,0.00037831915,0.88647825],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972576,0.00013569291,0.00032344813,0.00074862456,0.0007326832,0.0008019474],"domain_scores_gemma":[0.99836445,0.00015987759,0.000089341134,0.0007831618,0.00031136227,0.0002917797],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011043008,0.00046443217,0.000605238,0.0008638441,0.0004414227,0.00048728508,0.00423152,0.0004182828,0.00076338125],"category_scores_gemma":[0.000019254858,0.0004393306,0.00010967146,0.0006169347,0.00019183653,0.0005188739,0.0013750804,0.0015666799,0.00023839952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005078783,0.0007104267,0.000011359786,0.00054658315,0.0016289336,0.0025219899,0.035491418,0.47370654,0.0020104458,0.25539184,0.022743696,0.20015799],"study_design_scores_gemma":[0.0043096924,0.0007118442,0.000028626633,0.002746942,0.00014891745,0.000018465005,0.0034635481,0.32565713,0.00015653383,0.029296916,0.63082916,0.002632219],"about_ca_topic_score_codex":0.0023794346,"about_ca_topic_score_gemma":0.027544798,"teacher_disagreement_score":0.60808545,"about_ca_system_score_codex":0.0012426437,"about_ca_system_score_gemma":0.00059007225,"threshold_uncertainty_score":0.99980587},"labels":[],"label_agreement":null},{"id":"W2900937407","doi":"10.5539/ijsp.v8n1p16","title":"A New Method to Detect Outliers in High-frequency Time Series","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"Outlier; Series (stratigraphy); Computer science; Smoothness; Nonparametric statistics; Fidelity; Algorithm; Context (archaeology); Filter (signal processing); Time series; Simple (philosophy); Transformation (genetics); Data mining; Mathematics; Statistics; Artificial intelligence; Machine learning","score_opus":0.010235573659277344,"score_gpt":0.26523028521803843,"score_spread":0.2549947115587611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900937407","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14120036,0.00007447363,0.85635495,0.0002745375,0.0008403241,0.000056445293,0.00008887502,0.000016203947,0.001093835],"genre_scores_gemma":[0.30459183,0.000015044386,0.6950548,0.000034825585,0.00024458338,7.425479e-7,0.0000020148093,0.000007749494,0.00004836166],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9993111,0.00002838786,0.0003181312,0.0000739719,0.00016813986,0.00010030716],"domain_scores_gemma":[0.9994827,0.00010494156,0.000056759287,0.000049634207,0.00020896399,0.00009700655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038949717,0.000077347984,0.0001324297,0.00009883137,0.000015579955,0.000042961994,0.00013521028,0.000030370315,0.00012487428],"category_scores_gemma":[0.00018290483,0.00006999408,0.00001838505,0.000069652146,0.000026339587,0.000117161835,0.000024465839,0.00010904638,0.0000057641296],"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":[0.00042358934,0.000045895235,0.015461879,0.00008697757,0.00034371766,0.00016017097,0.0034166141,0.018766603,0.0116068525,0.04580864,0.005346111,0.8985329],"study_design_scores_gemma":[0.0017379158,0.0011084251,0.062478073,0.00038504318,0.000045320354,0.00040262338,0.000050462688,0.010282993,0.00992182,0.8981686,0.014838186,0.000580488],"about_ca_topic_score_codex":0.00008866046,"about_ca_topic_score_gemma":0.0001545813,"teacher_disagreement_score":0.89795244,"about_ca_system_score_codex":0.00007466675,"about_ca_system_score_gemma":0.000044863496,"threshold_uncertainty_score":0.28542757},"labels":[],"label_agreement":null},{"id":"W2901068648","doi":"10.1109/tpwrs.2018.2882560","title":"A Data-Driven Load Fluctuation Model for Multi-Region Power Systems","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"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 Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electric power system; Probabilistic logic; A priori and a posteriori; Computer science; Load management; Gaussian; Random variable; Power demand; Power (physics); Mathematics; Statistics; Engineering; Artificial intelligence","score_opus":0.0676911872754359,"score_gpt":0.2715237721997845,"score_spread":0.20383258492434864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901068648","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":[0.0054416703,0.00034516,0.9814494,0.000013408556,0.009462103,0.00074489514,0.0005252754,0.00069925375,0.0013187855],"genre_scores_gemma":[0.99602675,0.000020436017,0.0015213627,0.000022837292,0.00008232536,0.00022777659,0.000042806943,0.000121766214,0.0019339162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980675,0.000047491114,0.0005651266,0.000496575,0.00034832288,0.000475011],"domain_scores_gemma":[0.99849856,0.0000821997,0.000096960444,0.000921204,0.00024834546,0.00015272338],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029069165,0.0003553295,0.00036352017,0.0002024341,0.00027613403,0.0001537037,0.00041972267,0.00024766263,0.000013832504],"category_scores_gemma":[0.000009355202,0.00034955208,0.00012090871,0.00023561636,0.000059087088,0.0004998434,0.0000028960364,0.00021719196,0.000120274446],"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":[0.00004112352,0.00006967322,0.000002674818,0.00014601093,0.00015412612,0.0000028151146,0.0013570886,0.9928108,0.0014194648,0.0002047747,0.0036092072,0.00018225111],"study_design_scores_gemma":[0.0007831371,0.00013447172,0.0000027758454,0.00028782384,0.000059319205,0.000042340045,0.00019307742,0.9862404,0.00058234634,0.0000038244184,0.011284519,0.000385984],"about_ca_topic_score_codex":0.000106003376,"about_ca_topic_score_gemma":0.00018063954,"teacher_disagreement_score":0.9905851,"about_ca_system_score_codex":0.00025047924,"about_ca_system_score_gemma":0.000071714436,"threshold_uncertainty_score":0.99989563},"labels":[],"label_agreement":null},{"id":"W2901542073","doi":"10.1109/access.2018.2879965","title":"Oil Consumption Forecasting Using Optimized Adaptive Neuro-Fuzzy Inference System Based on Sine Cosine Algorithm","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":51,"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":"Adaptive neuro fuzzy inference system; Computer science; Inference system; Algorithm; Data mining; Oil consumption; Consumption (sociology); Fuzzy logic; Convergence (economics); Artificial intelligence; Sine; Machine learning; Fuzzy control system; Mathematics; Engineering; Economics","score_opus":0.07391811423364616,"score_gpt":0.2860097770229416,"score_spread":0.21209166278929545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901542073","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":[0.46826664,0.00006839289,0.51580703,0.000010823014,0.002913788,0.00011647293,0.000054472497,0.0009483345,0.011814054],"genre_scores_gemma":[0.97553045,0.000008990811,0.02350351,0.00009654705,0.00071949686,0.00001942456,0.000017298262,0.00007610441,0.000028208407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985105,0.00006290969,0.00038091218,0.0003325438,0.00026477428,0.00044838525],"domain_scores_gemma":[0.9989339,0.00034090312,0.00012604642,0.00029798012,0.00017613902,0.00012501572],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020215906,0.00031861468,0.00032189878,0.00020991186,0.00022201796,0.00018768478,0.000320401,0.00012511307,0.00003603054],"category_scores_gemma":[0.000058887013,0.0003172024,0.00006936495,0.00035785447,0.00008502274,0.00044651382,0.000046329624,0.00024041702,0.000030500041],"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":[0.00007751489,0.000025419884,0.00022618387,0.00022077902,0.00003497399,0.000046134108,0.00007296924,0.9562602,0.0021721974,0.000083017425,0.00009396103,0.040686656],"study_design_scores_gemma":[0.00088879454,0.00008889688,0.000054716897,0.0010784157,0.000034015782,0.000023932169,0.000012048197,0.9845867,0.012785532,0.000007466528,0.00010722334,0.00033226705],"about_ca_topic_score_codex":0.0001079885,"about_ca_topic_score_gemma":0.000017913208,"teacher_disagreement_score":0.5072638,"about_ca_system_score_codex":0.0001619977,"about_ca_system_score_gemma":0.00003649984,"threshold_uncertainty_score":0.999928},"labels":[],"label_agreement":null},{"id":"W2906329242","doi":"10.1109/pesgm.2018.8586486","title":"A Hybrid Probabilistic Wind Power Prediction Based on An Improved Decomposition Technique and Kernel Density Estimation","year":2018,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Kernel density estimation; Wind power; Probabilistic logic; Computer science; Algorithm; Entropy (arrow of time); Extreme learning machine; Numerical weather prediction; Hilbert–Huang transform; Mathematics; Artificial intelligence; Statistics; Engineering; White noise; Artificial neural network; Meteorology","score_opus":0.005703293637637131,"score_gpt":0.2151490160786441,"score_spread":0.20944572244100695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906329242","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":[0.659061,0.0000025106208,0.33617514,0.000010619442,0.00014495237,0.00020387296,0.0000073010015,0.00048013337,0.0039144326],"genre_scores_gemma":[0.9823196,5.093796e-7,0.017460587,0.000048711994,0.00007132954,0.00001786351,0.00004656793,0.0000212609,0.000013606415],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945223,0.000019847828,0.0001384102,0.00017518096,0.000077206394,0.00013711036],"domain_scores_gemma":[0.9996814,0.000027996595,0.000021292286,0.00015184877,0.00004820748,0.00006924591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014720365,0.00012197355,0.00008452496,0.00007492232,0.00008946698,0.0000416907,0.000037409965,0.00006077239,0.000035163666],"category_scores_gemma":[0.00002530257,0.00011606294,0.00001701305,0.00005883115,0.000037235528,0.00018573191,0.000008598265,0.000086520355,0.0000051956936],"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":[0.00040802013,0.0004486726,0.004690389,0.00040520087,0.00007574551,0.000019022951,0.00075309636,0.3631574,0.57906765,0.0034240244,0.0007316554,0.046819154],"study_design_scores_gemma":[0.00018900576,0.00037430643,0.00421405,0.000055649063,0.000009739456,0.000018742125,0.0000031259792,0.93250173,0.061930906,0.00054335187,0.000039969236,0.00011943895],"about_ca_topic_score_codex":0.000023258954,"about_ca_topic_score_gemma":0.000023278439,"teacher_disagreement_score":0.56934434,"about_ca_system_score_codex":0.000055727618,"about_ca_system_score_gemma":0.000009502792,"threshold_uncertainty_score":0.47329092},"labels":[],"label_agreement":null},{"id":"W2906865296","doi":"10.3390/en12010149","title":"Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting","year":2019,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"United Arab Emirates University","keywords":"Computer science; Deep learning; Artificial intelligence; Autocorrelation; Benchmark (surveying); Recurrent neural network; Artificial neural network; Time series; Boosting (machine learning); Autoregressive integrated moving average; Machine learning; Sequence (biology); Autoregressive model; Sequence learning; Term (time); Econometrics; Mathematics; Statistics","score_opus":0.03839637829722571,"score_gpt":0.22422530752158232,"score_spread":0.18582892922435662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2906865296","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":[0.98295826,0.004355561,0.010667253,0.000007025747,0.00022035716,0.000101826154,0.0000017762561,0.00024859214,0.0014393699],"genre_scores_gemma":[0.9913027,0.00022569962,0.008001764,0.000015098665,0.00009205847,0.000022680082,0.0000078933635,0.000046695524,0.0002853549],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916285,0.000010447566,0.00016704496,0.00022014252,0.00010394077,0.00033557432],"domain_scores_gemma":[0.99962497,0.00015869139,0.00002413911,0.00009142265,0.000036814545,0.00006398059],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013715545,0.0001765069,0.00018828725,0.00007795545,0.00009933443,0.000070002534,0.00007293726,0.00008136976,0.0000051404877],"category_scores_gemma":[0.000052771476,0.00017380154,0.00003109555,0.00009805378,0.000025141055,0.0002839481,0.000041835436,0.00013768808,9.1930184e-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":[0.000008783287,0.0000067434544,0.0026760472,0.00015701076,0.000030111969,0.00000358272,0.0009308185,0.82908785,0.1060872,0.00029936977,0.000009836315,0.060702655],"study_design_scores_gemma":[0.0002652811,0.00007725324,0.00023199878,0.000078954865,0.000013763585,0.000027780623,0.00010000134,0.9789737,0.019095344,0.00015050564,0.0007443011,0.00024115016],"about_ca_topic_score_codex":0.000012607689,"about_ca_topic_score_gemma":0.000034242952,"teacher_disagreement_score":0.14988582,"about_ca_system_score_codex":0.00004299714,"about_ca_system_score_gemma":0.000011209657,"threshold_uncertainty_score":0.70874214},"labels":[],"label_agreement":null},{"id":"W2908412753","doi":"10.22109/jemt.2018.126045.1077","title":"Improved Forecasting of Short Term Electricity Demand by using of Integrated Data Preparation and Input Selection Methods","year":2019,"lang":"en","type":"article","venue":"Journal of Energy Management and Technology","topic":"Energy Load and Power Forecasting","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":"Autoregressive integrated moving average; Normalization (sociology); Nonlinear autoregressive exogenous model; Parametric statistics; Selection (genetic algorithm); Term (time); Autoregressive model; Computer science; Model selection; Statistics; Econometrics; Time series; Mathematics; Artificial intelligence","score_opus":0.01825321272563487,"score_gpt":0.267966987677896,"score_spread":0.24971377495226113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908412753","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":[0.82759786,0.0011174878,0.17077668,0.0000071459067,0.00008869756,0.000044238575,0.0000021343346,0.000022927863,0.0003428239],"genre_scores_gemma":[0.9735872,0.00047289388,0.025863709,0.0000025300424,0.000014782759,7.48252e-7,0.000008083864,0.0000110376,0.00003897071],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992813,0.000025442203,0.00039495362,0.000114873015,0.000064024454,0.00011944087],"domain_scores_gemma":[0.99955374,0.000034192657,0.00020037357,0.000121918434,0.000066990935,0.000022760596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038292073,0.00010100512,0.00025388206,0.00043060616,0.000024376313,0.000010187259,0.00013482156,0.000106237865,0.000003089812],"category_scores_gemma":[0.000029862906,0.00008987013,0.000017770612,0.00038340682,0.00003255806,0.00019420651,0.00009731841,0.00012161576,8.476182e-9],"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":[0.000092588736,0.000049751405,0.020626422,0.0003683402,0.00064766855,0.0000040823406,0.00005400025,0.011436325,0.66115683,0.0016715083,0.00015271695,0.3037398],"study_design_scores_gemma":[0.00039411,0.00030709113,0.0001315423,0.00011485694,0.00012416339,0.0000829614,0.000048344245,0.81554615,0.1812672,0.0003643858,0.0015042253,0.00011495222],"about_ca_topic_score_codex":0.000015143876,"about_ca_topic_score_gemma":0.000011954866,"teacher_disagreement_score":0.8041098,"about_ca_system_score_codex":0.000024877576,"about_ca_system_score_gemma":0.000010890208,"threshold_uncertainty_score":0.36647975},"labels":[],"label_agreement":null},{"id":"W2909688863","doi":"10.2174/2352096512666190118160604","title":"Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market","year":2019,"lang":"en","type":"article","venue":"Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"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":"Mean absolute percentage error; Artificial neural network; Benchmark (surveying); Mean squared error; Wavelet transform; Metric (unit); Computer science; Electricity price forecasting; Statistics; Wind power; Regression; Econometrics; Electricity; Wavelet; Artificial intelligence; Electricity market; Mathematics; Engineering; Geography; Operations management","score_opus":0.00942326679443822,"score_gpt":0.22042979430716478,"score_spread":0.21100652751272655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909688863","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":[0.9248533,0.048565477,0.019095637,0.00010716174,0.0016111563,0.0032425995,0.000019710145,0.0012346959,0.0012702161],"genre_scores_gemma":[0.95461166,0.040318623,0.002920532,0.00015288325,0.00054658955,0.00026461945,0.00014088397,0.00060536986,0.00043883288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9885841,0.00009258028,0.0017276214,0.0016445406,0.0010181101,0.006933064],"domain_scores_gemma":[0.9975088,0.00076391496,0.0002828501,0.0006302949,0.00019180762,0.0006222956],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0009933796,0.0018084414,0.0017597342,0.001060512,0.00025886152,0.00017083592,0.00076700596,0.0007911459,0.00014294533],"category_scores_gemma":[0.00025244782,0.0017975342,0.00044029127,0.004034664,0.00004292912,0.0010246845,0.000071399314,0.0040262234,0.0000074725745],"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":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077554624,0.00023901077,0.0021620335,0.00023297388,0.00029152728,0.000019115432,0.000079018915,0.863919,0.0050414205,0.0031830291,0.00016219796,0.12389508],"study_design_scores_gemma":[0.0030026939,0.0028940297,0.0008082987,0.0003662013,0.00010817565,0.00018148718,0.0000027697063,0.89494145,0.0029973583,0.0006587149,0.09207608,0.001962723],"about_ca_topic_score_codex":0.0000718607,"about_ca_topic_score_gemma":0.00028639537,"teacher_disagreement_score":0.12193236,"about_ca_system_score_codex":0.0066314614,"about_ca_system_score_gemma":0.00052942964,"threshold_uncertainty_score":0.99946606},"labels":[],"label_agreement":null},{"id":"W2910014246","doi":"10.1016/j.renene.2019.01.049","title":"Prediction of wind power ramp events based on residual correction","year":2019,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Residual; Wind power forecasting; Wind power; Markov chain; Wind speed; Computer science; Numerical weather prediction; Power (physics); Stability (learning theory); Term (time); Electric power system; Control theory (sociology); Reliability engineering; Engineering; Meteorology; Algorithm; Artificial intelligence; Machine learning","score_opus":0.007826135737354257,"score_gpt":0.1790574217490179,"score_spread":0.17123128601166365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910014246","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":[0.680467,0.00013313122,0.013665015,0.000016438162,0.008022878,0.00009592361,0.000032088494,0.00047010023,0.29709744],"genre_scores_gemma":[0.9948929,0.000017604349,0.00011783557,0.000035900222,0.00012369077,0.0000036883266,0.000057315978,0.00003637429,0.004714721],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992072,0.000026712207,0.00020372497,0.0001577911,0.00020792785,0.00019665719],"domain_scores_gemma":[0.9995823,0.00006219069,0.00004558963,0.00023031498,0.000029462482,0.000050175982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009832321,0.00013287156,0.00015324897,0.0001371127,0.00002869249,0.000007722901,0.0000786371,0.00011343259,0.0003605845],"category_scores_gemma":[0.000016133261,0.00013492834,0.00005485319,0.00019366088,0.000008227177,0.00008820631,0.000010530333,0.00007251592,0.000013102618],"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":[0.000026824959,0.000023867464,0.0016451301,0.000017403247,0.000017903652,7.5121653e-7,0.000021537066,0.9782147,0.01671326,0.00005410464,0.0025919874,0.00067253294],"study_design_scores_gemma":[0.0007848305,0.0003278077,0.002090791,0.00024617283,0.000014402445,0.0000040506748,0.000029376974,0.7054863,0.24989307,0.000082934625,0.040823065,0.00021718803],"about_ca_topic_score_codex":0.0004687652,"about_ca_topic_score_gemma":0.000051877756,"teacher_disagreement_score":0.3144259,"about_ca_system_score_codex":0.00006128501,"about_ca_system_score_gemma":0.00002289124,"threshold_uncertainty_score":0.5502218},"labels":[],"label_agreement":null},{"id":"W2910062001","doi":"10.3390/en12020218","title":"Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model","year":2019,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":38,"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 Regina","funders":"National Science and Technology Major Project; China Scholarship Council; National Natural Science Foundation of China","keywords":"Principal component analysis; Support vector machine; Natural gas; Artificial neural network; Computer science; Artificial intelligence; Principal component regression; Component (thermodynamics); Extreme learning machine; Machine learning; Regression; Data mining; Statistics; Mathematics; Engineering","score_opus":0.00860401458586983,"score_gpt":0.1847411509908789,"score_spread":0.17613713640500908,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910062001","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":[0.9325255,0.00048603263,0.0032500501,0.000017118507,0.0006952975,0.000049360257,0.000002092313,0.0006558078,0.062318753],"genre_scores_gemma":[0.99452484,0.000015691976,0.003640293,0.00008161431,0.00014266788,0.000010915752,0.00002346184,0.00007239208,0.0014881132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988598,0.000021570202,0.00019552036,0.00023501029,0.00026911657,0.0004189889],"domain_scores_gemma":[0.99949795,0.00014784781,0.000039717917,0.00021196195,0.000040575786,0.00006195635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014777893,0.000243224,0.00020481642,0.00010576819,0.00009840795,0.00006646016,0.00016203949,0.000055619294,0.000060821018],"category_scores_gemma":[0.00007185534,0.00023367436,0.00009885055,0.00012763281,0.000018150507,0.00017286863,0.000035959332,0.00037457392,0.00006613275],"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":[0.0000146067805,0.0000055006094,0.00037976037,0.0000424137,0.000014437517,0.000011015394,0.0002038522,0.9761487,0.0027648348,0.00023263368,0.0001419814,0.020040259],"study_design_scores_gemma":[0.00034952734,0.000037476857,0.000035814217,0.00012882196,0.000007432802,0.00000794468,0.00004451088,0.98337495,0.013855189,0.00011020278,0.0017576489,0.0002904748],"about_ca_topic_score_codex":0.000016740518,"about_ca_topic_score_gemma":0.000016275659,"teacher_disagreement_score":0.061999362,"about_ca_system_score_codex":0.00009424031,"about_ca_system_score_gemma":0.00002502204,"threshold_uncertainty_score":0.95289636},"labels":[],"label_agreement":null},{"id":"W2913952550","doi":"10.1002/ese3.272","title":"Structure dependent weather normalization","year":2019,"lang":"en","type":"article","venue":"Energy Science & Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Normalization (sociology); Meteorology; Computer science; Geology; Artificial intelligence; Environmental science; Geography","score_opus":0.0024467237366858777,"score_gpt":0.16018103269116332,"score_spread":0.15773430895447743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913952550","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":[0.9364368,0.0003141265,0.046910346,0.0000066257235,0.0023576147,0.00004037166,0.0000049716136,0.00058665534,0.013342499],"genre_scores_gemma":[0.9982176,0.00001275142,0.0011976601,0.000021596,0.00013924696,0.0000031254726,0.000005197927,0.00003439957,0.0003684267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896926,0.0000030346266,0.0001399336,0.00020489255,0.00029114634,0.00039171966],"domain_scores_gemma":[0.9996127,0.000013732887,0.00001605439,0.00022137823,0.000032675718,0.00010348531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010911321,0.00015733337,0.00010782507,0.00020215349,0.00005843994,0.00007654443,0.0002820159,0.000057811125,0.00015968134],"category_scores_gemma":[0.000015207138,0.00015399599,0.00002851375,0.0005243907,0.0000237399,0.00047045152,0.000043278847,0.0001074107,0.000021639022],"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":[3.1557553e-7,0.0000011031168,0.0003160442,0.0000022690035,0.0000033312524,0.0000012115652,0.00006831911,0.70733094,0.20879394,0.082974456,0.0000072312387,0.00050081336],"study_design_scores_gemma":[0.00016221311,0.000017803939,0.00086026534,0.00004402264,0.0000044125345,0.00002360339,0.0000214039,0.55425364,0.39160433,0.00006261268,0.05256566,0.0003800545],"about_ca_topic_score_codex":0.000017355005,"about_ca_topic_score_gemma":0.000009290414,"teacher_disagreement_score":0.18281038,"about_ca_system_score_codex":0.00009636537,"about_ca_system_score_gemma":0.00002371138,"threshold_uncertainty_score":0.62797743},"labels":[],"label_agreement":null},{"id":"W2915629347","doi":"10.1109/tpwrs.2018.2872822","title":"Very Short-Term Wind Power Prediction Interval Framework via Bi-Level Optimization and Novel Convex Cost Function","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":38,"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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematical optimization; Benchmark (surveying); Prediction interval; Term (time); Electric power system; Interval (graph theory); Computer science; Minification; Wind power; Convex optimization; Differentiable function; Hyperparameter; Function (biology); Operator (biology); Power (physics); Engineering; Mathematics; Regular polygon; Artificial intelligence; Machine learning","score_opus":0.01623981703473089,"score_gpt":0.20754074978593282,"score_spread":0.1913009327512019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915629347","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":[0.07993054,0.0001537412,0.9035509,0.000009624719,0.0138375005,0.00046324608,0.00014934712,0.00042936794,0.0014757251],"genre_scores_gemma":[0.99876654,0.00003801961,0.00051970524,0.00003500112,0.000066335284,0.00004615419,0.000027781582,0.00009085612,0.000409587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985138,0.000036458136,0.00046268568,0.00037423457,0.00028260532,0.00033020953],"domain_scores_gemma":[0.9992821,0.000101033096,0.000058234633,0.00033952636,0.000078487654,0.00014058531],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017557283,0.00032628185,0.00032041667,0.00026134978,0.00014903978,0.00013554926,0.00010688262,0.0003564803,0.00025877668],"category_scores_gemma":[0.0000029228572,0.00033939857,0.00010581779,0.00026841095,0.00003801495,0.00048549077,0.0000020684354,0.00046690402,0.000068143374],"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":[0.0000695545,0.000074922995,0.00059357303,0.000111213965,0.00018679451,0.0000019953145,0.00045329818,0.99206513,0.005591446,0.00008256797,0.00007779793,0.00069167593],"study_design_scores_gemma":[0.0012429575,0.0006158069,0.0023923062,0.0012268753,0.00014681688,0.00016170651,0.00040123044,0.9868848,0.0033258519,0.000010307253,0.0027846915,0.00080665253],"about_ca_topic_score_codex":0.000026426906,"about_ca_topic_score_gemma":0.000007981329,"teacher_disagreement_score":0.918836,"about_ca_system_score_codex":0.00014431968,"about_ca_system_score_gemma":0.00001716663,"threshold_uncertainty_score":0.9999058},"labels":[],"label_agreement":null},{"id":"W2919322070","doi":"10.1109/repe.2018.8657666","title":"Wind Power Prediction Based on Recurrent Neural Network with Long Short-Term Memory Units","year":2018,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":42,"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; University of Toronto","funders":"","keywords":"Recurrent neural network; Wind power; Computer science; Artificial neural network; Renewable energy; Term (time); Chaotic; Wind power forecasting; Time series; Wind speed; Power (physics); Nonlinear system; Long short term memory; Artificial intelligence; Electric power system; Machine learning; Meteorology; Engineering; Electrical engineering","score_opus":0.015187763306048516,"score_gpt":0.20667216493836976,"score_spread":0.19148440163232125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919322070","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":[0.90292877,0.000046772926,0.0044252696,0.000017821012,0.0018635005,0.000105662075,0.0000056964136,0.0005442248,0.0900623],"genre_scores_gemma":[0.998611,0.0000024799817,0.00026273954,0.00008781643,0.00072718825,0.000004134988,0.000032121017,0.000039720966,0.00023284409],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991994,0.000018330858,0.00014556735,0.00016924557,0.00016417756,0.0003032788],"domain_scores_gemma":[0.9996,0.000034940844,0.00001421686,0.00020158495,0.00005502151,0.00009424933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008726406,0.00017189074,0.00010963206,0.00005609245,0.00009068442,0.000033268578,0.000082724946,0.00006483015,0.00026268905],"category_scores_gemma":[0.000005909217,0.00013525964,0.000023546805,0.00027314422,0.000037509293,0.000105741296,0.000012259901,0.0001668477,0.000018031484],"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":[0.00007856553,0.00002492762,0.032896496,0.000024695102,0.000040571682,0.000021135551,0.00018748475,0.94494945,0.00011972833,0.00007754666,0.0041893534,0.017390031],"study_design_scores_gemma":[0.00049908535,0.00082058704,0.058714136,0.0003579755,0.000031382784,0.000018886176,0.000027859096,0.93390095,0.0027982087,0.0000043265704,0.0024323135,0.00039430163],"about_ca_topic_score_codex":0.0000040379887,"about_ca_topic_score_gemma":0.000072116905,"teacher_disagreement_score":0.0956822,"about_ca_system_score_codex":0.000035067278,"about_ca_system_score_gemma":0.000013383311,"threshold_uncertainty_score":0.5515728},"labels":[],"label_agreement":null},{"id":"W2919719413","doi":"10.11575/prism/32986","title":"Forecasting of Wind Energy Generation in Alberta","year":2018,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Energy Load and Power Forecasting","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":"Wind power; Meteorology; Environmental science; Climatology; Engineering; Geography; Geology; Electrical engineering","score_opus":0.011371737940941188,"score_gpt":0.1795056093871324,"score_spread":0.1681338714461912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919719413","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":[0.88920414,0.00033868142,0.0075141517,0.0000048589463,0.0005123979,0.000065527245,9.284763e-7,0.000028334005,0.10233095],"genre_scores_gemma":[0.97125185,0.00024373994,0.01432062,0.0000046312093,0.00010393988,3.7338063e-7,0.0011113149,0.000062498555,0.012901045],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999284,0.000018747318,0.00020861502,0.00016973735,0.00014950849,0.00016934949],"domain_scores_gemma":[0.99955964,0.00003682879,0.00015175431,0.00015070412,0.000057671325,0.000043405413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007153458,0.00016656955,0.00030630245,0.000286695,0.00004399208,0.0000044773774,0.00018426907,0.00028583236,0.00010982156],"category_scores_gemma":[0.000017803075,0.00023002869,0.00009482493,0.00017488199,0.000031500214,0.00013198613,0.000021428268,0.0001444046,0.00000229325],"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":[0.00023106186,0.00015399784,0.0011408378,0.0019164456,0.00049661007,0.00008813636,0.02589778,0.005726618,0.017852468,0.00438842,0.006296398,0.9358112],"study_design_scores_gemma":[0.0004744988,0.00006389118,0.00078071305,0.00042239437,0.0000677564,0.000002268861,0.0001445349,0.97470766,0.011463453,0.00016437747,0.011357292,0.00035115113],"about_ca_topic_score_codex":0.0051669693,"about_ca_topic_score_gemma":0.0076083275,"teacher_disagreement_score":0.968981,"about_ca_system_score_codex":0.000047258483,"about_ca_system_score_gemma":0.00003972848,"threshold_uncertainty_score":0.93802977},"labels":[],"label_agreement":null},{"id":"W2921060539","doi":"10.1016/j.egypro.2018.12.044","title":"Renewable Energies Generation and Carbon Dioxide Emission Forecasting in Microgrids and National Grids using GRNN-GWO Methodology","year":2019,"lang":"en","type":"article","venue":"Energy Procedia","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":94,"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":"Renewable energy; Microgrid; Greenhouse gas; Environmental science; Electricity generation; Environmental economics; Environmental engineering; Engineering; Power (physics); Economics","score_opus":0.047441673588246164,"score_gpt":0.2446719266844879,"score_spread":0.19723025309624173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2921060539","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":[0.99320585,0.0023086814,0.0010359101,0.000013180524,0.0004663591,0.000045999994,0.0000025424033,0.00009483959,0.0028266432],"genre_scores_gemma":[0.9856382,0.00026752753,0.013330428,0.00003562547,0.00041335967,0.000012765359,0.000028779195,0.000042679505,0.00023061312],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892676,0.000052936128,0.00027919968,0.0002962606,0.00014578055,0.00029905245],"domain_scores_gemma":[0.99960345,0.0001408922,0.000053079788,0.00008164114,0.000048308364,0.00007260451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003637565,0.0002035399,0.00023861416,0.00017905442,0.000060476774,0.000036476493,0.00006256,0.00016652909,0.0000060629127],"category_scores_gemma":[0.00010194587,0.00020810582,0.00002292722,0.00021325868,0.000031097923,0.00019948179,0.000064021806,0.00012460923,1.9292709e-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":[0.000012128386,0.000007927623,0.015277008,0.000097912605,0.000021199518,0.0000055173473,0.0004370339,0.381558,0.59715086,0.0006531175,0.000046144814,0.0047331466],"study_design_scores_gemma":[0.00046588795,0.000030455763,0.0005834751,0.0001253812,0.000011450268,0.00010301868,0.00008139211,0.8126551,0.18370524,0.00057308195,0.0013418089,0.00032372354],"about_ca_topic_score_codex":0.00058399554,"about_ca_topic_score_gemma":0.00038122333,"teacher_disagreement_score":0.4310971,"about_ca_system_score_codex":0.00007802858,"about_ca_system_score_gemma":0.000043996537,"threshold_uncertainty_score":0.8486309},"labels":[],"label_agreement":null},{"id":"W2932672099","doi":"10.1109/cjece.2018.2876820","title":"A Probabilistic Approach Considering Contingency Parameters for Peak Load Demand Forecasting","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Probabilistic forecasting; Probabilistic logic; Contingency; Contingency table; Sensitivity (control systems); Demand forecasting; Monte Carlo method; Computer science; Bayesian probability; Electric power system; Probability distribution; Econometrics; Mathematical optimization; Power (physics); Statistics; Operations research; Engineering; Mathematics; Machine learning; Artificial intelligence","score_opus":0.016640664080470328,"score_gpt":0.17614426140462638,"score_spread":0.15950359732415606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2932672099","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":[0.4681107,0.0015572014,0.5291042,0.000013954959,0.000658603,0.00012706412,0.0000029008106,0.000051367842,0.0003740379],"genre_scores_gemma":[0.9555196,0.000008647956,0.04374842,0.000028647126,0.00064821483,0.0000055205273,0.0000010242139,0.000033356282,0.0000065414606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890065,0.000009153484,0.00036511823,0.00013547184,0.00009168325,0.0004978998],"domain_scores_gemma":[0.999068,0.00021061917,0.000058402573,0.00007143176,0.00015343432,0.00043809158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024316672,0.00018511586,0.00028302943,0.00020990486,0.00010814147,0.000101733625,0.00012844004,0.0000753743,0.0000025084519],"category_scores_gemma":[0.00018547902,0.00017849726,0.00008359458,0.00020707712,0.000042204596,0.000117532494,0.000009663378,0.00022169035,3.5260697e-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":[0.00003400402,0.000022885319,0.0015587477,0.0006625104,0.00046705102,0.00014398996,0.0023622385,0.8853248,0.0012527762,0.0041340874,0.0017772423,0.1022597],"study_design_scores_gemma":[0.000353806,0.0002404164,0.00025634078,0.000137557,0.000032301312,0.00055904384,0.000006447666,0.9944567,0.0002951106,0.0001893411,0.0032454606,0.00022749152],"about_ca_topic_score_codex":0.000057263074,"about_ca_topic_score_gemma":0.00014869095,"teacher_disagreement_score":0.4874089,"about_ca_system_score_codex":0.0001163005,"about_ca_system_score_gemma":0.00014103175,"threshold_uncertainty_score":0.7278907},"labels":[],"label_agreement":null},{"id":"W2937710912","doi":"10.1109/tii.2018.2861390","title":"An Online-Calibrated Time Series Based Model for Day-Ahead Natural Gas Demand Forecasting","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Time series; Computer science; Series (stratigraphy); Process (computing); Stage (stratigraphy); Data mining; Econometrics; Machine learning; Mathematics","score_opus":0.06450413581611357,"score_gpt":0.2464944786804331,"score_spread":0.18199034286431953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2937710912","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":[0.15588245,0.0000041374064,0.840883,0.000030665025,0.0014356495,0.00030379827,0.00038037307,0.00054682523,0.0005330907],"genre_scores_gemma":[0.97668487,0.000002880721,0.022162654,0.00012502672,0.00053102727,0.000037221587,0.00014517448,0.00006531151,0.00024582812],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859655,0.000024100458,0.0006449342,0.00011457847,0.00018695889,0.00043285108],"domain_scores_gemma":[0.99922675,0.00015132337,0.00009308545,0.00025255632,0.00012112466,0.00015513608],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023253015,0.00029336478,0.00027025142,0.00020635877,0.00033938157,0.00013318642,0.00020114446,0.00031188712,0.000051415987],"category_scores_gemma":[0.000019917366,0.00028558847,0.00011092407,0.0003151616,0.000084410334,0.0010944912,0.0000011160434,0.00046191702,0.000017135912],"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":[0.000117715856,0.00004198594,0.000001845417,0.00003717566,0.00004787748,4.2072907e-7,0.0008852883,0.98144513,0.00062670745,0.000014686133,0.0007073459,0.016073845],"study_design_scores_gemma":[0.0011668819,0.0003025387,3.9884122e-7,0.00012250169,0.000052251286,0.000007647584,0.00012200948,0.9651746,0.032000743,0.00004179361,0.00068667566,0.00032193353],"about_ca_topic_score_codex":0.000007281217,"about_ca_topic_score_gemma":0.00009147673,"teacher_disagreement_score":0.82080245,"about_ca_system_score_codex":0.00007341407,"about_ca_system_score_gemma":0.0000962478,"threshold_uncertainty_score":0.99995965},"labels":[],"label_agreement":null},{"id":"W2946412472","doi":"10.1016/j.eswax.2019.100006","title":"International roughness index prediction based on multigranularity fuzzy time series and particle swarm optimization","year":2019,"lang":"en","type":"article","venue":"Expert Systems with Applications X","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":27,"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":"Natural Science Foundation of Shaanxi Province; Chang'an University","keywords":"Particle swarm optimization; Fuzzy logic; Computer science; Artificial neural network; Data mining; Autoregressive model; Backpropagation; Time series; Mean squared error; Artificial intelligence; Machine learning; Mathematics; Statistics","score_opus":0.005057659583565095,"score_gpt":0.19431842516684897,"score_spread":0.18926076558328386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946412472","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":[0.086580105,0.0003892519,0.8832699,0.0002456984,0.0008824772,0.0015583327,0.000117993455,0.0011713739,0.025784848],"genre_scores_gemma":[0.9958769,0.000016513935,0.0029425754,0.00002386605,0.00015842971,0.0005365421,0.0001255063,0.000027079306,0.00029255886],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993706,0.00001577155,0.00016169611,0.00018150102,0.00015243283,0.000118014716],"domain_scores_gemma":[0.99961936,0.000031219734,0.00003642079,0.00020850805,0.000051500152,0.00005297507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007144466,0.00011756762,0.00010623858,0.000051557585,0.00007531127,0.00006725874,0.00007795953,0.000062405576,0.000023350007],"category_scores_gemma":[0.000003470397,0.00010528456,0.000014969309,0.00014138367,0.000017699585,0.00020653625,0.0000093340295,0.00006937046,0.00003125737],"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":[0.000026688449,0.000025074474,0.00483869,0.000028584673,0.00001752127,2.8332596e-7,0.00018008963,0.99304223,0.0008211196,0.00049010647,0.00010117465,0.00042841883],"study_design_scores_gemma":[0.00041160238,0.000038365426,0.0005727882,0.00007005023,0.000003709982,0.0000059092768,0.00008928889,0.9812702,0.0009933101,0.0000033951635,0.016419388,0.00012197358],"about_ca_topic_score_codex":0.00002972359,"about_ca_topic_score_gemma":0.0000025082068,"teacher_disagreement_score":0.9092968,"about_ca_system_score_codex":0.00004799572,"about_ca_system_score_gemma":0.000008682039,"threshold_uncertainty_score":0.42933798},"labels":[],"label_agreement":null},{"id":"W2947269208","doi":"10.4236/epe.2019.115015","title":"Eco-Friendly Selection of Diesel Generator Based on Genset-Synchro Technology for Off-Grid Remote Area Application in the North of Quebec","year":2019,"lang":"en","type":"article","venue":"Energy and Power Engineering","topic":"Energy Load and Power Forecasting","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":"Cégep de Rimouski; Université du Québec à Rimouski","funders":"","keywords":"Automotive engineering; Diesel generator; Stator; Rotor (electric); Electricity generation; Generator (circuit theory); Electricity; Engineering; Fuel efficiency; Diesel fuel; Greenhouse gas; Electric generator; Electrical engineering; Power (physics)","score_opus":0.0032514953059934073,"score_gpt":0.1701007763981911,"score_spread":0.16684928109219768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2947269208","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":[0.9106337,0.00044198145,0.088004075,0.000021788941,0.0002600287,0.00012476394,0.000018150498,0.00008641641,0.0004091341],"genre_scores_gemma":[0.9980273,0.000050066134,0.0017410541,0.000017141563,0.00005231617,0.000027118935,0.00002979978,0.000027225607,0.000027977607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993397,0.000008625899,0.00023318472,0.00014992178,0.000084685904,0.00018388807],"domain_scores_gemma":[0.9996026,0.000110093046,0.000047520978,0.00018500375,0.00003312038,0.000021660218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000110471876,0.00014864196,0.00019213212,0.00023744057,0.000021583326,0.0000063705074,0.00010804418,0.000111043206,0.0000057383368],"category_scores_gemma":[0.000019437972,0.000129705,0.000042778753,0.00035149994,0.000015073198,0.00004887204,0.00000993819,0.00010220901,3.6931732e-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":[0.000021602917,0.000016939686,0.002135444,0.000116702984,0.00002379341,5.289795e-7,0.00011716392,0.96582943,0.018162232,0.0034396772,0.000070615875,0.010065844],"study_design_scores_gemma":[0.00035320237,0.00013902203,0.0016223646,0.000075934426,0.000009177057,0.0000033853782,0.000022640772,0.93603545,0.036287483,0.000018561685,0.025263809,0.00016897667],"about_ca_topic_score_codex":0.000109591965,"about_ca_topic_score_gemma":0.0010791471,"teacher_disagreement_score":0.087393634,"about_ca_system_score_codex":0.000028429638,"about_ca_system_score_gemma":0.000014633597,"threshold_uncertainty_score":0.52892166},"labels":[],"label_agreement":null},{"id":"W2949334868","doi":"10.1145/3307772.3328306","title":"Using Synthetic Traces for Robust Energy System Sizing","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Waterloo","funders":"","keywords":"Sizing; Computer science; Energy (signal processing); Mathematics","score_opus":0.02522486314083878,"score_gpt":0.20368313065509136,"score_spread":0.1784582675142526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949334868","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":[0.3868363,0.0011413413,0.4833294,0.000009384181,0.0021203274,0.00015710529,0.000007764236,0.0011301158,0.12526827],"genre_scores_gemma":[0.9834193,0.0000052943005,0.01571123,0.000010815041,0.00013143294,0.000008187857,0.0000028272386,0.000045356894,0.00066556083],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939597,0.000006946988,0.00015832837,0.00012881136,0.00006778895,0.00024215774],"domain_scores_gemma":[0.99970233,0.00008713113,0.000018973076,0.00013160332,0.000017183002,0.00004276423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008052468,0.000119218,0.0001505637,0.00005599069,0.000046795558,0.000036556594,0.00008300234,0.000061788,0.000040807012],"category_scores_gemma":[0.0000048541556,0.000108818924,0.00006395917,0.00007501325,0.000005702762,0.00010131202,0.000010982513,0.000038822447,0.0000115782395],"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":[0.00000267818,0.000003273246,0.000091278554,0.00028440848,0.000028395041,0.000001234306,0.00006248019,0.96675396,0.012014364,0.016045267,0.000045324723,0.0046673464],"study_design_scores_gemma":[0.00013724109,0.000012660919,0.000003952052,0.00018244567,0.000013376596,0.000017585746,0.00023120378,0.9780462,0.0150764035,0.000013293706,0.006097119,0.00016854626],"about_ca_topic_score_codex":0.000041516883,"about_ca_topic_score_gemma":0.000016295779,"teacher_disagreement_score":0.596583,"about_ca_system_score_codex":0.00005270641,"about_ca_system_score_gemma":0.000006721197,"threshold_uncertainty_score":0.44375068},"labels":[],"label_agreement":null},{"id":"W2952133925","doi":"10.1016/j.petrol.2019.106187","title":"Conventional models and artificial intelligence-based models for energy consumption forecasting: A review","year":2019,"lang":"en","type":"review","venue":"Journal of Petroleum Science and Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":255,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"China Scholarship Council","keywords":"Artificial neural network; Predictive modelling; Mean absolute percentage error; Computer science; Energy consumption; Benchmark (surveying); Model selection; Machine learning; Artificial intelligence; Consumption (sociology); Engineering","score_opus":0.1341996585916335,"score_gpt":0.29777404485135517,"score_spread":0.16357438625972168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952133925","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000015016319,0.71414655,0.28512335,0.00000821785,0.0005197707,0.000086683176,0.000012380176,0.00002197379,0.000066035194],"genre_scores_gemma":[0.0019556105,0.9936151,0.004101641,0.000019194402,0.0002272968,0.00002132688,0.0000074752093,0.000043186417,0.000009157284],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809617,0.000013069235,0.0008877282,0.00023123143,0.0004102657,0.0003615078],"domain_scores_gemma":[0.9989638,0.00023177436,0.00029798457,0.00012370686,0.0001969519,0.00018580146],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012074247,0.00032741763,0.0010350854,0.0005717448,0.000086678534,0.00012061051,0.00025599162,0.00012491347,0.000005065665],"category_scores_gemma":[0.00009408235,0.00027776384,0.00025491184,0.00029166855,0.00008987357,0.0005949623,0.000037504375,0.00029278095,5.1997944e-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":[0.0000030963458,0.0000069113175,1.3212144e-7,0.027883995,0.00004740933,0.0000042484107,0.000011393145,0.49713585,0.000012026987,0.0051118676,0.00004712174,0.46973595],"study_design_scores_gemma":[0.000065663946,0.0000667722,5.2367188e-8,0.032955244,0.00027826126,0.00022381576,0.0000033073266,0.8913225,0.000010203228,0.00037739667,0.07443483,0.00026197912],"about_ca_topic_score_codex":9.613591e-7,"about_ca_topic_score_gemma":5.727715e-7,"teacher_disagreement_score":0.46947396,"about_ca_system_score_codex":0.0001319468,"about_ca_system_score_gemma":0.0002511876,"threshold_uncertainty_score":0.99996746},"labels":[],"label_agreement":null},{"id":"W2952498843","doi":"10.23977/acss.2019.31003","title":"Short-term Electricity Price Forecast of Electricity Market Based on E-BLSTM Model","year":2019,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Energy Load and Power Forecasting","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":"Electricity market; Electricity; Electricity price forecasting; Econometrics; Electricity price; Economics; Autoregressive integrated moving average; Term (time); Computer science; Time series; Engineering","score_opus":0.0110540537708019,"score_gpt":0.2202016851863913,"score_spread":0.2091476314155894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952498843","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":[0.5425124,0.003348944,0.44387978,0.0000026463779,0.00054117537,0.00029188403,0.000011100218,0.00008712637,0.0093249325],"genre_scores_gemma":[0.9982861,0.00024793233,0.001266808,0.000023732671,0.000091339476,0.000015462896,0.000004456135,0.000024624655,0.00003950277],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875635,0.000052603922,0.0004028854,0.00026156806,0.00020451013,0.00032209998],"domain_scores_gemma":[0.9993752,0.00025816823,0.000066703,0.00020097007,0.000039156184,0.000059783648],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031139443,0.00020951568,0.00040248825,0.0001761222,0.000026768099,0.00003311058,0.00015966054,0.00008460333,0.000006115458],"category_scores_gemma":[0.000004692369,0.0001899717,0.000054404463,0.0002827224,0.000014381967,0.00020919897,0.000021929709,0.00017053387,0.000001334103],"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":[0.000028389839,0.000029166004,0.010499597,0.00038928774,0.00001218663,0.0000033688787,0.00004932405,0.9692377,0.00092122937,0.00023923328,0.00005966972,0.018530883],"study_design_scores_gemma":[0.00030369847,0.0001608591,0.0007501841,0.00036726243,0.000004075844,0.0000053802996,0.0000023367206,0.9966055,0.001249381,0.00007237083,0.0002667708,0.00021217925],"about_ca_topic_score_codex":0.000007322318,"about_ca_topic_score_gemma":0.0000052771643,"teacher_disagreement_score":0.45577374,"about_ca_system_score_codex":0.000044026416,"about_ca_system_score_gemma":0.00001332998,"threshold_uncertainty_score":0.7746821},"labels":[],"label_agreement":null},{"id":"W2955045970","doi":"10.22201/fi.25940732e.2019.20n3.032","title":"Métodos numéricos en diferencias finitas para la estimación de recursos de Hardware FPGA en arquitecturas LFSR(n,k) fractales","year":2019,"lang":"es","type":"article","venue":"Ingeniería Investigación y Tecnología","topic":"Energy Load and Power Forecasting","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":"Fractal Systems (Canada)","funders":"","keywords":"Humanities; Physics; Art","score_opus":0.01372416115160926,"score_gpt":0.25169350680641833,"score_spread":0.23796934565480907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955045970","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":[0.96685964,0.0050408817,0.0036169605,0.0009513422,0.0014614656,0.00076274667,0.0002726406,0.001695491,0.019338839],"genre_scores_gemma":[0.97694474,0.0009169901,0.018785514,0.00064547546,0.0010220795,0.00018324656,0.0001408796,0.00039691044,0.0009641807],"study_design_codex":"observational","study_design_gemma":"not_applicable","domain_scores_codex":[0.99356157,0.00087903143,0.0012054441,0.0013040736,0.000659247,0.0023906438],"domain_scores_gemma":[0.9946467,0.002524543,0.00042941852,0.0013595497,0.00015134305,0.0008884233],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0013222087,0.0014724359,0.0013130121,0.00076086103,0.00034865763,0.0005466556,0.0015959129,0.0020908078,0.0006721498],"category_scores_gemma":[0.0029445442,0.0015518033,0.00051116216,0.0009611146,0.00050221937,0.0006123131,0.0005260812,0.0029569417,0.0013183114],"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":[0.00034606873,0.0006729496,0.44032192,0.0042713094,0.003375465,0.0023001193,0.030477524,0.16952917,0.16055162,0.024022773,0.009645693,0.15448537],"study_design_scores_gemma":[0.005949285,0.0019877637,0.14097138,0.00946379,0.0015831225,0.0032914167,0.0022544789,0.15252252,0.2677726,0.024775809,0.37949896,0.009928903],"about_ca_topic_score_codex":0.0004293418,"about_ca_topic_score_gemma":0.00005489071,"teacher_disagreement_score":0.36985326,"about_ca_system_score_codex":0.00087298994,"about_ca_system_score_gemma":0.00072790805,"threshold_uncertainty_score":0.99980253},"labels":[],"label_agreement":null},{"id":"W2955529518","doi":"10.1016/j.renene.2019.06.047","title":"Chaotic wind power time series prediction via switching data-driven modes","year":2019,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":79,"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":"Intermittency; Wind power; Markov chain; Chaotic; Series (stratigraphy); Time series; Wind speed; Computer science; Power (physics); Hidden Markov model; Data mining; Engineering; Meteorology; Artificial intelligence; Machine learning","score_opus":0.0078985129955333,"score_gpt":0.18337380286114538,"score_spread":0.1754752898656121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2955529518","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":[0.64897627,0.0016683675,0.11631185,0.000069440146,0.0041683926,0.00020810621,0.0001584455,0.0026115759,0.22582754],"genre_scores_gemma":[0.99006957,0.000088581524,0.0011976933,0.000031826647,0.00027647876,0.0000039063125,0.00027573478,0.000078918834,0.007977276],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988281,0.000023367378,0.00024891517,0.00032293305,0.00019599852,0.0003806935],"domain_scores_gemma":[0.99915373,0.000032758246,0.000042620693,0.0006507462,0.000023860719,0.000096261436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000107648244,0.00022696622,0.00025011323,0.00010523639,0.000081184546,0.00006420804,0.00033009742,0.00012678151,0.0003925138],"category_scores_gemma":[0.000009781996,0.00022794062,0.00004605436,0.00018229672,0.000013284902,0.00074445613,0.00013136583,0.00010794364,0.00010751845],"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":[0.0000066102284,0.000009319146,0.0002859645,0.000027670561,0.000060316226,0.0000044895633,0.00008364653,0.927458,0.06984921,0.00017316939,0.0011682591,0.00087332714],"study_design_scores_gemma":[0.00022533225,0.000053814503,0.00008587605,0.00010421656,0.000020033793,0.000030788342,0.000033638375,0.9399419,0.013183948,0.00027208816,0.045764,0.0002843937],"about_ca_topic_score_codex":0.00057289837,"about_ca_topic_score_gemma":0.00013269401,"teacher_disagreement_score":0.3410933,"about_ca_system_score_codex":0.000057036563,"about_ca_system_score_gemma":0.00002250949,"threshold_uncertainty_score":0.9295149},"labels":[],"label_agreement":null},{"id":"W2964641474","doi":"10.24963/ijcai.2019/965","title":"Intelligent Decision Support for Improving Power Management","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of British Columbia; BC Research (Canada)","funders":"Nanyang Technological University","keywords":"Computer science; Decision support system; Electricity; Big data; Load management; Power management; Demand response; Intelligent decision support system; Power consumption; Term (time); Power (physics); Operations research; Artificial intelligence; Data mining; Engineering","score_opus":0.008586655588651149,"score_gpt":0.21828469760037086,"score_spread":0.2096980420117197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964641474","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":[0.12037331,0.0000666598,0.531843,0.000007995504,0.0019166333,0.00031755376,0.000003056399,0.00035446355,0.34511733],"genre_scores_gemma":[0.96040046,0.000010694992,0.03220134,0.000063750434,0.000032681073,0.000016156704,0.000007722741,0.000030214636,0.007236972],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99943966,0.000001352203,0.00014933548,0.0001234544,0.00008263811,0.00020357397],"domain_scores_gemma":[0.9997366,0.000039250528,0.000010410353,0.0001583828,0.000013512675,0.000041810814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103214945,0.00009287356,0.000083214814,0.000059470713,0.000019395879,0.000025483681,0.00009444194,0.000034814904,0.0008748585],"category_scores_gemma":[0.000003737287,0.0000809764,0.000055872188,0.000053973363,0.0000029098524,0.00007044225,0.000036132347,0.000040247498,0.0002574008],"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":[0.000038205526,0.00003348127,0.00093920995,0.00040514668,0.00010278234,0.000008935251,0.00030220958,0.07228682,0.0042381836,0.033441428,0.008982893,0.8792207],"study_design_scores_gemma":[0.0010403277,0.00027644585,0.0006274703,0.00011222631,0.00003312902,0.000010880324,0.0004135129,0.28722417,0.039317887,0.0013353205,0.6688795,0.0007291348],"about_ca_topic_score_codex":0.0000020151713,"about_ca_topic_score_gemma":0.000003122009,"teacher_disagreement_score":0.8784916,"about_ca_system_score_codex":0.0000295499,"about_ca_system_score_gemma":0.0000024713256,"threshold_uncertainty_score":0.95790875},"labels":[],"label_agreement":null},{"id":"W2964951176","doi":"10.1109/isie.2019.8781349","title":"LSTM-based Short-term Load Forecasting for Building Electricity Consumption","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":45,"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 Victoria","funders":"","keywords":"Computer science; Benchmark (surveying); Flexibility (engineering); Perceptron; Term (time); Support vector machine; Artificial neural network; Multilayer perceptron; Artificial intelligence; Random forest; Recurrent neural network; Electricity; Machine learning; Energy consumption; Data mining; Engineering","score_opus":0.028226696288271902,"score_gpt":0.2416568517275203,"score_spread":0.2134301554392484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964951176","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":[0.83777905,0.00013763299,0.15249707,0.000006829893,0.0004863334,0.00024170573,0.0000056439967,0.00049360946,0.008352134],"genre_scores_gemma":[0.9883435,0.0000064006585,0.011154304,0.000045686134,0.00012772995,0.000029281822,0.000018529914,0.000046886307,0.00022771824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900097,0.000008237969,0.00023259221,0.00020498758,0.00014220203,0.000410994],"domain_scores_gemma":[0.9994832,0.00021225234,0.000024320485,0.00015721151,0.000054566786,0.00006839284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021321706,0.0001780345,0.00018265087,0.00008718877,0.000070030954,0.00004830917,0.00011059371,0.000096218646,0.00013121858],"category_scores_gemma":[0.00003664023,0.00017650741,0.00009677806,0.00012377523,0.000010178526,0.00013915684,0.00001332098,0.00012628188,0.000026715112],"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":[0.0000896188,0.00005428496,0.117783844,0.0012120465,0.00013729329,0.0000075449334,0.00016436631,0.47122994,0.3099615,0.005686169,0.0011469878,0.09252643],"study_design_scores_gemma":[0.0004532796,0.00005833754,0.000645068,0.00009619967,0.000016298634,0.000006771879,0.000004170166,0.90194035,0.09414417,0.00007525924,0.00227283,0.0002872835],"about_ca_topic_score_codex":0.000008179928,"about_ca_topic_score_gemma":0.000022629869,"teacher_disagreement_score":0.4307104,"about_ca_system_score_codex":0.00013015958,"about_ca_system_score_gemma":0.000025686717,"threshold_uncertainty_score":0.71977633},"labels":[],"label_agreement":null},{"id":"W2965375875","doi":"10.1109/isie.2019.8781274","title":"Forecasting in Small Smart Grid","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of New Brunswick","funders":"","keywords":"Smart grid; Computer science; Power demand; Electric power system; Renewable energy; Grid; Architecture; Demand response; Power (physics); Power grid; Key (lock); Distributed computing; Electricity; Engineering; Electrical engineering; Power consumption; Operating system","score_opus":0.01799912061765167,"score_gpt":0.1832808668721863,"score_spread":0.16528174625453465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965375875","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":[0.7257794,0.000057075056,0.0002699406,0.000007270813,0.00062498683,0.00003917234,6.4377446e-7,0.0001557251,0.27306578],"genre_scores_gemma":[0.99671036,0.0000054779716,0.0017814238,0.000039115326,0.000089688925,0.000003867284,0.000004967378,0.000022399523,0.0013427234],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994984,0.0000049940445,0.0001370407,0.00009515345,0.000037982438,0.00022641287],"domain_scores_gemma":[0.99980396,0.000044188848,0.000008104389,0.00010359124,0.000006357073,0.000033803863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009084381,0.00008741914,0.00009842017,0.00006996843,0.000010050488,0.000015315156,0.00007353312,0.00004411148,0.00028200334],"category_scores_gemma":[0.000009275937,0.00008283099,0.000027149386,0.0001314014,0.0000039093516,0.00006754831,0.00001952989,0.00011120032,0.0001359102],"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":[0.000011301677,0.000031120504,0.4242043,0.0002870785,0.000039812083,0.000051392482,0.00092034944,0.5008285,0.0064887367,0.0057196873,0.00175247,0.059665237],"study_design_scores_gemma":[0.00055278366,0.000034725188,0.005143097,0.00015309942,0.000003105939,0.000023235658,0.00009996124,0.9587147,0.0070487326,0.00025880674,0.027579352,0.00038839347],"about_ca_topic_score_codex":0.000063274056,"about_ca_topic_score_gemma":0.0003958848,"teacher_disagreement_score":0.4578862,"about_ca_system_score_codex":0.00002273016,"about_ca_system_score_gemma":0.000004261916,"threshold_uncertainty_score":0.337775},"labels":[],"label_agreement":null},{"id":"W2965525929","doi":"10.1109/tsg.2019.2933413","title":"Multiple Kernel Learning-Based Transfer Regression for Electric Load Forecasting","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":152,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Boosting (machine learning); Machine learning; Multiple kernel learning; Artificial intelligence; Ensemble learning; Probabilistic forecasting; Kernel (algebra); Flexibility (engineering); Electrical load; Scheduling (production processes); Support vector machine; Transfer of learning; Mathematical optimization; Kernel method; Engineering; Mathematics","score_opus":0.016589907164446388,"score_gpt":0.21038843151148734,"score_spread":0.19379852434704095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965525929","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":[0.4145959,0.00008311272,0.5807966,0.00002281408,0.0023798256,0.00034603762,0.000030398402,0.00055387896,0.0011914338],"genre_scores_gemma":[0.9979816,0.000020305886,0.00066485495,0.000048191196,0.00017251963,0.000113648886,0.000017649418,0.00010054617,0.00088068476],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986611,0.000030102969,0.0002930815,0.0003045536,0.00023510409,0.00047605234],"domain_scores_gemma":[0.99920404,0.00038110194,0.000024388593,0.00021257998,0.00007049343,0.000107421314],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018803292,0.00028592919,0.00025917392,0.00018776234,0.00021771218,0.00003747771,0.0001289152,0.00016397265,0.00012215611],"category_scores_gemma":[0.000010840511,0.0002715371,0.0002434339,0.00031938747,0.000013941477,0.00014495081,3.913953e-7,0.00046577785,0.00008237836],"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":[0.00013339646,0.000051797786,0.00023669594,0.0001287551,0.000042914893,0.0000021856056,0.0001561909,0.9557619,0.025930386,0.0000033715849,0.00016178149,0.017390586],"study_design_scores_gemma":[0.0013683822,0.00024584608,0.000043850596,0.0001686139,0.000033211374,0.0000072253083,0.000017926868,0.791566,0.19214797,0.0000043768982,0.014086875,0.0003097046],"about_ca_topic_score_codex":0.000027751687,"about_ca_topic_score_gemma":0.000078099096,"teacher_disagreement_score":0.5833857,"about_ca_system_score_codex":0.00013364898,"about_ca_system_score_gemma":0.000049290305,"threshold_uncertainty_score":0.99997365},"labels":[],"label_agreement":null},{"id":"W2965751469","doi":"10.1109/tii.2019.2933009","title":"A Novel Electricity Price Forecasting Approach Based on Dimension Reduction Strategy and Rough Artificial Neural Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Computer science; Artificial intelligence; Electricity; Dimensionality reduction; Electric power system; Benchmarking; Dimension (graph theory); Benchmark (surveying); Machine learning; Electricity market; Field (mathematics); Data mining; Power (physics); Engineering; Mathematics","score_opus":0.04992311691369354,"score_gpt":0.2172596967360863,"score_spread":0.16733657982239275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965751469","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":[0.38071242,0.0000052369987,0.61297977,0.000011156722,0.0015581395,0.00035687638,0.000014985772,0.00023266429,0.0041287714],"genre_scores_gemma":[0.9979014,0.000004782525,0.0016678884,0.000047291887,0.0002612342,0.000019827763,0.000020005642,0.000034186163,0.000043398617],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986493,0.000023458617,0.00056259416,0.0001419924,0.00024156643,0.0003811167],"domain_scores_gemma":[0.9994181,0.00012286472,0.000112912516,0.00019053802,0.000050514307,0.00010508386],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024047261,0.0002654624,0.00024075892,0.0002205101,0.00019764485,0.00011662419,0.00008611157,0.00033160302,0.000016455435],"category_scores_gemma":[0.000009869039,0.00025992244,0.00007548861,0.0005094481,0.000026362062,0.0004153223,0.0000012861148,0.0009338775,0.0000055360374],"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":[0.00010317292,0.000061115294,0.0000024649785,0.000031391886,0.000022304584,2.9151113e-7,0.00019339546,0.9577267,0.0004962188,0.000045965357,0.000048835645,0.04126812],"study_design_scores_gemma":[0.0008176945,0.0003189108,0.000002648188,0.0000715076,0.000030559935,0.000029200974,0.00017506197,0.9925347,0.0056709806,0.000005049424,0.000081806895,0.00026185895],"about_ca_topic_score_codex":0.000022813008,"about_ca_topic_score_gemma":0.000002061011,"teacher_disagreement_score":0.617189,"about_ca_system_score_codex":0.000109434375,"about_ca_system_score_gemma":0.000033197142,"threshold_uncertainty_score":0.9999853},"labels":[],"label_agreement":null},{"id":"W2965791322","doi":"10.1109/iciot.2019.00029","title":"Forecasting Building Energy Consumption with Deep Learning: A Sequence to Sequence Approach","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Independent Electricity System Operator","keywords":"Computer science; Deep learning; Recurrent neural network; Artificial intelligence; Energy consumption; Artificial neural network; Machine learning; Feed forward; Energy (signal processing); Data mining; Engineering; Control engineering","score_opus":0.03418991432279755,"score_gpt":0.2267830568314046,"score_spread":0.19259314250860704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965791322","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":[0.68248004,0.00008810249,0.26662302,0.0000082725055,0.00015220932,0.00010550479,0.0000011615522,0.0006251975,0.04991648],"genre_scores_gemma":[0.9443678,0.000015175308,0.054591175,0.000065151835,0.00007047311,0.000028395556,0.000013931924,0.0000574774,0.00079039694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987731,0.000023862483,0.00020568747,0.00032749856,0.0002045815,0.000465257],"domain_scores_gemma":[0.999504,0.0000797755,0.000038718004,0.0001832203,0.000044726465,0.00014952479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015360398,0.00023469512,0.0002035235,0.0001293946,0.000090709815,0.00007652448,0.00017554007,0.00008194263,0.00012158843],"category_scores_gemma":[0.000023490378,0.0002057796,0.000037539325,0.00030914595,0.000024713692,0.00025480118,0.000046872985,0.00022135134,0.0000446027],"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":[0.000011275134,0.0000064000656,0.005716868,0.000093983916,0.000026938495,0.000009719337,0.00033083532,0.9365958,0.032440264,0.004736462,0.000014216052,0.02001724],"study_design_scores_gemma":[0.00023698536,0.00009816978,0.000070610025,0.00015124507,0.000009612409,0.00015006965,0.00011366665,0.9845463,0.011007586,0.00003432515,0.003178804,0.00040260612],"about_ca_topic_score_codex":0.00007368299,"about_ca_topic_score_gemma":0.00003123011,"teacher_disagreement_score":0.26188776,"about_ca_system_score_codex":0.00009714944,"about_ca_system_score_gemma":0.000014150818,"threshold_uncertainty_score":0.8391448},"labels":[],"label_agreement":null},{"id":"W2966130868","doi":"10.1016/j.rser.2019.109293","title":"Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia","year":2019,"lang":"en","type":"article","venue":"Renewable and Sustainable Energy Reviews","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":71,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Ministry of Higher Education and Scientific Research; United Nations","keywords":"Multivariate adaptive regression splines; Autoregressive integrated moving average; Mars Exploration Program; Multivariate statistics; Forecast skill; Mean squared error; Artificial neural network; Autoregressive model; Meteorology; Probabilistic forecasting; Computer science; Linear regression; Econometrics; Environmental science; Statistics; Time series; Bayesian multivariate linear regression; Mathematics; Machine learning; Geography; Probabilistic logic","score_opus":0.020318332373761128,"score_gpt":0.25642637568319115,"score_spread":0.23610804330943003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966130868","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":[0.91327524,0.028407948,0.056898326,0.0000053096037,0.000052214047,0.00030571548,0.0000024660987,0.00012841048,0.0009243561],"genre_scores_gemma":[0.9780408,0.0078100823,0.012753371,0.00001325261,0.00006454461,0.000036551977,0.000050090595,0.00008328192,0.0011480063],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997216,0.00037820972,0.0006617917,0.00052545953,0.00017161547,0.0010469556],"domain_scores_gemma":[0.99909586,0.00017638945,0.00016081927,0.00028398726,0.00006114209,0.00022180281],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017680214,0.00048720906,0.0009917865,0.00015502951,0.00026524378,0.00015310783,0.00012626905,0.00017231816,0.000033435055],"category_scores_gemma":[0.0000839544,0.00039542996,0.00011019528,0.0011005014,0.000044890432,0.00032226276,0.00007935295,0.00030442272,4.923546e-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":[0.00005241774,0.00001536384,0.30013275,0.0014886503,0.00008794437,0.000062170555,0.00015664267,0.68717813,0.0012221597,0.00017104343,0.0000031874704,0.009429546],"study_design_scores_gemma":[0.0007902451,0.00016167032,0.0013329264,0.00047433106,0.0002449487,0.00007683585,0.0004791435,0.9855513,0.00049151975,0.000067470166,0.009650345,0.0006792665],"about_ca_topic_score_codex":0.0048006224,"about_ca_topic_score_gemma":0.0013109603,"teacher_disagreement_score":0.2987998,"about_ca_system_score_codex":0.00018128836,"about_ca_system_score_gemma":0.000048436017,"threshold_uncertainty_score":0.99984974},"labels":[],"label_agreement":null},{"id":"W2968465413","doi":"10.1049/iet-rpg.2019.0093","title":"Ramp events forecasting based on long‐term wind power prediction and correction","year":2019,"lang":"en","type":"article","venue":"IET Renewable Power Generation","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":32,"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":"Term (time); Wind power; Wind power forecasting; Meteorology; Computer science; Environmental science; Power (physics); Electric power system; Engineering; Electrical engineering; Physics","score_opus":0.012884631389232133,"score_gpt":0.1984815435725338,"score_spread":0.18559691218330168,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968465413","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":[0.9662392,0.00015021002,0.014035241,0.000026209775,0.00624354,0.00026703323,0.00001445342,0.00029467922,0.012729445],"genre_scores_gemma":[0.99766,0.000025824098,0.0003250237,0.00009925351,0.000292134,0.000014503025,0.00020373744,0.000059125872,0.001320423],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987743,0.000040675044,0.00028961652,0.0003346123,0.0002560172,0.00030481513],"domain_scores_gemma":[0.99948335,0.000047574504,0.000068399226,0.00024861295,0.00005652331,0.000095524054],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022746123,0.0002455888,0.00017807978,0.00017050725,0.00013673409,0.00009289632,0.000066450404,0.00017241764,0.00024516167],"category_scores_gemma":[0.000029205641,0.0002542221,0.00005680023,0.0001917022,0.000012573584,0.00033535197,0.000016746802,0.00015972393,0.000030755673],"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":[0.000028346958,0.000027911548,0.026835905,0.000025107454,0.000022602871,0.0000023294722,0.00016028051,0.93669844,0.031851854,0.000008523486,0.001832348,0.002506379],"study_design_scores_gemma":[0.00076038885,0.00029302321,0.009971272,0.00016923908,0.000017949033,0.000019179986,0.000018768626,0.95518136,0.031280454,0.000013441941,0.0019776525,0.00029727095],"about_ca_topic_score_codex":0.000032247877,"about_ca_topic_score_gemma":0.000088765504,"teacher_disagreement_score":0.031420786,"about_ca_system_score_codex":0.00009717028,"about_ca_system_score_gemma":0.0000244403,"threshold_uncertainty_score":0.999991},"labels":[],"label_agreement":null},{"id":"W2969768536","doi":"10.35378/gujs.459840","title":"Wind Speed Clustering Using Linkage-Ward Method: A Case Study of Khaaf, Iran","year":2019,"lang":"en","type":"article","venue":"GAZI UNIVERSITY JOURNAL OF SCIENCE","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"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 British Columbia","funders":"","keywords":"Wind power; Wind speed; Cluster analysis; Probabilistic logic; Renewable energy; Meteorology; Environmental science; Electricity; Computer science; Engineering; Geography; Electrical engineering; Artificial intelligence","score_opus":0.03493014996335591,"score_gpt":0.2570845628898798,"score_spread":0.22215441292652388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969768536","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":[0.9838906,0.000023011336,0.014190451,0.0000030253523,0.0006361013,0.000054630265,0.0000010629989,0.000013291608,0.0011878082],"genre_scores_gemma":[0.9883016,0.0000050313565,0.011583818,0.0000028844042,0.000036624482,1.257714e-9,3.073837e-8,0.00000648027,0.00006357518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991253,0.000038454167,0.00020818322,0.000111254354,0.0003159181,0.00020089839],"domain_scores_gemma":[0.99940807,0.00005880651,0.0001482669,0.00013328598,0.00013379169,0.000117790136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079713407,0.00009055331,0.00020841113,0.00042854034,0.0001419668,0.000025996042,0.00035513396,0.000030889525,0.000022633292],"category_scores_gemma":[0.000021132757,0.00009131364,0.00005918745,0.0006947233,0.00010481501,0.0006523635,0.0001078156,0.0002054158,0.0000015388091],"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":[0.00001860783,0.000038865794,0.005240593,0.000031853593,0.000027756456,0.0016424991,0.007876969,0.9329338,0.050649732,0.0000059908416,0.0000034310904,0.001529877],"study_design_scores_gemma":[0.0019829038,0.0005290561,0.0016418522,0.00031233963,0.000108914246,0.0073092445,0.0627326,0.92050594,0.0042997855,0.000008333578,0.00026725457,0.00030175733],"about_ca_topic_score_codex":0.00014661053,"about_ca_topic_score_gemma":0.00003104982,"teacher_disagreement_score":0.05485563,"about_ca_system_score_codex":0.00012371682,"about_ca_system_score_gemma":0.00006353119,"threshold_uncertainty_score":0.3723662},"labels":[],"label_agreement":null},{"id":"W2969981892","doi":"","title":"Hybrid Wavelet and Local Approximation Method for Urban Water Demand Forecasting – Chaotic Approach:","year":2018,"lang":"en","type":"article","venue":"WDSA / CCWI Joint Conference Proceedings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Chaotic; Wavelet; Nonlinear system; Correlation dimension; Phase space; Demand forecasting; Econometrics; Time series; Dimension (graph theory); Mathematics; Computer science; Mathematical optimization; Statistics; Artificial intelligence; Fractal dimension; Operations research; Fractal; Mathematical analysis","score_opus":0.03654169349550443,"score_gpt":0.2240163506905229,"score_spread":0.18747465719501846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969981892","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":[0.20721102,0.000055907054,0.78320676,0.00007581235,0.00021535475,0.00039330334,0.0000089134155,0.0003261091,0.00850685],"genre_scores_gemma":[0.8960133,0.000008985484,0.103076674,0.000064444575,0.0004827299,0.00011733065,0.00003834789,0.000064799846,0.00013338384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982521,0.000009497026,0.00042757342,0.00046450496,0.00018497657,0.000661367],"domain_scores_gemma":[0.99930656,0.000044491342,0.00007974546,0.00010436084,0.0002975241,0.00016732162],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00066637393,0.00035274573,0.0003860261,0.00016413024,0.00023936554,0.0002253262,0.00015249096,0.00012954893,0.000031709027],"category_scores_gemma":[0.00008796177,0.00028912502,0.00007256859,0.00010050083,0.00014698705,0.00042743192,0.00008909355,0.00021971879,0.00000992894],"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":[0.00030251773,0.00022019108,0.0012229959,0.009097029,0.00058844086,0.000012198044,0.054972757,0.0016121457,0.21344899,0.05865956,0.010049241,0.64981395],"study_design_scores_gemma":[0.00047482966,0.00014264304,0.000028729111,0.00016233361,0.000041827694,0.00011546818,0.00041502583,0.83454585,0.15832333,0.0030614196,0.0023050774,0.00038346348],"about_ca_topic_score_codex":0.000009040062,"about_ca_topic_score_gemma":0.0000021197536,"teacher_disagreement_score":0.8329337,"about_ca_system_score_codex":0.00005233079,"about_ca_system_score_gemma":0.000016768017,"threshold_uncertainty_score":0.9999561},"labels":[],"label_agreement":null},{"id":"W2974046511","doi":"10.1109/tii.2019.2942353","title":"Bayesian Deep Learning-Based Probabilistic Load Forecasting in Smart Grids","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":179,"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 Victoria","funders":"China Scholarship Council; Beijing University of Posts and Telecommunications; National Natural Science Foundation of China","keywords":"Computer science; Probabilistic logic; Smart grid; Smart meter; Overfitting; Machine learning; Artificial intelligence; Pooling; Cluster analysis; Data mining; Bayesian probability; Deep learning; Energy consumption; Scheduling (production processes); Artificial neural network; Engineering","score_opus":0.02537672286951327,"score_gpt":0.2091032948278648,"score_spread":0.18372657195835151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2974046511","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":[0.47768784,0.00001570429,0.4992122,0.00002446852,0.0036236162,0.0006766675,0.000021124813,0.00062768353,0.018110704],"genre_scores_gemma":[0.9988267,0.0000046140126,0.000771857,0.000039695562,0.00009017531,0.00004106476,0.000010961519,0.000043295877,0.00017164346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983696,0.000036727408,0.00073076866,0.000113384216,0.00030283967,0.00044672124],"domain_scores_gemma":[0.99926394,0.00026349278,0.00008892056,0.00022062838,0.00004906886,0.000113974354],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035331483,0.0002675079,0.00028983338,0.00028759264,0.00010421872,0.00007919627,0.00016319424,0.00031400225,0.00020464529],"category_scores_gemma":[0.000038712125,0.00027755843,0.00010599424,0.0005515495,0.000032282274,0.00034043303,0.0000012859989,0.0011390665,0.000114525814],"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":[0.000034521654,0.00003037216,0.00028921737,0.00009575806,0.00001986298,0.0000021299104,0.0007189055,0.97300076,0.000018721112,0.000010661681,0.000031498803,0.02574759],"study_design_scores_gemma":[0.0015200215,0.00018639998,0.000010477808,0.0002856242,0.000019904697,0.000007889935,0.00027299346,0.9929435,0.0021845454,0.000017234075,0.0022332892,0.00031807978],"about_ca_topic_score_codex":0.00004678201,"about_ca_topic_score_gemma":0.00015408608,"teacher_disagreement_score":0.52113885,"about_ca_system_score_codex":0.00033046008,"about_ca_system_score_gemma":0.000107208936,"threshold_uncertainty_score":0.99996763},"labels":[],"label_agreement":null},{"id":"W2975391820","doi":"10.1016/j.jcomm.2019.100107","title":"Econometric modelling and forecasting of intraday electricity prices","year":2019,"lang":"en","type":"article","venue":"Journal of commodity markets","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":108,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Narodowym Centrum Nauki; Narodowe Centrum Nauki; Deutsche Forschungsgemeinschaft","keywords":"Econometrics; Lasso (programming language); Electricity price forecasting; Benchmark (surveying); Electricity market; Economics; Electricity; Sample (material); Multivariate statistics; Econometric model; Quarter (Canadian coin); Computer science; Statistics; Mathematics; Engineering","score_opus":0.017333381256755175,"score_gpt":0.19244145867713341,"score_spread":0.17510807742037823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975391820","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":[0.97833925,0.0015799616,0.010094528,0.000011764955,0.00034288954,0.00004102346,0.0000031402835,0.000013540262,0.009573894],"genre_scores_gemma":[0.99608964,0.00035249948,0.0034174076,0.000006795027,0.00010063603,2.615885e-7,7.0684587e-7,0.00001528248,0.000016792581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990549,0.00003127648,0.0005150868,0.00006624183,0.00015681905,0.0001756713],"domain_scores_gemma":[0.9989914,0.00046354983,0.00029467212,0.000094272604,0.000080883845,0.000075250835],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007848103,0.00011254713,0.00034392386,0.00033407222,0.00002786935,0.00002626351,0.00014889149,0.00006054267,0.000029372934],"category_scores_gemma":[0.000067023604,0.00010292516,0.00008015383,0.00027612664,0.000015573498,0.0002932075,0.000027518474,0.0002812592,7.7053016e-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":[0.00009045903,0.00004162032,0.019543478,0.00042136243,0.0001589433,0.000007562238,0.00023185847,0.939552,0.0007270138,0.00029256754,0.00024084878,0.038692277],"study_design_scores_gemma":[0.0005238285,0.00012629874,0.006061164,0.00018366393,0.000026602027,0.00011268815,0.000023754423,0.9865762,0.0036598297,0.0005664162,0.001963191,0.00017638296],"about_ca_topic_score_codex":0.0000048890874,"about_ca_topic_score_gemma":0.0000019597524,"teacher_disagreement_score":0.04702417,"about_ca_system_score_codex":0.00003902152,"about_ca_system_score_gemma":0.000019416848,"threshold_uncertainty_score":0.4197166},"labels":[],"label_agreement":null},{"id":"W2975975302","doi":"10.1109/access.2019.2943752","title":"Similarity-Based Chained Transfer Learning for Energy Forecasting With Big Data","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Transfer of learning; Initialization; Artificial intelligence; Machine learning; Artificial neural network; Similarity (geometry); Big data; Energy consumption; Smart meter; Deep learning; Data modeling; Energy (signal processing); Recurrent neural network; Data mining; Smart grid; Database; Engineering","score_opus":0.07430641055625936,"score_gpt":0.2520843559768287,"score_spread":0.17777794542056935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2975975302","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":[0.38188273,0.00010947675,0.6117041,0.000044333417,0.0012386313,0.00019491719,0.00004591581,0.0004705887,0.004309337],"genre_scores_gemma":[0.99782133,0.000007426724,0.00096921646,0.00013821479,0.00047819965,0.000035701196,0.00024082109,0.000099529956,0.00020955536],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988324,0.00001928157,0.00021436381,0.00034415888,0.00016373539,0.00042603427],"domain_scores_gemma":[0.9991659,0.00022788114,0.000027067274,0.00044836593,0.00005089231,0.000079877456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019507288,0.00022848415,0.0002439912,0.00011339083,0.00010890111,0.00013644756,0.0006459874,0.00009802664,0.000028430075],"category_scores_gemma":[0.000021189288,0.00020415221,0.0000481404,0.00025222878,0.000019092085,0.00041528553,0.000037230886,0.00019470202,0.000002926304],"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":[0.00006629027,0.00001481238,0.0032109856,0.00026475932,0.000065728564,0.0000073705564,0.00007449715,0.96096754,0.0023122088,0.00012051307,0.00024599745,0.032649294],"study_design_scores_gemma":[0.0011201046,0.00009634728,0.00006286605,0.00017296268,0.000032929154,0.000005272195,0.000015327998,0.94000906,0.02145112,0.000033717573,0.036631644,0.0003686588],"about_ca_topic_score_codex":0.00006616767,"about_ca_topic_score_gemma":0.00027343162,"teacher_disagreement_score":0.6159386,"about_ca_system_score_codex":0.000024085773,"about_ca_system_score_gemma":0.00003857012,"threshold_uncertainty_score":0.8325085},"labels":[],"label_agreement":null},{"id":"W2979513172","doi":"10.1166/jctn.2019.8300","title":"Electric Load Forecasting with Deep Machine Learning","year":2019,"lang":"en","type":"article","venue":"Journal of Computational and Theoretical Nanoscience","topic":"Energy Load and Power Forecasting","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":"Mean absolute percentage error; JSON; Computer science; JavaScript; Artificial neural network; Document Object Model; Machine learning; Population; Nonlinear autoregressive exogenous model; Data mining; Artificial intelligence; Database; XML; World Wide Web","score_opus":0.004125728916688776,"score_gpt":0.17940574313535504,"score_spread":0.17528001421866626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979513172","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":[0.9148112,0.00046664267,0.080341965,0.000049325303,0.00013337554,0.000026372005,3.62127e-7,0.000019748539,0.004151033],"genre_scores_gemma":[0.9940914,0.000021445281,0.0057806172,0.000036963993,0.000044585395,2.2877073e-7,4.2885577e-7,0.000008018945,0.00001633043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992175,0.000020311969,0.00019408554,0.000076382414,0.00033350254,0.00015821033],"domain_scores_gemma":[0.9994174,0.00028851297,0.00006614875,0.00002540836,0.00011687089,0.000085627435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030547194,0.000083284205,0.00013071409,0.00007769419,0.00006591392,0.000044596964,0.00009233814,0.000024135348,0.000041347277],"category_scores_gemma":[0.00006998315,0.00005621688,0.000028353394,0.00024532745,0.00009999967,0.0001535575,0.000015285426,0.00021849861,0.0000031164823],"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":[0.000039022623,0.0000120738205,0.003564658,0.000022392338,0.000014727487,0.000019533147,0.00016803066,0.8436841,0.0012992809,0.13758956,0.0000032063538,0.0135834515],"study_design_scores_gemma":[0.00032020904,0.00033138486,0.00088994595,0.00007362148,0.000007831831,0.0006454778,0.0000170708,0.9719428,0.00052085397,0.024917658,0.00023657082,0.000096606025],"about_ca_topic_score_codex":5.36288e-7,"about_ca_topic_score_gemma":3.559128e-7,"teacher_disagreement_score":0.1282587,"about_ca_system_score_codex":0.000023502189,"about_ca_system_score_gemma":0.0000395275,"threshold_uncertainty_score":0.22924578},"labels":[],"label_agreement":null},{"id":"W2979792730","doi":"10.30880/ijie.2019.11.03.024","title":"A Hybrid Method of Least Square Support Vector Machine and Bacterial Foraging Optimization Algorithm for Medium Term Electricity Price Forecasting","year":2019,"lang":"en","type":"article","venue":"International Journal of Integrated Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"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":"Universiti Teknikal Malaysia Melaka","keywords":"Volatility (finance); Electricity market; Electricity price forecasting; Electricity; Computer science; Term (time); Medium term; Support vector machine; Electricity price; Scheduling (production processes); Econometrics; Mathematical optimization; Economics; Artificial intelligence; Engineering; Mathematics","score_opus":0.007125990886377938,"score_gpt":0.22131609997195797,"score_spread":0.21419010908558003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979792730","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":[0.12930717,0.00015415711,0.8681225,0.00002402843,0.0020294897,0.00011445111,0.00009292838,0.000053734537,0.00010154213],"genre_scores_gemma":[0.79163563,0.00005399647,0.20778263,0.000007974269,0.00034238683,0.00000563092,0.00009423974,0.000057694717,0.000019830846],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877363,0.0000145356635,0.00058324466,0.00012870415,0.0002711248,0.00022875873],"domain_scores_gemma":[0.998918,0.0001979072,0.00024950362,0.000068565234,0.00049342826,0.00007260916],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040671305,0.00021407747,0.00034054596,0.00041260678,0.000019396328,0.000062027044,0.0002373665,0.00006805757,0.00006872822],"category_scores_gemma":[0.00016840776,0.00019916416,0.00012368633,0.00014285921,0.00000893753,0.00036764893,0.000029725907,0.0002785847,4.100591e-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":[0.00008264829,0.000019762954,0.00019665157,0.00011651184,0.00033868203,0.000020366828,0.00018144278,0.8905969,0.04666881,0.000058154994,0.000038325466,0.061681747],"study_design_scores_gemma":[0.0008859004,0.00014459746,0.00010152401,0.00036766613,0.000032174048,0.0005828848,0.000028338683,0.9479732,0.048712227,0.000008451195,0.0009880645,0.0001749537],"about_ca_topic_score_codex":0.000010006876,"about_ca_topic_score_gemma":8.7117644e-7,"teacher_disagreement_score":0.6623285,"about_ca_system_score_codex":0.00015675927,"about_ca_system_score_gemma":0.000066708075,"threshold_uncertainty_score":0.8121679},"labels":[],"label_agreement":null},{"id":"W2979845028","doi":"10.1109/ccece.2019.8861769","title":"Scalable Local Short-Term Energy Consumption Forecasting","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 New Brunswick","funders":"","keywords":"Scalability; Computer science; Smart grid; Energy consumption; Smart meter; Big data; Term (time); Distributed computing; Real-time computing; Grid; Data mining; Database; Engineering","score_opus":0.01960416404350897,"score_gpt":0.20833718212493388,"score_spread":0.1887330180814249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979845028","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":[0.7287239,0.00022154798,0.07511927,0.000004267736,0.0007817044,0.000042698313,0.000002113508,0.000552888,0.19455165],"genre_scores_gemma":[0.9969484,0.000032172804,0.0008295991,0.000032825756,0.0000986091,0.0000059764925,0.000019323368,0.000035819954,0.001997248],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992495,0.000007706559,0.00017915636,0.00015151598,0.0001092048,0.0003028875],"domain_scores_gemma":[0.99969554,0.000046235273,0.000010744283,0.00015509958,0.000017281065,0.00007508337],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000663793,0.00013937309,0.00013751992,0.000061963496,0.00003777388,0.00003443474,0.00008883009,0.00008365116,0.0011913596],"category_scores_gemma":[0.000003022551,0.00013266316,0.000048313806,0.00008558822,0.000020234314,0.00016530516,0.000029328135,0.00009257921,0.00019524904],"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":[0.000019635274,0.000046423498,0.0833433,0.0003508317,0.00012719138,0.00003374747,0.00020978636,0.4801029,0.027071662,0.019190276,0.0032035718,0.38630068],"study_design_scores_gemma":[0.00022906419,0.00003900597,0.00072680396,0.000103685,0.000009589235,0.00004601868,0.000029357792,0.9606973,0.026937509,0.00008577235,0.0107649835,0.00033094597],"about_ca_topic_score_codex":0.000023349463,"about_ca_topic_score_gemma":0.000047880032,"teacher_disagreement_score":0.48059437,"about_ca_system_score_codex":0.000042877327,"about_ca_system_score_gemma":0.000006106832,"threshold_uncertainty_score":0.9997217},"labels":[],"label_agreement":null},{"id":"W2979858166","doi":"10.1109/ccece.2019.8861542","title":"SARIMA Model Forecasting of Short-Term Electrical Load Data Augmented by Fast Fourier Transform Seasonality Detection","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Fast Fourier transform; Algorithm; Series (stratigraphy); Computer science; Component (thermodynamics); Term (time); Physics","score_opus":0.029328884369806116,"score_gpt":0.23403369544990907,"score_spread":0.20470481108010297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979858166","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":[0.66952395,0.00016130086,0.30662307,0.0000116817655,0.00016801087,0.00017461376,0.00011301954,0.0002290876,0.022995252],"genre_scores_gemma":[0.99693686,0.000018438404,0.0023107654,0.000016960497,0.000045696866,0.00000396706,0.00015279335,0.000040387036,0.0004741286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986544,0.000013302546,0.0003470755,0.00029372855,0.00032249614,0.00036894905],"domain_scores_gemma":[0.9993315,0.00006709656,0.000030001333,0.00042506985,0.00005175835,0.00009460684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026450984,0.00020110686,0.0002451427,0.00003750951,0.00004478013,0.00002531576,0.00028511908,0.00012332601,0.00008847399],"category_scores_gemma":[0.000022016065,0.0001911674,0.00006943495,0.00021080981,0.000018452096,0.00036938203,0.000053448577,0.00022818294,0.000005992245],"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":[0.00013799687,0.00014064612,0.007349892,0.00041622884,0.00030197497,0.0000040813015,0.00037246585,0.20214401,0.2348693,0.00021897876,0.0015333656,0.55251104],"study_design_scores_gemma":[0.00029336516,0.00004479484,0.0000652967,0.000033940716,0.00002670061,0.000007302342,0.000011038933,0.9412234,0.057438042,0.000041824293,0.00060939876,0.00020489584],"about_ca_topic_score_codex":0.000051637202,"about_ca_topic_score_gemma":0.00013229635,"teacher_disagreement_score":0.7390794,"about_ca_system_score_codex":0.00011316223,"about_ca_system_score_gemma":0.00003588662,"threshold_uncertainty_score":0.77955806},"labels":[],"label_agreement":null},{"id":"W2981554396","doi":"10.3390/forecast1010012","title":"Quantile Regression and Clustering Models of Prediction Intervals for Weather Forecasts: A Comparative Study","year":2019,"lang":"en","type":"article","venue":"Forecasting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"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 Calgary; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Prediction interval; Quantile regression; Probabilistic forecasting; Quantile; Cluster analysis; Probabilistic logic; Computer science; Model output statistics; Numerical weather prediction; Weather forecasting; Consensus forecast; Econometrics; Statistics; Machine learning; Mathematics; Artificial intelligence; Meteorology; Geography","score_opus":0.05801115352355336,"score_gpt":0.27150225032635106,"score_spread":0.2134910968027977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981554396","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":[0.9570333,0.00023104576,0.035610646,0.0000021532996,0.00046476757,0.0006711324,0.000045276887,0.00014558417,0.0057961354],"genre_scores_gemma":[0.99590325,0.0000060792204,0.0037594947,0.0000029906755,0.00008605928,0.000058252906,0.000015475522,0.00004486002,0.0001235149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989424,0.000025198644,0.00041176286,0.00023266659,0.00013107588,0.00025692667],"domain_scores_gemma":[0.9994358,0.00017220594,0.0001138444,0.00016073338,0.00006547117,0.000051935414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029370858,0.00020367831,0.0003665475,0.0001202166,0.00006867285,0.000028823953,0.00009536163,0.0000691426,0.00001626093],"category_scores_gemma":[0.000020661859,0.00017867256,0.00006861589,0.00012313109,0.00002108106,0.0003179186,0.00007081454,0.00011737947,0.0000015551371],"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":[0.00029227778,0.00016116128,0.021566894,0.0012141594,0.00032382878,0.000003574553,0.03149413,0.89875615,0.009970742,0.00060534134,0.0003412912,0.035270445],"study_design_scores_gemma":[0.00086403766,0.00046154618,0.0002321709,0.0006631285,0.00002768389,0.000011862883,0.0023479825,0.99275887,0.0020375787,0.0003244558,0.00010776138,0.00016289733],"about_ca_topic_score_codex":0.000018138173,"about_ca_topic_score_gemma":0.000045086246,"teacher_disagreement_score":0.09400274,"about_ca_system_score_codex":0.00003139922,"about_ca_system_score_gemma":0.000006591329,"threshold_uncertainty_score":0.72860557},"labels":[],"label_agreement":null},{"id":"W2981695982","doi":"10.1088/1757-899x/609/5/052038","title":"Application of data mining in understanding the charging patterns of the hot water tank in a residential building: a case study","year":2019,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Energy Load and Power Forecasting","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":"Storage tank; Cluster analysis; Occupancy; Automation; Raw data; Engineering; Process engineering; Environmental science; Computer science; Civil engineering; Waste management; Mechanical engineering; Artificial intelligence","score_opus":0.037583738125012915,"score_gpt":0.24404426933374782,"score_spread":0.2064605312087349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981695982","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":[0.9970826,0.000016421676,0.0022858605,0.000020801335,0.00030382958,0.00023707237,0.000010182004,0.000019938383,0.00002330992],"genre_scores_gemma":[0.99983305,0.000008539887,0.00011012753,0.0000020224315,0.000019525358,0.00001224023,0.0000015281137,0.000011085696,0.0000018615401],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99898523,0.000020356314,0.00032612644,0.00021097086,0.00020171097,0.00025562523],"domain_scores_gemma":[0.99948484,0.00003844636,0.00004820986,0.0003800778,0.00002723317,0.000021193546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010481765,0.00011723675,0.00019395907,0.00015063214,0.000058347716,0.00008978857,0.00046504717,0.00002921886,0.000008804799],"category_scores_gemma":[0.000032182583,0.00007572196,0.000008568154,0.0003053596,0.00007304391,0.0006016121,0.00029889776,0.00007890555,2.1179918e-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":[0.0000073816345,0.0000069776324,0.004945298,0.00014673064,0.000004911255,0.000011346521,0.006919766,0.016953206,0.97026426,0.00057495077,1.6958954e-7,0.00016499226],"study_design_scores_gemma":[0.0005478227,0.00006551933,0.014077214,0.0007189936,0.000016846832,0.00018458397,0.024436668,0.14936937,0.8101629,0.000079479425,0.000016451648,0.00032415823],"about_ca_topic_score_codex":0.0006479911,"about_ca_topic_score_gemma":0.00029367395,"teacher_disagreement_score":0.16010137,"about_ca_system_score_codex":0.00004536193,"about_ca_system_score_gemma":0.000027336993,"threshold_uncertainty_score":0.30878517},"labels":[],"label_agreement":null},{"id":"W2981719102","doi":"10.1049/iet-gtd.2019.0241","title":"Microgrid energy management: how uncertainty modelling impacts economic performance","year":2019,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":16,"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 Calgary","funders":"","keywords":"Microgrid; Energy management; Computer science; Environmental economics; Energy (signal processing); Reliability engineering; Risk analysis (engineering); Operations research; Business; Engineering; Economics; Control (management)","score_opus":0.009312707824747943,"score_gpt":0.17999367417270568,"score_spread":0.17068096634795774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981719102","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":[0.57890224,0.00072200433,0.4182157,0.00009423774,0.00070786953,0.00011368029,0.00007568547,0.0002376564,0.0009309418],"genre_scores_gemma":[0.99351615,0.0026164723,0.0007452568,0.00002636379,0.00023934145,0.00001744633,0.0024917626,0.00003047487,0.00031674412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901605,0.000023234057,0.0002440851,0.00025875666,0.0001387081,0.00031916788],"domain_scores_gemma":[0.99960905,0.000009416841,0.00004491743,0.00019125048,0.000027758673,0.00011762652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000121727935,0.00021922438,0.00015829082,0.00005981066,0.00013881548,0.00011109624,0.000110326764,0.00012031027,0.00013421538],"category_scores_gemma":[5.2559255e-7,0.00021920241,0.00008786012,0.000111365414,0.000012320894,0.00037102145,0.000008916187,0.00010673539,0.000045378303],"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":[0.0000126742625,0.000009514771,0.00007323816,0.0000764898,0.00002692078,8.8280484e-7,0.000047840444,0.9260032,0.02119156,0.0009289371,0.0017476194,0.04988112],"study_design_scores_gemma":[0.00036152982,0.00002756035,0.000049305123,0.00006333885,0.00001633072,0.000004244301,0.0000114935,0.81461394,0.069696516,0.000022109103,0.11490838,0.00022528014],"about_ca_topic_score_codex":0.000016508404,"about_ca_topic_score_gemma":0.0000052985106,"teacher_disagreement_score":0.41747043,"about_ca_system_score_codex":0.00020312124,"about_ca_system_score_gemma":0.000018263112,"threshold_uncertainty_score":0.8938815},"labels":[],"label_agreement":null},{"id":"W2982398001","doi":"10.1142/s0218001420520059","title":"A Building Energy Consumption Prediction Method Based on Integration of a Deep Neural Network and Transfer Reinforcement Learning","year":2019,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Jiangsu Provincial Key Research and Development Program; National Natural Science Foundation of China","keywords":"Computer science; Energy consumption; Artificial neural network; Artificial intelligence; Autoencoder; Deep learning; Transfer of learning; AdaBoost; Reinforcement learning; Energy (signal processing); Machine learning; Engineering","score_opus":0.042791765364232924,"score_gpt":0.28012884775758684,"score_spread":0.2373370823933539,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982398001","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":[0.37315723,0.00004394806,0.6260917,0.000025963185,0.000543713,0.000028006954,0.0000036249198,0.0000118625285,0.000093933406],"genre_scores_gemma":[0.99735266,0.00014781581,0.0021679197,0.00007837648,0.00021830558,0.0000023976886,0.00001931974,0.000010665844,0.00000252973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990374,0.00007165006,0.0004793434,0.00009446935,0.00022264163,0.00009452229],"domain_scores_gemma":[0.9994561,0.00018241264,0.00011591882,0.00003127691,0.00016755001,0.000046745023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035015828,0.00010227825,0.00013454078,0.00018732235,0.00003253978,0.00004788674,0.000061739054,0.000054612872,0.00014290326],"category_scores_gemma":[0.000032134903,0.000096270676,0.000054290933,0.000056788664,0.00002195694,0.00018466232,0.000008185392,0.00021015925,0.0000023618722],"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":[0.00007257408,0.000010132507,0.00068101275,0.000014008974,0.000027320262,0.0000023392677,0.00016460042,0.48543203,0.008024247,0.00023220638,9.3651244e-7,0.5053386],"study_design_scores_gemma":[0.000112217946,0.0002228382,0.00036425426,0.00038940035,0.000018406841,0.000034977522,0.000100295125,0.9525524,0.045107,0.0009697591,0.000044742468,0.00008371581],"about_ca_topic_score_codex":0.000013429183,"about_ca_topic_score_gemma":0.000016123846,"teacher_disagreement_score":0.62419546,"about_ca_system_score_codex":0.000028433526,"about_ca_system_score_gemma":0.0000052108308,"threshold_uncertainty_score":0.39258042},"labels":[],"label_agreement":null},{"id":"W2984309128","doi":"10.1109/poweri.2014.7117662","title":"Short-term load forecasting of Ontario Electricity Market by considering the effect of temperature","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"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":"Term (time); Artificial neural network; Electricity; Electricity market; Scheduling (production processes); Electric power system; Computer science; Reliability (semiconductor); Electrical load; Peak load; Reliability engineering; Operations research; Power (physics); Engineering; Artificial intelligence; Automotive engineering; Operations management; Voltage; Electrical engineering","score_opus":0.006535542110276217,"score_gpt":0.1843637885107121,"score_spread":0.1778282464004359,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984309128","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":[0.9388766,0.00019226594,0.0004811743,0.0000048122524,0.00015948793,0.00009893808,0.0000030514875,0.000075281,0.060108345],"genre_scores_gemma":[0.9990456,0.000004579763,0.0002487722,0.000009237466,0.00004408947,0.0000061337446,0.000003883208,0.00002376048,0.00061393995],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99911034,0.00004997415,0.00028907665,0.00012544643,0.00017442777,0.0002507396],"domain_scores_gemma":[0.9991827,0.00049110176,0.000043210373,0.00020535396,0.000035465288,0.0000421693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051393145,0.00018107872,0.00029505693,0.000035176537,0.000053048014,0.000017613327,0.00014833282,0.0000932096,0.000102012265],"category_scores_gemma":[0.00010324609,0.00011959731,0.00009060754,0.00013601857,0.000036439542,0.00006565846,0.00003234193,0.0002577384,6.597864e-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":[0.00018573053,0.00004202543,0.35197687,0.0014657906,0.00058772013,0.000009720433,0.0027693918,0.05048402,0.4979852,0.00042943683,0.020908564,0.07315551],"study_design_scores_gemma":[0.0004990279,0.00030511865,0.003866637,0.00024540402,0.00006437594,0.000036715428,0.000012170844,0.060609095,0.9295559,0.000026188942,0.004455404,0.0003239503],"about_ca_topic_score_codex":0.0010155948,"about_ca_topic_score_gemma":0.0038003407,"teacher_disagreement_score":0.4315707,"about_ca_system_score_codex":0.00009022788,"about_ca_system_score_gemma":0.000026677997,"threshold_uncertainty_score":0.48770365},"labels":[],"label_agreement":null},{"id":"W2985287323","doi":"10.1002/gch2.201900065","title":"Modeling and Forecasting of Energy Demands for Household Applications","year":2019,"lang":"en","type":"article","venue":"Global Challenges","topic":"Energy Load and Power Forecasting","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":"Western University","funders":"","keywords":"Autoregressive integrated moving average; Autoregressive model; Energy (signal processing); Energy consumption; Artificial neural network; Spline interpolation; Solar energy; Interpolation (computer graphics); Environmental science; Moving average; Econometrics; Computer science; Meteorology; Time series; Statistics; Engineering; Mathematics; Geography; Telecommunications; Artificial intelligence; Electrical engineering","score_opus":0.039778002906287455,"score_gpt":0.2201153764851382,"score_spread":0.18033737357885077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2985287323","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":[0.74797016,0.04345803,0.15689075,0.00007114629,0.00033525276,0.0003053626,0.00012963456,0.0004040085,0.050435636],"genre_scores_gemma":[0.9961189,0.0013095611,0.0023903458,0.000007927496,0.00008860372,0.000044125052,0.0000082043525,0.000017802551,0.000014549261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945796,0.0000038962526,0.00016780633,0.00013572816,0.00006352807,0.00017110707],"domain_scores_gemma":[0.99974686,0.0000416905,0.000022901831,0.00012023791,0.000026308946,0.00004200875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007146915,0.00010198117,0.00015582338,0.000025458809,0.000027852153,0.0000077144105,0.00007285291,0.000067280365,0.0000012427967],"category_scores_gemma":[0.0000053350755,0.0001040706,0.00004033329,0.000052356307,0.000009915059,0.000059063463,0.000024449184,0.000031082254,4.5814332e-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":[0.000010138609,0.000012689238,0.00040814924,0.00056134106,0.000056750545,2.5826967e-7,0.000103514576,0.7988786,0.0003196477,0.10086113,0.000016311687,0.09877144],"study_design_scores_gemma":[0.00039379424,0.000049373717,0.00009571416,0.00009689554,0.000017287774,0.000009413373,0.0001788062,0.9884468,0.0004622132,0.0046581035,0.0053820354,0.00020958974],"about_ca_topic_score_codex":0.000011590629,"about_ca_topic_score_gemma":0.000050565915,"teacher_disagreement_score":0.24814871,"about_ca_system_score_codex":0.0000163425,"about_ca_system_score_gemma":0.000004997485,"threshold_uncertainty_score":0.42438757},"labels":[],"label_agreement":null},{"id":"W2986912224","doi":"10.1109/poweri.2014.7117664","title":"Multi step ahead forecasting of wind power by genetic algorithm based neural networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"Artificial neural network; Mean absolute percentage error; Time series; Wind power; Computer science; Mean squared error; Genetic algorithm; Feedforward neural network; Algorithm; Metric (unit); Series (stratigraphy); Wind speed; Performance metric; Artificial intelligence; Machine learning; Statistics; Engineering; Mathematics; Meteorology","score_opus":0.01092932762841075,"score_gpt":0.1941752539720267,"score_spread":0.18324592634361594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2986912224","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":[0.12948461,0.0003449232,0.86415213,0.000009213517,0.0005751622,0.000083062696,0.000008624941,0.00022596831,0.0051162923],"genre_scores_gemma":[0.90436363,0.0000025531162,0.09520971,0.00007584616,0.00010912636,0.0000030248593,0.000016847487,0.000053895546,0.0001653474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99894136,0.000030341038,0.00032470535,0.00017972465,0.00013613813,0.0003877483],"domain_scores_gemma":[0.99945897,0.00013847587,0.00004893209,0.0002086448,0.000036865284,0.000108096654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001364902,0.0002057578,0.00022535943,0.00005941402,0.00004795142,0.000023573903,0.00014915188,0.00010902631,0.00013323347],"category_scores_gemma":[0.000024704983,0.0001929904,0.00008074412,0.0001530164,0.000032101452,0.00007041724,0.000026110538,0.00016701594,0.0000037294062],"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":[0.0000022320482,0.000015967169,0.000897763,0.000022425762,0.000016923734,0.0000020455068,0.000034648765,0.8745574,0.00073621556,0.000007084012,0.00088647014,0.12282083],"study_design_scores_gemma":[0.0004833495,0.000069328955,0.0005175195,0.000037114904,0.0000108724635,0.000005655906,0.000013672243,0.994775,0.001634416,0.0000020293814,0.0022316673,0.00021938693],"about_ca_topic_score_codex":0.000053122632,"about_ca_topic_score_gemma":0.000017465685,"teacher_disagreement_score":0.77487904,"about_ca_system_score_codex":0.000016173119,"about_ca_system_score_gemma":0.0000046662653,"threshold_uncertainty_score":0.78699195},"labels":[],"label_agreement":null},{"id":"W2991062704","doi":"10.1109/ecce.2019.8912917","title":"A Generic Load Forecasting Method for Aggregated Thermostatically Controlled Loads Based on Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Energie NB Power (Canada); University of New Brunswick","funders":"","keywords":"Generalization; Computer science; Demand response; Convolutional neural network; Demand forecasting; Quantization (signal processing); Artificial neural network; Machine learning; Data mining; Artificial intelligence; Electricity; Engineering; Algorithm; Operations research; Mathematics","score_opus":0.017217899520262904,"score_gpt":0.23109168630540688,"score_spread":0.213873786785144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991062704","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":[0.044537574,0.00021565448,0.9294676,0.00009660163,0.0009672729,0.0007657715,0.000016634916,0.0004957434,0.023437157],"genre_scores_gemma":[0.94753003,0.0000024394744,0.050597407,0.0007366353,0.0002181976,0.00011985166,0.00006021941,0.00007730258,0.00065793394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99844074,0.00006453043,0.0004125948,0.00028538122,0.0002449481,0.0005517875],"domain_scores_gemma":[0.99806845,0.0013884263,0.00007405157,0.00021275427,0.00013349058,0.00012282412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047397258,0.00029855306,0.00045432794,0.00008119575,0.00008471022,0.00005633406,0.00015514568,0.00013939133,0.0003626619],"category_scores_gemma":[0.00013971137,0.000243391,0.00022217385,0.00021107664,0.00002115474,0.00008019351,0.000017159366,0.00020473923,0.000017250255],"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":[0.00040556607,0.000020730817,0.00014831926,0.000035858928,0.0000776449,0.0000036289443,0.00002318957,0.9821435,0.0013504965,0.001324184,0.0004500888,0.014016795],"study_design_scores_gemma":[0.005826752,0.00020450655,0.000054593547,0.000059552644,0.00003493827,0.0000082018205,0.000010993168,0.9920784,0.00046146635,0.00009655817,0.00085533416,0.00030869336],"about_ca_topic_score_codex":0.000013972202,"about_ca_topic_score_gemma":0.000013036422,"teacher_disagreement_score":0.9029924,"about_ca_system_score_codex":0.00010396104,"about_ca_system_score_gemma":0.000050414506,"threshold_uncertainty_score":0.9925197},"labels":[],"label_agreement":null},{"id":"W2991301930","doi":"10.1088/1742-6596/1343/1/012038","title":"Data-driven short-term load forecasting for heating and cooling demand in office buildings","year":2019,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Energy Load and Power Forecasting","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":"Carleton University; National Research Council Canada","funders":"","keywords":"Autoregressive model; Term (time); Demand response; Time horizon; Computer science; Artificial neural network; Cooling load; Peak load; Electricity; Meteorology; Econometrics; Engineering; Machine learning; Mathematical optimization; Automotive engineering; Economics; Mathematics","score_opus":0.04341919720730702,"score_gpt":0.254822557828882,"score_spread":0.211403360621575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991301930","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":[0.96300536,0.0002649494,0.03574954,0.00001302549,0.00031306714,0.00009379873,0.000023615727,0.000022381164,0.0005142669],"genre_scores_gemma":[0.9904338,0.000096277705,0.009132643,0.000008678797,0.00027289908,0.0000017703718,0.000010663214,0.00002582167,0.00001741854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990353,0.000012807156,0.00041327323,0.00014068506,0.0001549147,0.00024299414],"domain_scores_gemma":[0.99932563,0.00017093075,0.00013373104,0.00014444371,0.00016516683,0.000060121645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032837372,0.00015804646,0.00033673536,0.000054563305,0.000052848933,0.00011239133,0.00022971797,0.000052495445,0.0000043846417],"category_scores_gemma":[0.00006955245,0.0001513866,0.000040669303,0.0001011168,0.000031414256,0.0010580445,0.00007999535,0.00024135131,5.3478186e-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":[0.00047020562,0.00007335602,0.059464127,0.0020014977,0.00033661048,0.000051795298,0.009338781,0.42499787,0.14689815,0.01584022,0.00012590692,0.34040147],"study_design_scores_gemma":[0.0018157674,0.00050972827,0.0029861736,0.0036673436,0.000110602814,0.00028974618,0.0020505253,0.91676855,0.06636348,0.0024624718,0.0021064088,0.00086921104],"about_ca_topic_score_codex":0.0000076054607,"about_ca_topic_score_gemma":0.000056367826,"teacher_disagreement_score":0.49177065,"about_ca_system_score_codex":0.0000391347,"about_ca_system_score_gemma":0.00007349548,"threshold_uncertainty_score":0.61733663},"labels":[],"label_agreement":null},{"id":"W2991539995","doi":"10.1109/smartgridcomm.2019.8909756","title":"An ensemble deep learning model for short-term load forecasting based on ARIMA and LSTM","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":28,"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":"Autoregressive integrated moving average; Computer science; Payload (computing); Term (time); Electrical load; Electric power system; Power (physics); Artificial intelligence; Time series; Machine learning","score_opus":0.021311599940318927,"score_gpt":0.2250879797119296,"score_spread":0.20377637977161067,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991539995","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":[0.6153679,0.00003855327,0.35743654,0.000006084983,0.00012347598,0.00013276414,0.0000020007562,0.00030238397,0.026590288],"genre_scores_gemma":[0.9821617,0.0000040104915,0.016988618,0.00007402522,0.00007315171,0.000017611883,0.000021872818,0.000063423635,0.0005955841],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910104,0.000009766272,0.00017195079,0.00024486423,0.00012635923,0.00034603832],"domain_scores_gemma":[0.9995447,0.00012881175,0.000018327379,0.00016944647,0.000036066616,0.000102689744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018408296,0.00018884304,0.00017741077,0.00006950812,0.00009223841,0.00006789469,0.0000845788,0.00009231392,0.00003149379],"category_scores_gemma":[0.000023551578,0.0001809927,0.00005093981,0.00005927451,0.000009778799,0.00017532082,0.000014198303,0.00016643234,0.000006501404],"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":[0.00001977821,0.00001028659,0.003536981,0.00008245495,0.00000890992,0.0000013109844,0.00023310925,0.9465021,0.007654485,0.0003075651,0.000012538206,0.041630518],"study_design_scores_gemma":[0.00036273786,0.00015321487,0.00012228731,0.000063036336,0.000010047173,0.0000037518903,0.000036146233,0.9951413,0.0035468913,0.0000778764,0.00023280786,0.00024989792],"about_ca_topic_score_codex":0.0000058448886,"about_ca_topic_score_gemma":0.000049641563,"teacher_disagreement_score":0.36679378,"about_ca_system_score_codex":0.000045194098,"about_ca_system_score_gemma":0.000014929329,"threshold_uncertainty_score":0.73806685},"labels":[],"label_agreement":null},{"id":"W2992927816","doi":"","title":"Model Simulations Predicting Vergence Adaptations To Near Adds","year":2009,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Vergence (optics); Geology; Computer science; Artificial intelligence","score_opus":0.0547091483530831,"score_gpt":0.32226846542586357,"score_spread":0.26755931707278047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2992927816","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":[0.9898876,0.000032169228,0.0013339755,0.000119451666,0.00027803588,0.00015908797,0.00001687305,0.00022718603,0.007945635],"genre_scores_gemma":[0.9782269,9.2884784e-7,0.021404488,0.00019067411,0.000057872898,0.00001937885,0.0000057727007,0.000013905325,0.00008008308],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853826,0.00002836973,0.00026120324,0.00036908852,0.00028798712,0.00051511475],"domain_scores_gemma":[0.9991678,0.00010430318,0.000041530126,0.00018188954,0.00013761003,0.0003668495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002444045,0.00018532746,0.00015439218,0.00016944943,0.0006121722,0.00007435699,0.00037237015,0.00007221101,0.000044872944],"category_scores_gemma":[0.00046751415,0.0001941788,0.000033091237,0.0014332335,0.000699615,0.00053171517,0.00006002503,0.00020778603,0.00004017255],"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":[0.0000012935253,0.000011206915,0.0014970131,0.0000014311981,0.0000029457842,0.0000072535063,0.0010719068,0.6754683,0.32101652,0.00048820226,0.000019725758,0.00041414963],"study_design_scores_gemma":[0.00006621887,0.00017187677,0.016848072,0.000037288642,0.0000061683872,0.00002734279,0.00006223782,0.9322075,0.04654596,0.0037853958,0.00003436329,0.0002075411],"about_ca_topic_score_codex":0.000019907266,"about_ca_topic_score_gemma":0.000005865026,"teacher_disagreement_score":0.27447057,"about_ca_system_score_codex":0.00009754465,"about_ca_system_score_gemma":0.00018920428,"threshold_uncertainty_score":0.79183817},"labels":[],"label_agreement":null},{"id":"W2994519682","doi":"10.1109/bracis.2019.00053","title":"A Comparison Study on Time Series Forecasting Given Smart Grid Load Uncertainties","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"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":"Smart grid; Computer science; Time series; Artificial neural network; Support vector machine; Adaptive neuro fuzzy inference system; Energy consumption; Autoregressive integrated moving average; Data mining; Fuzzy logic; Machine learning; Artificial intelligence; Fuzzy control system; Engineering","score_opus":0.023184696254107854,"score_gpt":0.2355324385957647,"score_spread":0.21234774234165685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2994519682","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":[0.8494365,0.00005714366,0.00015000014,0.000026851061,0.00085324293,0.0002187377,0.0000045589086,0.0005578326,0.14869511],"genre_scores_gemma":[0.9922901,0.0000013750267,0.00037559902,0.000024913996,0.00015449928,0.000013742897,0.000010387958,0.000043061453,0.0070863534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989338,0.000024506333,0.00026824424,0.00020585618,0.00024074796,0.000326856],"domain_scores_gemma":[0.9995283,0.00009818438,0.000034437424,0.00023822898,0.000040385443,0.000060446404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017296393,0.00022308464,0.00030211316,0.00007115045,0.00006997666,0.00006748693,0.00015141828,0.00005583058,0.00048922707],"category_scores_gemma":[0.000022863587,0.00019529353,0.000059202852,0.00015013153,0.000017133028,0.00018123524,0.00004903608,0.00018586127,0.00073245465],"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":[0.00018466929,0.00039495892,0.35371116,0.0002521372,0.0004954071,0.000044421817,0.015020418,0.59064764,0.0042885956,0.0010571472,0.02461505,0.009288417],"study_design_scores_gemma":[0.0034084416,0.0042973673,0.021042423,0.00070788315,0.00012039642,0.000054261887,0.011625753,0.81319267,0.022021865,0.00033852307,0.120566204,0.0026241972],"about_ca_topic_score_codex":0.00012512886,"about_ca_topic_score_gemma":0.00012409485,"teacher_disagreement_score":0.33266872,"about_ca_system_score_codex":0.00007874703,"about_ca_system_score_gemma":0.000014623285,"threshold_uncertainty_score":0.94144666},"labels":[],"label_agreement":null},{"id":"W2995477907","doi":"10.1109/iecon.2019.8927167","title":"Sensitivity Analysis of Exogenous Variables for Load Forecasting Using Polynomial Regression","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Université du Québec à Trois-Rivières","funders":"","keywords":"Principal component analysis; Regression analysis; Computer science; Entropy (arrow of time); Dimensionality reduction; Dimension (graph theory); Set (abstract data type); Sensitivity (control systems); Variable (mathematics); Regression; Linear regression; Polynomial regression; Data mining; Statistics; Mathematics; Econometrics; Machine learning; Artificial intelligence; Engineering","score_opus":0.026430840153821206,"score_gpt":0.23299969106715687,"score_spread":0.20656885091333566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995477907","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":[0.9251312,0.00008299346,0.06736182,0.000001865949,0.0003462144,0.00010709541,0.000027911263,0.00010308872,0.0068378258],"genre_scores_gemma":[0.9820641,0.0000032194555,0.0176253,0.000008297124,0.000086903834,0.0000018396705,0.000019411713,0.000026424499,0.00016447798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991606,0.000018805078,0.00026024296,0.00016697116,0.00013083195,0.00026253567],"domain_scores_gemma":[0.9993594,0.0002596007,0.00006991449,0.00018843035,0.00007669222,0.000045919845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035596054,0.00014451331,0.00036689057,0.00018713303,0.00005287526,0.00001645195,0.000054679287,0.000090663125,0.00007036994],"category_scores_gemma":[0.000047465182,0.00012676565,0.0001819361,0.00044681333,0.000012351921,0.000108718195,0.000029142315,0.000062599946,0.0000014223159],"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":[0.000021860804,0.000010753938,0.0049438123,0.00009064324,0.00043430322,0.0000020286204,0.00020575966,0.8300554,0.1602783,0.00009414376,0.000050419596,0.0038125496],"study_design_scores_gemma":[0.00021888406,0.000021435924,0.0002418173,0.00006895825,0.0002989511,0.0000061207543,0.000054266828,0.96061987,0.03801709,0.000009845675,0.00028643274,0.00015634767],"about_ca_topic_score_codex":0.0003251758,"about_ca_topic_score_gemma":0.00016969444,"teacher_disagreement_score":0.13056444,"about_ca_system_score_codex":0.00007533876,"about_ca_system_score_gemma":0.000029864574,"threshold_uncertainty_score":0.51693535},"labels":[],"label_agreement":null},{"id":"W2997330812","doi":"10.1007/s11356-019-07402-1","title":"A novel compound wind speed forecasting model based on the back propagation neural network optimized by bat algorithm","year":2019,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Wind speed; Artificial neural network; Computer science; Hilbert–Huang transform; Wind power; Data pre-processing; Renewable energy; Backpropagation; Algorithm; Preprocessor; Mode (computer interface); Data mining; Artificial intelligence; Engineering; Meteorology","score_opus":0.03811752443559387,"score_gpt":0.2512101685141574,"score_spread":0.21309264407856351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997330812","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":[0.9830438,0.00009131467,0.008577282,0.00050717226,0.00024640546,0.0005732075,0.00004262548,0.0000392287,0.006878965],"genre_scores_gemma":[0.99699306,0.000013734226,0.0022528442,0.00014746944,0.00006768254,0.000004016611,0.000015321277,0.000015044573,0.0004908088],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998159,0.00005201792,0.00014348925,0.00027010703,0.0008324725,0.000542895],"domain_scores_gemma":[0.9995566,0.00011347042,0.000024547331,0.00017564694,0.000012019588,0.00011772728],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015823306,0.00012491719,0.00009423548,0.000081573955,0.00057380996,0.00014269231,0.00022217487,0.000047698235,0.00015451432],"category_scores_gemma":[0.000022760936,0.00009257474,0.000024706487,0.00041143518,0.00045839418,0.0002676996,0.00008857481,0.0003061163,0.000054191827],"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":[0.000013033482,0.000018840807,0.00010388089,0.0000046985724,0.0000022891077,4.398222e-7,0.000073650204,0.9269833,0.06595652,0.00003984811,0.00062798016,0.006175523],"study_design_scores_gemma":[0.0003860334,0.0000737795,0.00035266718,0.00002968451,0.0000015365044,0.0000050260046,0.00007890161,0.9954358,0.0029392445,0.000023091896,0.00055935537,0.000114905735],"about_ca_topic_score_codex":0.000018367668,"about_ca_topic_score_gemma":8.9976834e-7,"teacher_disagreement_score":0.06845248,"about_ca_system_score_codex":0.00020316207,"about_ca_system_score_gemma":0.000030326937,"threshold_uncertainty_score":0.44133404},"labels":[],"label_agreement":null},{"id":"W2997340036","doi":"","title":"A High Resolution Wind Forecasting System and its Real-Time Application for Wind Plants' Management in Quebec","year":2009,"lang":"en","type":"article","venue":"AGUFM","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Meteorology; Environmental science; Remote sensing; Computer science; Geography","score_opus":0.010298279785161128,"score_gpt":0.19964852022351892,"score_spread":0.1893502404383578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997340036","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":[0.9900411,0.00024519107,0.0018772053,0.000029765251,0.000099683886,0.00042892134,0.000011713978,0.0002229719,0.0070434655],"genre_scores_gemma":[0.9980458,0.00003381314,0.0014098393,0.0000111940435,0.00011576437,0.000021143282,0.000049323426,0.000018107014,0.00029499864],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992997,0.000009829185,0.00020864246,0.00017377305,0.00007504348,0.00023300613],"domain_scores_gemma":[0.99976915,0.00003810529,0.000038017944,0.00010260987,0.000011278978,0.000040833216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015080984,0.000118880474,0.00013711899,0.00009743936,0.0000568612,0.000022723196,0.00006114681,0.000067996465,0.0000011352813],"category_scores_gemma":[0.0000047571366,0.00012710903,0.000020277641,0.000090180096,0.0000046857795,0.000120520315,0.00001218297,0.00005430088,0.0000060552875],"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":[0.00011451856,0.00004740336,0.00016548432,0.00097229343,0.000086975895,0.00004586453,0.001333054,0.7433404,0.021485286,0.047756303,0.0004306414,0.18422179],"study_design_scores_gemma":[0.00072672626,0.00004785735,0.0030040352,0.00039386994,0.000020908077,0.000015910296,0.00010711516,0.991838,0.001716298,0.00023466836,0.0016571009,0.00023756016],"about_ca_topic_score_codex":0.0009301497,"about_ca_topic_score_gemma":0.00047863062,"teacher_disagreement_score":0.24849756,"about_ca_system_score_codex":0.000107403794,"about_ca_system_score_gemma":0.000004227032,"threshold_uncertainty_score":0.5183356},"labels":[],"label_agreement":null},{"id":"W2998878989","doi":"10.1007/s42835-020-00346-4","title":"A Framework of Using Machine Learning Approaches for Short-Term Solar Power Forecasting","year":2020,"lang":"en","type":"article","venue":"Journal of Electrical Engineering and Technology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":133,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"University of Regina","keywords":"Feature selection; Machine learning; Artificial intelligence; Random forest; Computer science; Artificial neural network; Solar power; Gradient boosting; Feature (linguistics); Ensemble learning; Term (time); Data mining; Power (physics)","score_opus":0.029972432061908017,"score_gpt":0.21570555012344209,"score_spread":0.18573311806153406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2998878989","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":[0.58732074,0.004001373,0.40828744,0.00008320248,0.00010052308,0.0000533597,0.0000017523931,0.00012648704,0.000025126368],"genre_scores_gemma":[0.95670205,0.0000744475,0.04306213,0.000005523357,0.000112360096,0.0000018095159,7.050237e-7,0.000039923492,0.0000010511214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990969,0.0000064599385,0.000415766,0.00010525543,0.00009532284,0.0002803372],"domain_scores_gemma":[0.9995322,0.00016818848,0.00009253215,0.00005562396,0.00005756347,0.00009386852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014521621,0.00015861582,0.00039215325,0.0003013357,0.00003963129,0.000013387704,0.00013272956,0.00022678147,0.0000020715765],"category_scores_gemma":[0.0005984221,0.00014801274,0.0000881543,0.00053061463,0.00002405051,0.0000703926,0.000029599336,0.00075895275,6.4120556e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000052714488,0.000029761177,0.005603393,0.00034470414,0.00029223622,0.00004037069,0.00034686094,0.8876754,0.06194512,0.004352586,0.0000101006735,0.039306726],"study_design_scores_gemma":[0.00021055434,0.00040446772,0.000021363141,0.00012856425,0.00004314051,0.00023269787,0.000016390808,0.98513496,0.012886724,0.00021428215,0.0005620181,0.00014481616],"about_ca_topic_score_codex":3.8884073e-7,"about_ca_topic_score_gemma":8.821022e-8,"teacher_disagreement_score":0.3693813,"about_ca_system_score_codex":0.000024886596,"about_ca_system_score_gemma":0.000014667577,"threshold_uncertainty_score":0.60357845},"labels":[],"label_agreement":null},{"id":"W2999869395","doi":"10.3390/en13020391","title":"Multi-Sequence LSTM-RNN Deep Learning and Metaheuristics for Electric Load Forecasting","year":2020,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":246,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"United Arab Emirates University","keywords":"Computer science; Hyperparameter; Artificial intelligence; Machine learning; Smart grid; Metaheuristic; Energy consumption; Domain knowledge; Mathematical optimization; Engineering","score_opus":0.03967173078159233,"score_gpt":0.23798460567370353,"score_spread":0.1983128748921112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999869395","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":[0.81028754,0.018087503,0.16340803,0.00014714988,0.0007022825,0.00027455296,0.000016224974,0.0017920105,0.005284691],"genre_scores_gemma":[0.9638089,0.00036568774,0.035272114,0.000059766793,0.00026260642,0.000025954765,0.000011054437,0.00005722018,0.00013670367],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990903,0.000017273624,0.00021274322,0.00021540833,0.00011538893,0.00034888918],"domain_scores_gemma":[0.99949497,0.00023410947,0.000043571825,0.000065009735,0.00005320271,0.000109134824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011484814,0.00019454972,0.0002147208,0.000047409278,0.00015070147,0.00006700405,0.00010361929,0.00007056265,0.000008531098],"category_scores_gemma":[0.0005249726,0.00019729108,0.000056366425,0.00021076215,0.000025372681,0.0001240919,0.000040241062,0.00019820972,0.000003528812],"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":[0.00001893755,0.0000072891885,0.0009644881,0.00030222072,0.000093866045,0.000021047214,0.001881685,0.836117,0.08546162,0.0011383073,0.00019101372,0.07380252],"study_design_scores_gemma":[0.00031204708,0.000071075796,0.00005056765,0.0000286561,0.000032765907,0.0000118799435,0.00012768242,0.9595637,0.013540582,0.000056852,0.025947768,0.0002564367],"about_ca_topic_score_codex":0.000011238628,"about_ca_topic_score_gemma":0.000015770336,"teacher_disagreement_score":0.15352133,"about_ca_system_score_codex":0.00003310433,"about_ca_system_score_gemma":0.000015980522,"threshold_uncertainty_score":0.80452967},"labels":[],"label_agreement":null},{"id":"W3000200386","doi":"","title":"Examination of Discontinuities of Monthly Wind Speed and Measured Trends in Canada","year":2007,"lang":"en","type":"article","venue":"AGUFM","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Meteorology; Classification of discontinuities; Wind speed; Environmental science; Geology; Climatology; Geodesy; Geography; Mathematics","score_opus":0.010357310755371557,"score_gpt":0.18550002016919281,"score_spread":0.17514270941382126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000200386","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":[0.9808418,0.00031124605,0.000011198819,0.0000029734094,0.00010227235,0.00001120258,0.000006612941,0.0000065992017,0.018706087],"genre_scores_gemma":[0.99980485,0.000003991088,0.000046145193,0.0000014471619,0.0000135254495,7.777637e-8,0.0000049520654,0.000005915386,0.000119101176],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996368,0.0000055773035,0.00014860551,0.00004014654,0.00008167625,0.00008713704],"domain_scores_gemma":[0.9998689,0.00003212557,0.000023725415,0.000045374305,0.000011927927,0.000017919905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012962757,0.00004649825,0.000097476324,0.000082275306,0.000004701763,0.0000016998224,0.000027149497,0.000018898263,0.000007851787],"category_scores_gemma":[0.000009922851,0.000046761106,0.000008850463,0.000105465835,0.00001194059,0.000043408214,0.000005088219,0.000033108226,3.0373954e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000032914304,0.00003263479,0.10751904,0.0002935074,0.000089891815,0.000041225856,0.011861561,0.3040766,0.063799605,0.0008924835,0.0001954116,0.5111651],"study_design_scores_gemma":[0.0003489717,0.000020265492,0.91009414,0.00009805281,0.0000079045585,0.0000013184977,0.000910733,0.02014936,0.068001665,0.000020086894,0.00023201953,0.000115504394],"about_ca_topic_score_codex":0.34099618,"about_ca_topic_score_gemma":0.89349383,"teacher_disagreement_score":0.80257505,"about_ca_system_score_codex":0.000039412793,"about_ca_system_score_gemma":0.000012996666,"threshold_uncertainty_score":0.6633922},"labels":[],"label_agreement":null},{"id":"W3003350829","doi":"10.1109/pesgm40551.2019.8973554","title":"Direct Interval Forecast of Uncertain Wind Power Based on Recurrent Neural Networks","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Prediction interval; Wind power; Recurrent neural network; Computer science; Artificial neural network; Interval (graph theory); Nonparametric statistics; Mathematical optimization; Electric power system; Upper and lower bounds; Power (physics); Artificial intelligence; Machine learning; Statistics; Mathematics; Engineering","score_opus":0.011273078202576241,"score_gpt":0.21140111248157803,"score_spread":0.20012803427900178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003350829","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":[0.7675282,0.00011880167,0.0029777824,0.00002810343,0.0022899867,0.000141342,0.000008714385,0.00022983062,0.22667727],"genre_scores_gemma":[0.99926704,0.0000029818834,0.00021080748,0.000075039665,0.00006591798,0.0000022188692,0.000015399626,0.00003192753,0.00032869013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992275,0.000021501279,0.00021816723,0.00015007367,0.0001287679,0.00025394937],"domain_scores_gemma":[0.9995616,0.00011183323,0.000031419906,0.00021350368,0.000021721304,0.000059919043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011241543,0.00016378381,0.00020726629,0.000075347794,0.000014998528,0.000014880419,0.00012372143,0.00006861544,0.000619062],"category_scores_gemma":[0.000011430464,0.000136204,0.00010642533,0.00014338762,0.000014759641,0.00006103561,0.00002061517,0.00015796482,0.000017534278],"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":[0.000029978291,0.000020480291,0.0028980996,0.000029221343,0.000017246979,0.0000021936855,0.000058579633,0.9866887,0.00015323088,0.00015627069,0.0008021109,0.009143891],"study_design_scores_gemma":[0.00029765363,0.00021648529,0.00065680244,0.00013090148,0.000005412745,0.0000012654763,0.000018525252,0.9944652,0.0016176634,0.0000036219476,0.0024219153,0.00016456054],"about_ca_topic_score_codex":0.000014805211,"about_ca_topic_score_gemma":0.000015166535,"teacher_disagreement_score":0.23173885,"about_ca_system_score_codex":0.000031829335,"about_ca_system_score_gemma":0.000005712556,"threshold_uncertainty_score":0.6778295},"labels":[],"label_agreement":null},{"id":"W3004401463","doi":"10.1109/pesgm40551.2019.8973934","title":"Foretasting-Aided State Estimator for an Electrical Distribution System","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Waterloo","funders":"","keywords":"Observability; Backup; Kalman filter; Estimator; Extended Kalman filter; Computer science; Control theory (sociology); Algorithm; Artificial intelligence; Mathematics; Statistics","score_opus":0.009373355061100248,"score_gpt":0.21044991723338852,"score_spread":0.20107656217228828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004401463","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":[0.59324384,0.000043223503,0.3985197,0.0000049082487,0.00045974238,0.0002486151,0.00005808863,0.0009443197,0.0064775837],"genre_scores_gemma":[0.9953247,9.201525e-7,0.0041208556,0.0000067628694,0.0000694957,0.000025189289,0.00019527998,0.000030291447,0.00022654848],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992834,0.000008257868,0.00018140867,0.0001391842,0.0000905059,0.00029724115],"domain_scores_gemma":[0.9996606,0.00006602268,0.000021074802,0.00013283604,0.0000342443,0.00008523492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000130569,0.00011787264,0.00014450154,0.000026932412,0.000048331138,0.000043940865,0.000089216366,0.00004805802,0.000015345306],"category_scores_gemma":[0.000015311609,0.00010523377,0.000047588914,0.00010379266,0.000005214848,0.00014904977,0.000009084822,0.00006785304,0.00004039763],"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":[0.00016158374,0.000088650675,0.013097832,0.0019095165,0.00022474257,0.000009933608,0.0002936877,0.67160785,0.028934153,0.20104429,0.009430533,0.07319724],"study_design_scores_gemma":[0.00030689553,0.00011528983,0.00025027076,0.00003727394,0.000008761493,0.00001111763,0.000018145554,0.9900187,0.006777597,0.000105173305,0.0021887808,0.00016199125],"about_ca_topic_score_codex":0.000009492094,"about_ca_topic_score_gemma":0.000005974115,"teacher_disagreement_score":0.40208083,"about_ca_system_score_codex":0.0000820353,"about_ca_system_score_gemma":0.000010570964,"threshold_uncertainty_score":0.42913085},"labels":[],"label_agreement":null},{"id":"W3004665554","doi":"10.1016/j.energy.2020.117081","title":"Wind power forecasting using attention-based gated recurrent unit network","year":2020,"lang":"en","type":"article","venue":"Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":321,"is_retracted":false,"has_abstract":false,"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; National Natural Science Foundation of China","keywords":"Wind power forecasting; Feature selection; Computer science; Wind power; Selection (genetic algorithm); Task (project management); Artificial intelligence; Machine learning; Model selection; Electric power system; Feature (linguistics); Power (physics); Sequence (biology); Data mining; Engineering; Systems engineering","score_opus":0.03768735253833277,"score_gpt":0.21862834515258267,"score_spread":0.1809409926142499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004665554","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":[0.84960777,0.0020905915,0.11695004,0.00025274386,0.003058895,0.000107988766,0.000021047781,0.0014703652,0.026440548],"genre_scores_gemma":[0.9962878,0.000007981284,0.00248716,0.0003860435,0.0006505918,0.0000019223623,0.000060554576,0.00007276477,0.000045147575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988672,0.000036362686,0.00028646798,0.00021612317,0.00015868954,0.00043516632],"domain_scores_gemma":[0.99951607,0.00006483581,0.00005465957,0.00014088902,0.000041199917,0.00018232102],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007949199,0.00022044354,0.00020423805,0.000050529667,0.00012990621,0.00004709983,0.00013885167,0.000093321796,0.0002511526],"category_scores_gemma":[0.000031813623,0.00023517529,0.00009559273,0.0005026288,0.000021226817,0.000120476514,0.000033709035,0.0001717625,0.000011096168],"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":[0.000011336883,0.0000067943324,0.00059282815,0.000022017008,0.000034925022,0.00002059924,0.00006529467,0.99137706,0.0014508311,0.0005544125,0.001002492,0.004861385],"study_design_scores_gemma":[0.00023978454,0.000047105328,0.00008314735,0.00014741559,0.000018958532,0.0000050860817,0.000016929082,0.9284602,0.0013010851,0.00004971142,0.069341525,0.00028906253],"about_ca_topic_score_codex":0.000035718214,"about_ca_topic_score_gemma":0.000023368115,"teacher_disagreement_score":0.14668006,"about_ca_system_score_codex":0.000037709826,"about_ca_system_score_gemma":0.000026880745,"threshold_uncertainty_score":0.95901704},"labels":[],"label_agreement":null},{"id":"W3008533347","doi":"10.1109/access.2020.2975738","title":"Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":201,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Recurrent neural network; Artificial intelligence; Deep learning; Artificial neural network; Feed forward; Encoder; Machine learning; Sequence learning; Scheduling (production processes); Sequence (biology); Feedforward neural network; Time horizon; Control engineering; Engineering; Mathematical optimization","score_opus":0.0776218189074291,"score_gpt":0.28435029029029035,"score_spread":0.20672847138286127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3008533347","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":[0.4483853,0.000142312,0.5493989,0.00015919143,0.0007178169,0.0002742927,0.0000035356454,0.00042101517,0.0004976096],"genre_scores_gemma":[0.99705344,0.000015378271,0.0019558342,0.00024264611,0.0005465385,0.0000830044,0.000022523704,0.00005810882,0.000022506862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988172,0.000019641553,0.00022956873,0.0003112799,0.00018445424,0.0004378305],"domain_scores_gemma":[0.9994274,0.000083177656,0.00006119233,0.00011734056,0.000105113875,0.00020577677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010716799,0.00021624986,0.00019220756,0.000039234874,0.00013742113,0.00017410131,0.0003376537,0.0000653284,0.000010337894],"category_scores_gemma":[0.000073261064,0.00020253676,0.00005807162,0.0003980639,0.000021258746,0.000434181,0.000043793407,0.00028159394,0.0000052161768],"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":[0.000039565053,0.0000035419619,0.0021894076,0.0000886166,0.000017412123,0.000013718412,0.00025858852,0.94243574,0.0013735646,0.0000148222725,0.00015890777,0.053406086],"study_design_scores_gemma":[0.0002709522,0.00018040901,0.00015469117,0.00013013846,0.000019417019,0.000016085789,0.000024654486,0.9961735,0.0010490471,0.0000094158195,0.0016863744,0.0002853045],"about_ca_topic_score_codex":0.000019073217,"about_ca_topic_score_gemma":0.0000653709,"teacher_disagreement_score":0.54866815,"about_ca_system_score_codex":0.00008315261,"about_ca_system_score_gemma":0.000015525953,"threshold_uncertainty_score":0.82592094},"labels":[],"label_agreement":null},{"id":"W3013162446","doi":"10.29007/mbb7","title":"Reducing error propagation for long term energy forecasting using multivariate prediction","year":2020,"lang":"en","type":"article","venue":"EPiC series in computing","topic":"Energy Load and Power Forecasting","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":"Trent University; Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Term (time); Computer science; Feature (linguistics); Energy (signal processing); Artificial intelligence; Machine learning; Multivariate statistics; Energy consumption; Mean absolute percentage error; Data mining; Econometrics; Statistics; Artificial neural network; Mathematics; Engineering","score_opus":0.0515206301022517,"score_gpt":0.25466496531700444,"score_spread":0.20314433521475275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013162446","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":[0.5915954,0.00012773742,0.4062755,0.000049789545,0.0009403359,0.00015470643,0.000005456107,0.00038563996,0.00046541425],"genre_scores_gemma":[0.9694812,0.0000036266883,0.029327035,0.000050388324,0.0010141152,0.000011457523,0.000038921356,0.000063863954,0.000009340648],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986454,0.000035250927,0.0004999619,0.00030087153,0.00011574686,0.00040273377],"domain_scores_gemma":[0.99952924,0.0001365294,0.00010649299,0.000109145636,0.000042341817,0.00007625969],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022477566,0.00021070559,0.00023766868,0.00009533857,0.00019808068,0.000064785156,0.00012311352,0.00009750012,0.000007168347],"category_scores_gemma":[0.00019890183,0.00023974423,0.0000611984,0.00032171773,0.000025298972,0.0003542754,0.00006822742,0.0001751033,4.7274008e-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":[0.00002614357,0.000007002308,0.0063099014,0.00027465448,0.000025137466,0.000010345803,0.002632682,0.93489033,0.009599365,0.00024762852,0.000017578914,0.04595925],"study_design_scores_gemma":[0.00041343804,0.000054960416,0.0013866656,0.0004439799,0.000012988199,0.0000271433,0.00013762273,0.98587966,0.011200621,0.00006178933,0.00015826784,0.000222878],"about_ca_topic_score_codex":0.000043114356,"about_ca_topic_score_gemma":0.000018152274,"teacher_disagreement_score":0.37788585,"about_ca_system_score_codex":0.000119505494,"about_ca_system_score_gemma":0.000023731642,"threshold_uncertainty_score":0.97764856},"labels":[],"label_agreement":null},{"id":"W3014566940","doi":"10.1109/ccece43985.2019.9052396","title":"An Overview of Wind-Solar System Output Power","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Wind power; Computer science; Environmental science; Solar power; Electric power system; Photovoltaic system; Meteorology; Power (physics); Electrical engineering; Engineering; Physics","score_opus":0.02024872238221992,"score_gpt":0.22745355000776984,"score_spread":0.20720482762554993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3014566940","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":[0.7813975,0.0009767729,0.0006284776,0.0000026252012,0.00063975964,0.0000597514,0.000004354166,0.0003186284,0.21597211],"genre_scores_gemma":[0.9987609,0.00001596372,0.0004181494,0.000015712734,0.000030037852,6.856986e-7,0.0000035664739,0.000022873917,0.00073211023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999513,0.000010348186,0.00015721229,0.000086848486,0.000092934955,0.0001396931],"domain_scores_gemma":[0.9996677,0.000013996571,0.0000177222,0.00023538712,0.000018039951,0.000047138547],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009087512,0.00008716298,0.00015376092,0.000037950875,0.000009404209,0.000010623581,0.00010506125,0.000052595555,0.00025207864],"category_scores_gemma":[0.0000016890053,0.00007568243,0.000044715776,0.000077340184,0.000004720601,0.00011724894,0.000011771232,0.00005687784,0.00013090548],"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":[0.000027686137,0.00015651723,0.06475019,0.007032173,0.00047861,0.000037779784,0.003327526,0.545193,0.075693935,0.27874538,0.003549383,0.021007784],"study_design_scores_gemma":[0.002565429,0.0009007539,0.032055903,0.003410761,0.00011290236,0.00012732891,0.0028411737,0.59046066,0.12547143,0.00014823086,0.23944217,0.0024632497],"about_ca_topic_score_codex":0.000021563887,"about_ca_topic_score_gemma":0.0000037271,"teacher_disagreement_score":0.27859715,"about_ca_system_score_codex":0.000017583363,"about_ca_system_score_gemma":0.0000050414956,"threshold_uncertainty_score":0.308624},"labels":[],"label_agreement":null},{"id":"W3016073894","doi":"10.18280/ejee.220104","title":"A Short-Term Output Power Prediction Model of Wind Power Based on Deep Learning of Grouped Time Series","year":2020,"lang":"en","type":"article","venue":"European Journal of Electrical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"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":"Natural Science Foundation of Inner Mongolia; Inner Mongolia Agricultural University; National Natural Science Foundation of China","keywords":"Wind power; Randomness; Artificial neural network; Time series; Wind speed; Computer science; Power (physics); Term (time); Sliding window protocol; Deep learning; Data set; Artificial intelligence; Machine learning; Engineering; Statistics; Meteorology; Mathematics","score_opus":0.008744304023304377,"score_gpt":0.16915509183112357,"score_spread":0.1604107878078192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016073894","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":[0.63551277,0.0003777814,0.35809073,0.000037059322,0.00020998402,0.00006803491,0.000007071478,0.00017731669,0.005519286],"genre_scores_gemma":[0.9970161,0.000019459934,0.0027253407,0.000015226884,0.000118282966,2.2510596e-7,0.0000026515443,0.00008354464,0.000019202635],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998596,0.000057188186,0.00066468737,0.00011759081,0.00031126296,0.0002532417],"domain_scores_gemma":[0.9994314,0.000079065656,0.00012740238,0.00009544414,0.000089055735,0.0001776371],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030865686,0.00020807004,0.00036452364,0.00021847863,0.000023447477,0.000016179778,0.00019245205,0.000045108114,0.000021478334],"category_scores_gemma":[0.00021742735,0.00020002053,0.00017587582,0.00032843932,0.000015390026,0.00017469795,0.00002088272,0.00059864344,0.000003628248],"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":[0.000080829384,0.000021126494,0.00019883878,0.000044264896,0.000059793012,0.000039271745,0.0003340536,0.9032905,0.094445504,0.00004394463,0.000028257788,0.001413591],"study_design_scores_gemma":[0.00039149862,0.0010466782,0.0015911545,0.00017241777,0.000031951182,0.000022565135,0.0000067910764,0.9873141,0.009042074,0.000001157144,0.00021978046,0.00015981065],"about_ca_topic_score_codex":8.513163e-8,"about_ca_topic_score_gemma":1.1121557e-8,"teacher_disagreement_score":0.36150333,"about_ca_system_score_codex":0.000038331797,"about_ca_system_score_gemma":0.000014791571,"threshold_uncertainty_score":0.81566006},"labels":[],"label_agreement":null},{"id":"W3016782295","doi":"10.1109/iccs45141.2019.9065415","title":"Short-term Wind and PV Generation Forecasting of time-series using ANN","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"Nonlinear autoregressive exogenous model; Mean squared error; Artificial neural network; Renewable energy; Computer science; Term (time); Photovoltaic system; Time series; Wind power; Statistics; Machine learning; Engineering; Mathematics","score_opus":0.02651390872877309,"score_gpt":0.2130003969792008,"score_spread":0.1864864882504277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016782295","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":[0.987206,0.0001551947,0.0016890806,0.0000023519995,0.00018176904,0.00005135961,0.000002635601,0.00006244109,0.010649202],"genre_scores_gemma":[0.99382484,0.000012659837,0.005640065,0.0000042058205,0.00011027183,4.4034186e-7,0.000010015209,0.000020066416,0.0003774219],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995531,0.000005765225,0.0001552862,0.00009345462,0.00006406685,0.00012836447],"domain_scores_gemma":[0.9998316,0.000017885666,0.000016882184,0.00008448888,0.000019944402,0.000029170495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000066753164,0.00009153962,0.000121868936,0.00004664647,0.00002975245,0.000022817301,0.000033415952,0.000047631496,0.00010518132],"category_scores_gemma":[0.0000060144857,0.000086993496,0.0000209675,0.00006256968,0.000014887388,0.00023128466,0.000021197517,0.000046153647,0.0000043729806],"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":[0.0000054633542,0.0000062802123,0.035221487,0.00014601869,0.000042608874,0.0000028703575,0.0004922194,0.4118226,0.53974295,0.00029398847,0.000080536585,0.012143007],"study_design_scores_gemma":[0.00009213126,0.000028499247,0.000921639,0.000056533292,0.000010025677,0.000026104797,0.000030789037,0.925211,0.07330859,0.00001551192,0.00015795803,0.00014119604],"about_ca_topic_score_codex":0.000007150454,"about_ca_topic_score_gemma":0.0000065511954,"teacher_disagreement_score":0.51338845,"about_ca_system_score_codex":0.000010828795,"about_ca_system_score_gemma":0.0000045548118,"threshold_uncertainty_score":0.3547492},"labels":[],"label_agreement":null},{"id":"W3017113651","doi":"10.1109/icaiic48513.2020.9065071","title":"RNN-based Deep Learning for One-hour ahead Load Forecasting","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Recurrent neural network; Computer science; Long short term memory; Term (time); Artificial intelligence; Artificial neural network; Machine learning; Scheme (mathematics); Deep learning; Probabilistic forecasting","score_opus":0.04056688952661744,"score_gpt":0.21848958210269118,"score_spread":0.17792269257607374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017113651","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":[0.094177164,0.00045687644,0.8381289,0.00052222563,0.00048288997,0.0002932369,0.000006811036,0.0023798407,0.063552015],"genre_scores_gemma":[0.97793853,0.000004419389,0.020939868,0.00037129404,0.00043677245,0.00002817822,0.000019484327,0.0000793155,0.00018215486],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896765,0.000012699165,0.0002440824,0.00021044002,0.00015816312,0.000406979],"domain_scores_gemma":[0.999405,0.0002482941,0.00003335856,0.000090090405,0.000057417365,0.00016580506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013470599,0.00018717907,0.00020471745,0.00003753343,0.00012351322,0.000054172633,0.00013208983,0.00008568144,0.0001583613],"category_scores_gemma":[0.00031540488,0.00019500346,0.00011168667,0.00018250075,0.000012980644,0.000102627855,0.000021068601,0.00022466002,0.00003816866],"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":[0.000024058,0.0000090817375,0.00072866934,0.0002393116,0.00003930666,0.000005076901,0.00052618166,0.9227168,0.0044978494,0.0004012252,0.0005419432,0.070270516],"study_design_scores_gemma":[0.0004309933,0.00010081646,0.00003227661,0.000066465174,0.000016855034,0.0000014861163,0.000084739266,0.95441025,0.011291075,0.00003048244,0.033277452,0.00025708252],"about_ca_topic_score_codex":0.000012779519,"about_ca_topic_score_gemma":0.00004279783,"teacher_disagreement_score":0.88376135,"about_ca_system_score_codex":0.00005107213,"about_ca_system_score_gemma":0.00002465714,"threshold_uncertainty_score":0.79520106},"labels":[],"label_agreement":null},{"id":"W3018313252","doi":"10.1109/epec47565.2019.9074819","title":"Machine Learning-Based Demand and PV Power Forecasts","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Support vector machine; Demand forecasting; Perceptron; Demand response; Renewable energy; Artificial neural network; Electricity generation; Power (physics); Machine learning; Artificial intelligence; Operations research; Engineering; Electricity","score_opus":0.0049892193653989884,"score_gpt":0.17788612305728005,"score_spread":0.17289690369188107,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018313252","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":[0.85617226,0.00075378583,0.005997029,0.00003223115,0.0002984602,0.00006892769,0.0000022016357,0.00039536564,0.13627973],"genre_scores_gemma":[0.9971022,0.000014479405,0.00056870875,0.00006495353,0.000020936515,0.000002020084,0.000008309583,0.00002731405,0.002191061],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954116,0.000008692437,0.00009438688,0.00010960623,0.00006791178,0.0001782511],"domain_scores_gemma":[0.99978215,0.000047819398,0.000010783704,0.00008878611,0.000009688368,0.00006077924],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000073728566,0.00011001174,0.000107518434,0.00004903686,0.000029127243,0.00002530781,0.000043767162,0.000051067163,0.0007400349],"category_scores_gemma":[0.000008679608,0.00009382942,0.000026546732,0.00006257828,0.0000107795595,0.000065621694,0.000015355072,0.00012579064,0.00008186625],"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":[0.000023003675,0.000020001755,0.12860045,0.00019583465,0.00006349829,0.000013675877,0.00032651663,0.85614854,0.0028586884,0.0022906167,0.0006744195,0.008784761],"study_design_scores_gemma":[0.0005781823,0.00009215923,0.0021720831,0.000045566576,0.000006181941,0.000009434049,0.000015374813,0.9461504,0.0038597793,0.00006272479,0.046769463,0.00023861704],"about_ca_topic_score_codex":0.00001192393,"about_ca_topic_score_gemma":0.000019015675,"teacher_disagreement_score":0.14092994,"about_ca_system_score_codex":0.0000088475135,"about_ca_system_score_gemma":0.0000039846723,"threshold_uncertainty_score":0.81028634},"labels":[],"label_agreement":null},{"id":"W3019005994","doi":"10.1109/tpwrs.2020.2989725","title":"Rotor Angle Stability Prediction of Power Systems With High Wind Power Penetration Using a Stability Index Vector","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":36,"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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electric power system; Control theory (sociology); Wind power; Stability (learning theory); Rotor (electric); Engineering; Computer science; Power (physics); Artificial intelligence; Physics; Machine learning; Electrical engineering","score_opus":0.0241684268153288,"score_gpt":0.19839873215791684,"score_spread":0.17423030534258804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3019005994","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":[0.57727545,0.00014417863,0.41769573,0.0000130970975,0.0026633062,0.0007603751,0.00045122142,0.00035353203,0.0006431306],"genre_scores_gemma":[0.9996367,0.0000041489525,0.00010392439,0.000011000554,0.00004810214,0.00006917318,0.000013451482,0.00009733083,0.000016167123],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973211,0.00019051044,0.0008865306,0.0005372356,0.00063366524,0.00043095334],"domain_scores_gemma":[0.99863034,0.00011542262,0.00019442076,0.0005484651,0.00024695313,0.00026436543],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00035686634,0.0004344217,0.0005933498,0.00015789873,0.00016676892,0.00011163844,0.00019219724,0.00028132767,0.00017410255],"category_scores_gemma":[0.000011609942,0.00040416722,0.00015087609,0.000629272,0.00009062935,0.0005322297,0.00000233582,0.00044797803,0.000010707258],"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":[0.00033966408,0.00023763151,0.0022707158,0.00071675907,0.0003096121,0.000008279227,0.0037007432,0.93714464,0.055147298,0.00005638009,0.00004736245,0.000020946976],"study_design_scores_gemma":[0.004848661,0.0042635477,0.010210703,0.0018240801,0.0004027566,0.00014271142,0.007035549,0.82203573,0.14490998,0.0000061228247,0.0022068026,0.002113362],"about_ca_topic_score_codex":0.0005142139,"about_ca_topic_score_gemma":0.00005547439,"teacher_disagreement_score":0.42236128,"about_ca_system_score_codex":0.0003473601,"about_ca_system_score_gemma":0.000108066255,"threshold_uncertainty_score":0.99984103},"labels":[],"label_agreement":null},{"id":"W3020126078","doi":"10.1109/epec47565.2019.9074784","title":"Probabilistic Models for Residential and Commercial Loads with High Time Resolution","year":2019,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Ontario Tech University","funders":"","keywords":"Load profile; Probabilistic logic; Electricity; Consumption (sociology); Residential area; Scale (ratio); Computer science; Environmental science; Engineering; Civil engineering; Artificial intelligence; Geography; Electrical engineering","score_opus":0.008452611471936839,"score_gpt":0.18332369936268078,"score_spread":0.17487108789074393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020126078","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":[0.94361794,0.00006533611,0.035178255,0.000053002346,0.00020018253,0.00030331887,0.000011112384,0.0002487585,0.020322122],"genre_scores_gemma":[0.99433017,0.00000301176,0.0040761237,0.00001684348,0.000091292895,0.000013847655,0.000019208028,0.000022033757,0.00142745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957454,0.000005858798,0.000092321236,0.0001088806,0.00006470413,0.00015366988],"domain_scores_gemma":[0.9997967,0.000044570752,0.000010923136,0.00009172937,0.00002180105,0.00003426686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006470613,0.000083995474,0.00010197457,0.00003038265,0.000035731573,0.000026428308,0.0000406462,0.00004662591,0.000052652416],"category_scores_gemma":[0.000004405809,0.00006932256,0.000014717979,0.000042681673,0.000015916316,0.00013401953,0.000013319004,0.00004788259,0.000015004059],"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":[0.00013480484,0.000013368892,0.00020469674,0.0001890894,0.00004114631,0.0000012093749,0.0001955012,0.95749354,0.0021352114,0.03449044,0.0031376788,0.0019633246],"study_design_scores_gemma":[0.00068053807,0.00013442263,0.00050719647,0.000052961812,0.000019591418,0.0000062906433,0.0000067180886,0.9937236,0.0009065842,0.0029276358,0.0008585553,0.0001759474],"about_ca_topic_score_codex":0.00005540203,"about_ca_topic_score_gemma":0.000081414284,"teacher_disagreement_score":0.050712276,"about_ca_system_score_codex":0.000017932176,"about_ca_system_score_gemma":0.000007718422,"threshold_uncertainty_score":0.28268918},"labels":[],"label_agreement":null},{"id":"W3024020705","doi":"","title":"Development of the GIC forecasting for Canadian Power grid","year":2017,"lang":"en","type":"article","venue":"EGUGA","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Power grid; Power (physics); Computer science","score_opus":0.030897660095361677,"score_gpt":0.21425721947224177,"score_spread":0.1833595593768801,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024020705","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":[0.92327875,0.000073083014,0.00028369637,0.000082197366,0.0024092027,0.00013596944,0.00002261936,0.00004208963,0.073672384],"genre_scores_gemma":[0.994368,6.1271567e-7,0.0051492276,0.00001986248,0.00010886219,0.000012154832,0.0000029166922,0.000018440267,0.00031990235],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952114,0.0000023203797,0.00013118474,0.00006689987,0.00005957427,0.00021886334],"domain_scores_gemma":[0.99962157,0.00001831595,0.000042494074,0.00023302551,0.000022362457,0.00006226142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011125029,0.00007540388,0.00007901484,0.000027607253,0.00041219784,0.000030097235,0.00026103877,0.000038128303,0.000028961407],"category_scores_gemma":[0.000065252876,0.000058292422,0.000043271753,0.00002346174,0.000020399168,0.000055511162,0.00002889921,0.000055958215,0.0000039194006],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000469416,0.00009039032,0.23335634,0.0015657599,0.0010384803,0.00004110198,0.03581682,0.07457355,0.03498126,0.033934817,0.06237372,0.52218086],"study_design_scores_gemma":[0.00047242126,0.000016180684,0.030527227,0.00029615322,0.00001565385,0.000010207058,0.000115099596,0.008626153,0.05036624,0.0002475972,0.9089203,0.00038676968],"about_ca_topic_score_codex":0.00080752,"about_ca_topic_score_gemma":0.035349857,"teacher_disagreement_score":0.8465466,"about_ca_system_score_codex":0.000047204303,"about_ca_system_score_gemma":0.00007346761,"threshold_uncertainty_score":0.9822525},"labels":[],"label_agreement":null},{"id":"W3024085441","doi":"10.23977/jeis.2020.51004","title":"Research on Short-Term Load Forecasting of Micro-Grid Based on PSO-SVM Model","year":2020,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Support vector machine; Particle swarm optimization; Grid; Computer science; Volatility (finance); Power grid; Term (time); Mathematical optimization; Data mining; Artificial intelligence; Machine learning; Power (physics); Econometrics; Mathematics","score_opus":0.05360084094915453,"score_gpt":0.2917639258341826,"score_spread":0.23816308488502808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3024085441","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":[0.9777495,0.00014774008,0.009200137,0.00021068868,0.00011199952,0.000051008028,0.0000043123064,0.000011847772,0.0125127155],"genre_scores_gemma":[0.99862456,0.00012326098,0.0010194641,0.00017105282,0.000054099022,5.40041e-7,8.324322e-7,0.00000438772,0.0000017791657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985604,0.000009799742,0.00040027758,0.000056221543,0.00070376863,0.000269489],"domain_scores_gemma":[0.99921876,0.00007446077,0.000109110806,0.000071737086,0.0004046577,0.000121263525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013683022,0.00007470173,0.000121398516,0.000296234,0.00012255284,0.0000874427,0.00022379341,0.000032946107,0.0000035039277],"category_scores_gemma":[0.00016970129,0.000061222854,0.00003616672,0.0005853299,0.00007391236,0.0013767596,0.000022075768,0.00039341548,0.0000013808759],"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":[0.000040588056,0.0000065647764,0.0000642863,0.000040469484,0.0000030798008,6.531148e-7,0.00055503857,0.9746433,0.014413349,0.0016677483,0.00018031363,0.008384613],"study_design_scores_gemma":[0.0001772205,0.00048894197,0.000073622716,0.000094260424,0.0000022493873,0.000008849388,0.00005325706,0.9557049,0.04202272,0.00004265884,0.0012712551,0.000060061764],"about_ca_topic_score_codex":5.462288e-7,"about_ca_topic_score_gemma":4.874281e-7,"teacher_disagreement_score":0.02760937,"about_ca_system_score_codex":0.00012230725,"about_ca_system_score_gemma":0.00034817363,"threshold_uncertainty_score":0.24965957},"labels":[],"label_agreement":null},{"id":"W3027588342","doi":"","title":"Wind Power Potential Variability in the Middle East","year":2018,"lang":"en","type":"article","venue":"Japan Geoscience Union","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Wind power; Middle East; Geology; Environmental science; Meteorology; Climatology; Geography; Engineering; Archaeology; Electrical engineering","score_opus":0.016406182935271844,"score_gpt":0.20230907939411355,"score_spread":0.1859028964588417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027588342","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":[0.9680437,0.000011545834,0.0058337585,0.000083000785,0.00096315215,0.00006219242,0.0000028829481,0.00007105407,0.024928728],"genre_scores_gemma":[0.9992931,0.0000014087644,0.00033970914,0.0000877507,0.00016542834,0.0000019104355,0.0000022296272,0.0000069665425,0.000101498066],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991343,0.00008496338,0.00013562331,0.0001700212,0.00019160983,0.00028350484],"domain_scores_gemma":[0.9996324,0.000037976803,0.00001683451,0.0002467483,0.000025719408,0.000040325038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012086855,0.00009038692,0.000068540045,0.00006257951,0.00012543093,0.00005470076,0.00030748916,0.00005102116,0.00008458181],"category_scores_gemma":[0.00007737902,0.00006588552,0.000026917009,0.00045357022,0.000166494,0.00019138528,0.000033008695,0.0001326987,0.0000354637],"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":[0.00010976647,0.0012621064,0.12847145,0.0002932072,0.00006388657,0.00008855177,0.2180117,0.124230705,0.22569253,0.06692212,0.005222633,0.22963135],"study_design_scores_gemma":[0.0009867642,0.00048309777,0.4222201,0.00025848695,0.0000202379,0.00014222026,0.005716206,0.508599,0.0036923648,0.0039713704,0.052758094,0.0011520932],"about_ca_topic_score_codex":0.00007711929,"about_ca_topic_score_gemma":0.00006229064,"teacher_disagreement_score":0.38436824,"about_ca_system_score_codex":0.000030463518,"about_ca_system_score_gemma":0.000014528489,"threshold_uncertainty_score":0.26867336},"labels":[],"label_agreement":null},{"id":"W3030029333","doi":"10.24018/ejece.2020.4.3.210","title":"Short-term Power Load Forecast of an Electrically Heated House in St. John’s, Newfoundland, Canada","year":2020,"lang":"en","type":"article","venue":"European Journal of Electrical Engineering and Computer Science","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Term (time); Computer science; Artificial neural network; Power (physics); Electric power system; Dropout (neural networks); Set (abstract data type); Real-time computing; Artificial intelligence; Machine learning","score_opus":0.0076497894435146535,"score_gpt":0.17483240003534398,"score_spread":0.16718261059182932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3030029333","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":[0.93611246,0.000451385,0.06293414,0.0000363877,0.0002237505,0.000032084063,0.0000012292503,0.00005857864,0.00015001302],"genre_scores_gemma":[0.9964056,0.00003839284,0.0033296235,0.000059343703,0.0001360946,1.6022193e-7,3.2022422e-7,0.000029492698,9.4666683e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998579,0.00003604608,0.0004592318,0.00017202372,0.00038905628,0.0003646405],"domain_scores_gemma":[0.9993169,0.00005219312,0.000053968102,0.00009737735,0.00010529585,0.0003742991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044003196,0.00016157718,0.00026420504,0.00017391164,0.000034873465,0.00006557374,0.00041431605,0.00001880841,0.0000017883017],"category_scores_gemma":[0.00006272216,0.00014511988,0.00003703076,0.001004125,0.00003355794,0.00024006129,0.000044623383,0.0003788999,2.9213876e-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":[0.000055172495,0.000049537364,0.0023428532,0.00004608604,0.000039506827,0.00086233934,0.0006473832,0.85853326,0.037069805,0.0001437141,0.00020841949,0.10000194],"study_design_scores_gemma":[0.00044248157,0.0008001876,0.027989496,0.000083736595,0.0000075818716,0.00022604907,0.000002505559,0.9673841,0.0019348607,0.0000017918129,0.0008860633,0.0002411656],"about_ca_topic_score_codex":0.00026221212,"about_ca_topic_score_gemma":0.00035440899,"teacher_disagreement_score":0.10885084,"about_ca_system_score_codex":0.0001369311,"about_ca_system_score_gemma":0.0001948557,"threshold_uncertainty_score":0.59178174},"labels":[],"label_agreement":null},{"id":"W3036632324","doi":"10.1109/access.2020.3004156","title":"Performance Prediction and Interpretation of a Refuse Plastic Fuel Fired Boiler","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","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":"Brandon University; University of Alberta","funders":"National Research Foundation of Korea; Sejong University; National Research Foundation","keywords":"Boiler (water heating); Artificial neural network; Incineration; Computer science; Mean absolute percentage error; Artificial intelligence; Machine learning; Process engineering; Engineering; Waste management","score_opus":0.018213127665311964,"score_gpt":0.221439290593916,"score_spread":0.20322616292860404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3036632324","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":[0.9937093,0.0001111434,0.004001937,0.000012365543,0.00040064022,0.00003171159,0.000008457588,0.000107148684,0.0016172564],"genre_scores_gemma":[0.9996955,0.0000945264,0.000059617763,0.000028365803,0.000093684095,0.000005363894,0.000005191329,0.000011443265,0.0000063170273],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996639,0.000004895229,0.00012618052,0.00007425169,0.000059112564,0.000071688264],"domain_scores_gemma":[0.9998431,0.00003421342,0.000023772891,0.000046168567,0.000015327005,0.000037417958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000022583812,0.000060527724,0.00007767196,0.000027833901,0.000014778353,0.00002200994,0.000069579095,0.000031177424,0.0000116570545],"category_scores_gemma":[0.00002238462,0.000059423106,0.000012281305,0.0000955869,0.000012683756,0.0003609929,0.000014079546,0.000059621576,0.0000022661523],"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":[0.00017392944,0.00002094563,0.0606571,0.0022129286,0.00010256415,0.000005059504,0.0074928286,0.842427,0.028373694,0.000032567972,0.0018147746,0.056686584],"study_design_scores_gemma":[0.00018247077,0.000044974782,0.012165187,0.00009724511,0.000011641301,0.0000017061702,0.000010527493,0.97671,0.010374061,0.0000073502156,0.00033264482,0.00006214269],"about_ca_topic_score_codex":0.0000042382153,"about_ca_topic_score_gemma":0.0000027194133,"teacher_disagreement_score":0.13428302,"about_ca_system_score_codex":0.0000069677853,"about_ca_system_score_gemma":0.0000035683063,"threshold_uncertainty_score":0.24232039},"labels":[],"label_agreement":null},{"id":"W3037903850","doi":"10.1609/aaai.v34i08.7027","title":"PIDS: An Intelligent Electric Power Management Platform","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Energy Load and Power Forecasting","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":"University of British Columbia","funders":"National Science Council; National Natural Science Foundation of China","keywords":"Electricity; Electric power system; Computer science; Consumption (sociology); Process (computing); Power consumption; Power management; Power (physics); Electric power; Demand response; Business; Environmental economics; Operations research; Operations management; Telecommunications; Risk analysis (engineering); Engineering; Economics; Electrical engineering","score_opus":0.06675763103910648,"score_gpt":0.2556870596545758,"score_spread":0.1889294286154693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037903850","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":[0.83871174,0.000102479775,0.0052321907,0.0008724029,0.00071836595,0.00053265487,0.000007765064,0.0005251395,0.15329725],"genre_scores_gemma":[0.9988927,0.00012984787,0.00053230335,0.00022181503,0.00010303036,0.000018837489,0.0000011311789,0.000035683985,0.000064632775],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9984364,0.00000606542,0.00049419544,0.00032372828,0.00035072523,0.0003888702],"domain_scores_gemma":[0.99937063,0.00002969856,0.000113942566,0.00015831421,0.0001531236,0.0001742621],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021406137,0.00026506046,0.00023717755,0.00010153802,0.00014400347,0.00011539654,0.0008726474,0.000087930326,0.0002023415],"category_scores_gemma":[0.00007735621,0.00021175285,0.00010343671,0.00062381395,0.00008311974,0.00026804532,0.00011767659,0.00040422988,0.00012329327],"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":[0.00014757986,0.0001423312,0.00015094383,0.00024273113,0.000101667036,0.0000027399356,0.004863363,0.009759173,0.07917509,0.7500253,0.0006188406,0.15477028],"study_design_scores_gemma":[0.000027657969,0.0003540101,0.000060674785,0.0002008371,0.000029244939,0.00000327943,0.0017919273,0.16132373,0.80598885,0.02839777,0.0014372615,0.00038476792],"about_ca_topic_score_codex":0.000005996859,"about_ca_topic_score_gemma":0.0000032990047,"teacher_disagreement_score":0.72681373,"about_ca_system_score_codex":0.000046027024,"about_ca_system_score_gemma":0.00001518201,"threshold_uncertainty_score":0.86350304},"labels":[],"label_agreement":null},{"id":"W3045793201","doi":"10.1109/access.2020.3012143","title":"A Stacking Ensemble Model to Predict Daily Number of Hospital Admissions for Cardiovascular Diseases","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":27,"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 British Columbia","funders":"Key Research and Development Program of Sichuan Province; National Natural Science Foundation of China","keywords":"Random forest; Mean squared error; Mean absolute percentage error; Support vector machine; Gradient boosting; Stacking; Decision tree; Lasso (programming language); Linear regression; Computer science; Statistics; Mean absolute error; Artificial intelligence; Feature selection; Machine learning; Mathematics","score_opus":0.0306794009126727,"score_gpt":0.27133014237862374,"score_spread":0.24065074146595103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045793201","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":[0.54329914,0.00045042371,0.45360696,0.00007895312,0.0004963396,0.00026091133,0.00020968751,0.0002558813,0.0013416791],"genre_scores_gemma":[0.9969992,0.00001936314,0.0024850126,0.00008783582,0.00027071094,0.000057483405,0.000012992334,0.000046063495,0.00002134698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924266,0.000006934022,0.00018019427,0.00018105768,0.00016802388,0.0002211549],"domain_scores_gemma":[0.99938786,0.000041547217,0.000020682071,0.00019412131,0.000055133914,0.0003006287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003811644,0.00013013756,0.00024568444,0.000026210548,0.00004689051,0.000042285912,0.000266246,0.00004397483,0.000016463362],"category_scores_gemma":[0.00010191431,0.00012837331,0.00026007724,0.00016771989,0.0000073571414,0.00023797683,0.0000517888,0.0000618794,0.0000038462217],"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":[0.000013742158,0.000016230151,0.001989615,0.00019860281,0.00021003767,0.000004011068,0.0005174977,0.98838925,0.0007447446,0.00006729002,0.004931682,0.002917315],"study_design_scores_gemma":[0.000637432,0.00008151324,0.00026035248,0.0001965693,0.00023623451,0.0000013125131,0.000056290257,0.9693241,0.021041125,0.00037133665,0.0073739816,0.00041976848],"about_ca_topic_score_codex":0.000012634206,"about_ca_topic_score_gemma":0.0000017166911,"teacher_disagreement_score":0.45370004,"about_ca_system_score_codex":0.000015512198,"about_ca_system_score_gemma":0.000035110603,"threshold_uncertainty_score":0.52349114},"labels":[],"label_agreement":null},{"id":"W3045941597","doi":"10.1109/icc40277.2020.9148937","title":"Electrical Load Forecasting Using Edge Computing and Federated Learning","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":246,"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":"Computer science; Smart meter; Deep learning; Big data; Smart grid; Enhanced Data Rates for GSM Evolution; Edge computing; Federated learning; Machine learning; Artificial intelligence; Scheme (mathematics); Data modeling; Edge device; Variety (cybernetics); Information privacy; Data mining; Computer security; Database; Engineering; Cloud computing","score_opus":0.039734403292077,"score_gpt":0.24496746011395826,"score_spread":0.20523305682188125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045941597","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":[0.75149834,0.002177193,0.21068472,0.00003840025,0.0010053116,0.00022488894,0.000003831494,0.0021632626,0.03220408],"genre_scores_gemma":[0.9832343,0.00005365967,0.01579189,0.00004939049,0.0006398962,0.0000026445384,0.000033114622,0.00012535373,0.00006973947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812156,0.00005907872,0.00048716503,0.0005232831,0.00024115271,0.000567746],"domain_scores_gemma":[0.99930984,0.00017605648,0.00010653492,0.00011778562,0.00008228202,0.00020752686],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023911288,0.00047877428,0.0005422187,0.00011031996,0.00029507047,0.00035777202,0.00015180669,0.000376676,0.000026008944],"category_scores_gemma":[0.00019683778,0.0005148248,0.00010439348,0.00025805726,0.000030349847,0.00007707872,0.0005459983,0.0018562354,0.000006224909],"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":[0.000008564125,0.000007031283,0.0014773295,0.00047402657,0.00015134145,0.00006857963,0.0006826542,0.94509685,0.004374821,0.00016222859,0.00019877753,0.047297813],"study_design_scores_gemma":[0.00019815245,0.000025183515,0.000058689602,0.00037453717,0.00004259236,0.00007034586,0.000045456083,0.99620783,0.0014436106,0.00013167271,0.0008474525,0.0005544577],"about_ca_topic_score_codex":0.00010915495,"about_ca_topic_score_gemma":0.000016102666,"teacher_disagreement_score":0.231736,"about_ca_system_score_codex":0.00020755624,"about_ca_system_score_gemma":0.00010324672,"threshold_uncertainty_score":0.99973035},"labels":[],"label_agreement":null},{"id":"W3081151853","doi":"10.5539/ijsp.v9n5p61","title":"Functional Time Series Analysis of Land Surface Temperature","year":2020,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"African Union","keywords":"Estimator; Spline (mechanical); Mathematics; Nonparametric statistics; Series (stratigraphy); Mean squared error; Econometrics; Inference; Parametric model; Parametric statistics; Functional data analysis; Applied mathematics; Statistics; Computer science","score_opus":0.010316444945841023,"score_gpt":0.21290843409894641,"score_spread":0.2025919891531054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081151853","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":[0.9817972,0.0002389787,0.016223308,0.000383126,0.00029104573,0.000023542334,0.0007090107,0.000011590533,0.0003221672],"genre_scores_gemma":[0.9872111,0.00007671951,0.012514524,0.000032060947,0.00010015315,1.5348405e-7,0.000040190036,0.0000048256466,0.000020263675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938786,0.00001557626,0.00028856506,0.00005537863,0.0002033565,0.00004927793],"domain_scores_gemma":[0.9994078,0.000096924006,0.00009141294,0.000030575848,0.00031531675,0.00005792017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013230955,0.00006253621,0.00017405495,0.00004455569,0.000013619975,0.000026127489,0.00007505123,0.000029069988,0.00017993822],"category_scores_gemma":[0.0000947226,0.00005299535,0.000049356015,0.000117720025,0.000036401172,0.0000928231,0.000016044734,0.000105415565,7.656026e-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":[0.00029574902,0.00005116143,0.108533174,0.000118461976,0.0033924961,0.000033695676,0.0008774817,0.8623527,0.0117364,0.00548275,0.0034257998,0.0037000831],"study_design_scores_gemma":[0.0021500648,0.0007657957,0.46207225,0.00018826037,0.0016062574,0.00015490805,0.00012012723,0.48945522,0.010419084,0.019940767,0.012392867,0.0007344015],"about_ca_topic_score_codex":0.0000043130353,"about_ca_topic_score_gemma":0.000007726784,"teacher_disagreement_score":0.37289754,"about_ca_system_score_codex":0.000015357033,"about_ca_system_score_gemma":0.00002060158,"threshold_uncertainty_score":0.21610877},"labels":[],"label_agreement":null},{"id":"W3081576544","doi":"10.3390/su12177076","title":"Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":158,"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 Windsor","funders":"","keywords":"Support vector machine; Term (time); Computer science; Long short term memory; Microgrid; Artificial intelligence; Machine learning; Artificial neural network; Regression; Algorithm; Data mining; Recurrent neural network; Statistics; Mathematics","score_opus":0.017270140487063587,"score_gpt":0.2432673267034357,"score_spread":0.2259971862163721,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3081576544","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":[0.99462223,0.0008422989,0.003031282,0.00011169335,0.00033462635,0.00037712386,0.000032452255,0.0002882979,0.0003599981],"genre_scores_gemma":[0.9989145,0.000038213686,0.0005725758,0.000020832555,0.0002896873,0.000021908972,0.000045734327,0.00006508543,0.000031476815],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99787253,0.00005994852,0.00061848736,0.0005217981,0.00031586736,0.0006113877],"domain_scores_gemma":[0.998776,0.000117185955,0.00006576466,0.0003802122,0.00034548546,0.00031535188],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043530582,0.00037765826,0.00051131565,0.00006784156,0.00012304726,0.000045834284,0.00023447143,0.00013306945,0.000055446224],"category_scores_gemma":[0.00028612037,0.00036040103,0.0001562042,0.00022821831,0.00018438682,0.00027756114,0.00021667578,0.0003797076,0.00000141122],"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":[0.0001783526,0.00012732815,0.34989116,0.006081091,0.00016609169,0.0008075345,0.0057673035,0.004637387,0.0125050945,0.00002123053,0.00047378265,0.61934364],"study_design_scores_gemma":[0.0023918638,0.0013718475,0.2537232,0.0009332086,0.00042051543,0.00073657045,0.002778484,0.2853501,0.4460947,0.0008343331,0.0017952897,0.0035698675],"about_ca_topic_score_codex":0.00002222925,"about_ca_topic_score_gemma":0.000016221173,"teacher_disagreement_score":0.6157738,"about_ca_system_score_codex":0.00037238697,"about_ca_system_score_gemma":0.00013971346,"threshold_uncertainty_score":0.9998848},"labels":[],"label_agreement":null},{"id":"W3083236402","doi":"10.1016/j.energy.2020.118773","title":"A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting","year":2020,"lang":"en","type":"article","venue":"Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":101,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Acadia University","funders":"","keywords":"Wind power; Wind speed; Computer science; Kalman filter; Artificial intelligence; Artificial neural network; Hybrid power; Deep learning; Machine learning; Power (physics); Engineering; Meteorology","score_opus":0.03484107561358816,"score_gpt":0.20837385773904996,"score_spread":0.1735327821254618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083236402","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":[0.11298862,0.0010021343,0.8776103,0.00009011724,0.0004372949,0.00008602451,0.000010069351,0.00035728046,0.0074181645],"genre_scores_gemma":[0.9938473,0.000044365937,0.005332582,0.00020982577,0.00026860897,0.000003913455,0.000028100141,0.00007851148,0.00018677018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99903744,0.000010253666,0.00025120607,0.0002498946,0.00010257713,0.0003485943],"domain_scores_gemma":[0.99955803,0.00010836707,0.000042858694,0.00008537168,0.00003255666,0.00017281847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007977694,0.0002084578,0.00021713907,0.00005409676,0.00010101605,0.000058904374,0.0001050284,0.000060508384,0.00002309297],"category_scores_gemma":[0.000067438865,0.00021810512,0.00007381265,0.00010310748,0.00001991355,0.00015711646,0.000067319044,0.00014710773,0.0000016846668],"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":[0.000022570259,0.000006149019,0.000016010765,0.00004388375,0.000045603512,0.00000580119,0.0010045401,0.97130316,0.0035844597,0.0013151518,0.00010854202,0.022544118],"study_design_scores_gemma":[0.00040279643,0.00008886413,0.000003188768,0.00004358114,0.000013991556,0.000028371021,0.00012292236,0.97216696,0.006392174,0.00026683896,0.020217352,0.00025296467],"about_ca_topic_score_codex":0.00002066529,"about_ca_topic_score_gemma":0.000011673222,"teacher_disagreement_score":0.8808587,"about_ca_system_score_codex":0.000025292871,"about_ca_system_score_gemma":0.0000075958683,"threshold_uncertainty_score":0.88940686},"labels":[],"label_agreement":null},{"id":"W3083492053","doi":"10.1016/j.seta.2020.100802","title":"An intelligent hybrid model of neuro Wavelet, time series and Recurrent Kalman Filter for wind speed forecasting","year":2020,"lang":"en","type":"article","venue":"Sustainable Energy Technologies and Assessments","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":82,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Acadia University","funders":"","keywords":"Wind speed; Kalman filter; Computer science; Wind power; Hybrid system; Time series; Wavelet; Grid; Artificial neural network; Artificial intelligence; Machine learning; Engineering; Meteorology; Mathematics","score_opus":0.0219086209159937,"score_gpt":0.23993760100033293,"score_spread":0.21802898008433924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083492053","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":[0.88936377,0.0009257707,0.10605179,0.00041682014,0.00015042398,0.00028721074,0.00007143716,0.0010450828,0.0016877081],"genre_scores_gemma":[0.9917315,0.00031827594,0.0075516375,0.0000374288,0.000029621566,0.000012758105,0.000034769517,0.0000418302,0.00024213595],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897474,0.000008564846,0.0002505742,0.00027491953,0.00009650678,0.00039469555],"domain_scores_gemma":[0.99958605,0.00005038441,0.00006866568,0.0001590591,0.00006697514,0.00006889945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008180204,0.00021191918,0.00024349366,0.00009764935,0.00011713686,0.000063327796,0.00017342575,0.0000886568,0.0000044847316],"category_scores_gemma":[0.00007637242,0.0002079309,0.000038966573,0.00013382184,0.00008567242,0.00031526762,0.00018843253,0.00012210978,8.906196e-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":[0.00022880548,0.00009159482,0.00040081952,0.0018668454,0.00022594778,0.00007789217,0.00043611444,0.35763416,0.009998163,0.061332524,0.0021323725,0.56557477],"study_design_scores_gemma":[0.00023509431,0.00060755824,0.000004379059,0.00004371441,0.000021998414,0.000007844247,0.0021350035,0.943717,0.04186352,0.0064367023,0.0046896883,0.00023747054],"about_ca_topic_score_codex":0.000011695865,"about_ca_topic_score_gemma":0.0000017384435,"teacher_disagreement_score":0.5860829,"about_ca_system_score_codex":0.000024493813,"about_ca_system_score_gemma":0.000021717748,"threshold_uncertainty_score":0.8479176},"labels":[],"label_agreement":null},{"id":"W3084340700","doi":"10.3390/en13184708","title":"Machine Learning for Energy Systems","year":2020,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":16,"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":"Russian Foundation for Basic Research; National Natural Science Foundation of China","keywords":"Electric power system; Computer science; Electrification; Wind power; Renewable energy; Systems engineering; Industrial engineering; Engineering; Power (physics); Electrical engineering","score_opus":0.014571597080919077,"score_gpt":0.18943626857217621,"score_spread":0.17486467149125715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084340700","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":[0.25347131,0.09890851,0.40338784,0.0011734581,0.009410446,0.00043221284,0.00012272022,0.013102077,0.21999142],"genre_scores_gemma":[0.99778605,0.0001871897,0.00050604896,0.000072641276,0.0005208817,0.00002974419,0.00003826079,0.00004491874,0.00081425725],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994975,0.000010864289,0.000130377,0.00010810781,0.00006649555,0.00018671097],"domain_scores_gemma":[0.9997773,0.0000638819,0.000017532075,0.00005924457,0.000014522771,0.000067481975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000035080953,0.000114478826,0.00013708466,0.000027373737,0.00006432346,0.000041188006,0.00008977782,0.000044917924,0.000014802962],"category_scores_gemma":[0.00002992739,0.000109989785,0.00005070402,0.000088564266,0.00000949388,0.0000685387,0.000018585743,0.000071078095,0.000005529758],"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":[0.000004292773,0.000001261302,0.00008391656,0.00006341557,0.000024454828,0.000002137954,0.00020019693,0.9836211,0.0022400094,0.009136788,0.001575887,0.0030465373],"study_design_scores_gemma":[0.0001117697,0.000026820286,0.000003600558,0.000013676134,0.000005108063,0.0000016914448,0.000049354574,0.5839638,0.004362388,0.00002312973,0.4113331,0.00010553607],"about_ca_topic_score_codex":0.00004703874,"about_ca_topic_score_gemma":0.000006470054,"teacher_disagreement_score":0.74431473,"about_ca_system_score_codex":0.000010809809,"about_ca_system_score_gemma":0.0000046074915,"threshold_uncertainty_score":0.44852534},"labels":[],"label_agreement":null},{"id":"W3087959594","doi":"10.1109/compsac48688.2020.00-77","title":"Data-Driven Adaptive Regularized Risk Forecasting","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"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 Manitoba; Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Risk analysis (engineering); Business","score_opus":0.07035496042861558,"score_gpt":0.223653774666605,"score_spread":0.1532988142379894,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3087959594","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":[0.18442482,0.00054637186,0.52372,0.00037013058,0.0008705747,0.0003147547,0.00050651014,0.0038067359,0.28544006],"genre_scores_gemma":[0.93681866,0.000028526461,0.062420666,0.00012667423,0.00035846053,0.0000035522978,0.00008807081,0.000044307635,0.000111105706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992195,0.000019947594,0.00018718361,0.00022738914,0.00011094214,0.00023504275],"domain_scores_gemma":[0.9994596,0.00007691852,0.000031615225,0.00028094623,0.000017389195,0.00013352207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008767146,0.00014002134,0.00016033679,0.000023572937,0.000066903114,0.00003210594,0.00031755128,0.00005472578,0.00016811772],"category_scores_gemma":[0.00010284772,0.00012926005,0.000036843296,0.00017351178,0.000017169014,0.00022751688,0.000150424,0.00020395426,0.00006689673],"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":[0.00006733435,0.000018300534,0.0017241677,0.00010096112,0.00040648397,0.00008568831,0.0019153206,0.83034164,0.004102488,0.0039227065,0.03300815,0.12430679],"study_design_scores_gemma":[0.00025225323,0.00002858579,0.00004357178,0.000017559623,0.000022125,0.0000043568416,0.00008263731,0.97863877,0.00073806307,0.00007417921,0.019927932,0.00016998635],"about_ca_topic_score_codex":0.000023079816,"about_ca_topic_score_gemma":0.00003886388,"teacher_disagreement_score":0.75239384,"about_ca_system_score_codex":0.000012861148,"about_ca_system_score_gemma":0.0000092494165,"threshold_uncertainty_score":0.5271072},"labels":[],"label_agreement":null},{"id":"W3088108101","doi":"","title":"Wavelets and artificial neural networks in power system transient classification and short-term power load prediction","year":2002,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"Artificial neural network; Term (time); Transient (computer programming); Artificial intelligence; Wavelet; Electric power system; Computer science; Power (physics); Pattern recognition (psychology); Machine learning; Physics","score_opus":0.013439851832436393,"score_gpt":0.17793218832769814,"score_spread":0.16449233649526174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088108101","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":[0.9926226,0.0005459778,0.000648977,0.000021445005,0.00073515833,0.00017839193,0.000020854002,0.00011716323,0.00510939],"genre_scores_gemma":[0.9994616,0.00020292679,0.000044320655,0.0000013128839,0.00004207054,6.802614e-7,0.00012845326,0.00002790914,0.00009075668],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.999146,0.000030555944,0.00015890507,0.0002628128,0.0001883528,0.00021337919],"domain_scores_gemma":[0.99965775,0.000022150201,0.00006738411,0.00012964527,0.000051889398,0.00007120103],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009880909,0.00021050792,0.00026792768,0.00019441354,0.00009948991,0.000027347445,0.00008552627,0.000324202,0.000004880694],"category_scores_gemma":[0.000002873773,0.0002835002,0.000052508374,0.00014362132,0.000037803435,0.00017803116,0.000012297616,0.00029660956,0.0000012745577],"study_design_candidate":"observational","study_design_consensus":"observational","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0027662693,0.0009116391,0.35050014,0.013194073,0.00179014,0.0016082705,0.076473035,0.2983718,0.03962512,0.006686127,0.0077960957,0.20027733],"study_design_scores_gemma":[0.00029314743,0.0000740037,0.54246134,0.00047587763,0.000080709644,0.000014886391,0.014923792,0.44107524,0.000044979774,0.0000021942433,0.00026043627,0.00029337683],"about_ca_topic_score_codex":0.00011851048,"about_ca_topic_score_gemma":0.04137095,"teacher_disagreement_score":0.19998395,"about_ca_system_score_codex":0.00016372313,"about_ca_system_score_gemma":0.000008949849,"threshold_uncertainty_score":0.99996173},"labels":[],"label_agreement":null},{"id":"W3088290669","doi":"10.1109/sest48500.2020.9203539","title":"Short-term Load Forecasting based on Wavelet Approach","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Term (time); Mean squared error; Computer science; Noise (video); Wavelet; Algorithm; Data mining; Artificial intelligence; Statistics; Mathematics","score_opus":0.04178059293453915,"score_gpt":0.207573669057782,"score_spread":0.16579307612324284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088290669","genre_codex":"other","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":[0.10655885,0.000053017426,0.13192147,0.00013554131,0.0002674567,0.0001316817,0.000008713129,0.0012840515,0.7596392],"genre_scores_gemma":[0.9892876,0.0000017197159,0.009813318,0.0004511663,0.0002715416,0.000009857885,0.000022878436,0.000047701367,0.00009418791],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912596,0.000009614692,0.0001776365,0.00020304302,0.00019499002,0.00028876294],"domain_scores_gemma":[0.9996301,0.00005082552,0.000010460547,0.00013906261,0.00001822161,0.000151343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007634205,0.00018042613,0.00015490846,0.000032547145,0.000050178336,0.000039102782,0.00013408411,0.000068118505,0.00010975366],"category_scores_gemma":[0.000033363325,0.00016255154,0.00006974276,0.00017349949,0.000012355332,0.00006775982,0.000020931691,0.00019103983,0.00003501022],"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":[0.000025984664,0.000038105238,0.0018265395,0.0002656136,0.000040868137,0.000042929583,0.00061616587,0.9074042,0.0022360238,0.0008819082,0.004366945,0.082254715],"study_design_scores_gemma":[0.0001765966,0.000044413675,0.00015402398,0.000027425123,0.000006382958,0.0000036273948,0.000024727877,0.9926219,0.003780378,0.0000053063973,0.0029409132,0.00021430093],"about_ca_topic_score_codex":0.000002576347,"about_ca_topic_score_gemma":0.0000017463329,"teacher_disagreement_score":0.88272876,"about_ca_system_score_codex":0.000039538867,"about_ca_system_score_gemma":0.000012781918,"threshold_uncertainty_score":0.66286594},"labels":[],"label_agreement":null},{"id":"W3088452515","doi":"10.1016/j.apenergy.2021.116918","title":"N-BEATS neural network for mid-term electricity load forecasting","year":2021,"lang":"en","type":"preprint","venue":"Applied Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"HEC Montréal","funders":"","keywords":"Computer science; Artificial neural network; Electricity; Term (time); Electricity market; Electric power system; Demand forecasting; Time series; Probabilistic forecasting; Econometrics; Artificial intelligence; Machine learning; Power (physics); Operations research; Engineering; Probabilistic logic; Economics","score_opus":0.02065949125530398,"score_gpt":0.21221031329469495,"score_spread":0.19155082203939097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088452515","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":[0.6205165,0.011796263,0.23644991,0.000048406146,0.011433895,0.0008408377,0.00011068697,0.0031594364,0.11564407],"genre_scores_gemma":[0.98709154,0.00023323148,0.007816833,0.00015811721,0.0029633488,0.0005086542,0.0007825831,0.00023435535,0.00021134359],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9972432,0.000023528177,0.00060606474,0.00073572533,0.00030453948,0.0010868949],"domain_scores_gemma":[0.99871206,0.0002437215,0.00016196311,0.000590494,0.000107439904,0.00018432045],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00020932364,0.00068992213,0.00075340056,0.000097239164,0.00019282209,0.00020841933,0.0004671257,0.0006176812,0.00003580213],"category_scores_gemma":[0.000027662774,0.00078365335,0.00035271354,0.0003203896,0.00003201131,0.0000599145,0.00037058795,0.0006724882,0.0000020619304],"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":[0.000024177203,0.000019510599,0.000034084012,0.00030660577,0.000208967,0.000021048945,0.0001224298,0.9262549,0.0025852413,0.0019208525,0.0018927214,0.06660945],"study_design_scores_gemma":[0.0010714775,0.000063970954,0.000092769056,0.00049088604,0.00025838954,0.000047411977,0.000034855304,0.92187804,0.042294003,0.004684669,0.02685583,0.0022276703],"about_ca_topic_score_codex":0.0001358152,"about_ca_topic_score_gemma":0.00036758656,"teacher_disagreement_score":0.36657503,"about_ca_system_score_codex":0.0002694353,"about_ca_system_score_gemma":0.00012347926,"threshold_uncertainty_score":0.9994614},"labels":[],"label_agreement":null},{"id":"W3088641311","doi":"10.1109/compsac48688.2020.00-76","title":"Modeling of Short-Term Electricity Demand and Comparison of Machine Learning Approaches for Load Forecasting","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Manitoba","funders":"","keywords":"Term (time); Computer science; Electricity; Demand forecasting; Electricity demand; Machine learning; Industrial engineering; Artificial intelligence; Electricity generation; Operations research; Engineering; Power (physics); Electrical engineering","score_opus":0.0835315421767799,"score_gpt":0.24579223118308402,"score_spread":0.16226068900630414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3088641311","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":[0.5681915,0.0010291502,0.4289091,0.000009072358,0.000016148117,0.000078574114,0.000003407729,0.00007331764,0.0016896806],"genre_scores_gemma":[0.99156564,0.000022957664,0.008316623,0.000004857097,0.000042305866,0.0000050850113,0.000011292945,0.000024557474,0.000006702587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921435,0.0000115488765,0.0003507136,0.00013916128,0.000102765975,0.0001814338],"domain_scores_gemma":[0.9996979,0.00010158119,0.00003909464,0.000050102844,0.00003527746,0.000075994365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015981063,0.0001275362,0.00031920327,0.000039277325,0.000049559025,0.000010035299,0.00006906245,0.000057775123,0.0000045822],"category_scores_gemma":[0.00010543863,0.000120554985,0.000054622073,0.000120535246,0.000016271633,0.00006950022,0.000031182808,0.00014249436,7.069029e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023771121,0.000009240189,0.015219601,0.0004951594,0.000039456634,2.1205811e-7,0.0012040968,0.96478057,0.0043309797,0.00019305605,0.000004646828,0.013699206],"study_design_scores_gemma":[0.00021985166,0.00010575472,0.000025417667,0.00003622101,0.000026825333,0.0000020078912,0.00012863029,0.9804256,0.018856598,0.000030509593,0.000025249601,0.00011735702],"about_ca_topic_score_codex":0.000022237113,"about_ca_topic_score_gemma":0.000032498036,"teacher_disagreement_score":0.4233741,"about_ca_system_score_codex":0.000012605282,"about_ca_system_score_gemma":0.000010466037,"threshold_uncertainty_score":0.49160895},"labels":[],"label_agreement":null},{"id":"W3097280320","doi":"10.1016/j.oceaneng.2020.108254","title":"Intelligent optimized deep learning hybrid models of neuro wavelet, Fourier Series and Recurrent Kalman Filter for tidal currents constitutions forecasting","year":2020,"lang":"en","type":"article","venue":"Ocean Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Acadia University","funders":"","keywords":"Kalman filter; Computer science; Artificial neural network; Wavelet; Series (stratigraphy); Artificial intelligence; Hybrid system; Particle swarm optimization; Algorithm; Harmonic; Filter (signal processing); Fourier series; Machine learning; Mathematics; Geology","score_opus":0.028620745275302784,"score_gpt":0.20687057497208697,"score_spread":0.17824982969678418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3097280320","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":[0.14271535,0.0011518887,0.8541091,0.00005495138,0.0006668721,0.00027540256,0.000050780785,0.00042955225,0.0005461039],"genre_scores_gemma":[0.96602505,0.00017443432,0.033439953,0.000015892276,0.0001904335,0.00001511673,0.000050373914,0.00007380075,0.00001495261],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883777,0.000011557608,0.0003989978,0.00024708186,0.00012756417,0.00037701678],"domain_scores_gemma":[0.9994448,0.00015110942,0.0000593344,0.000107226566,0.000054466134,0.00018310745],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000104133025,0.00027741413,0.00032757552,0.000099975005,0.00009535268,0.000041049185,0.000121386016,0.000051522096,0.000010961663],"category_scores_gemma":[0.0002249607,0.0003043592,0.00010682746,0.00013954722,0.000041710882,0.0002883144,0.000074078875,0.00029050803,8.545993e-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":[0.000023283079,0.0000069947096,0.000050064686,0.0003892683,0.000057461122,0.0000062109852,0.0005657951,0.9857974,0.00038337728,0.0010193153,0.000113848066,0.011586979],"study_design_scores_gemma":[0.00039574673,0.000104666826,0.0000043307723,0.00018938581,0.000037918584,0.000032780918,0.000055275304,0.986927,0.0055901124,0.000043805103,0.006331571,0.00028740984],"about_ca_topic_score_codex":0.0000012451153,"about_ca_topic_score_gemma":5.2365186e-7,"teacher_disagreement_score":0.82330966,"about_ca_system_score_codex":0.000027581422,"about_ca_system_score_gemma":0.00001136934,"threshold_uncertainty_score":0.9999409},"labels":[],"label_agreement":null},{"id":"W3101652029","doi":"10.1109/dasc-picom-cbdcom-cyberscitech49142.2020.00113","title":"Internet of Energy: Ensemble Learning through Multilevel Stacking for Load Forecasting","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Lakehead University","funders":"","keywords":"Multivariate adaptive regression splines; Gradient boosting; Computer science; Artificial intelligence; Machine learning; Mean squared error; Support vector machine; Random forest; Principal component analysis; Ensemble learning; Boosting (machine learning); Mean absolute percentage error; Data mining; Electricity; Stacking; Regression analysis; Statistics; Artificial neural network; Engineering; Mathematics; Bayesian multivariate linear regression","score_opus":0.06435875792517112,"score_gpt":0.2354721289939792,"score_spread":0.17111337106880808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101652029","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":[0.06464608,0.00028177616,0.88595873,0.00003668869,0.00022956937,0.00007021842,0.0000051830584,0.00040978866,0.04836196],"genre_scores_gemma":[0.9685013,0.000015330172,0.030559845,0.00011093107,0.00017779402,0.000012890509,0.00001538582,0.000059849313,0.0005466527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990263,0.000011990314,0.00033016855,0.00018854313,0.00013669109,0.00030630906],"domain_scores_gemma":[0.9995167,0.00019335425,0.00006476671,0.0000788624,0.000075654054,0.000070614544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000935576,0.00017630903,0.00024563007,0.000025738707,0.000043248292,0.000024777952,0.00013785073,0.000075250755,0.00006438953],"category_scores_gemma":[0.0001736712,0.00017456734,0.00010455721,0.000116642,0.00001728172,0.0001775486,0.00005505098,0.00013677291,0.0000032916316],"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":[0.0000952568,0.000029190312,0.0014998255,0.00096265593,0.0002744091,0.000012747329,0.013652644,0.66989213,0.05210327,0.015108195,0.005190521,0.24117915],"study_design_scores_gemma":[0.00037837186,0.00008762539,0.0000066854677,0.00009914253,0.000012619043,0.000003114913,0.00020231842,0.8573909,0.11470457,0.00015313088,0.0267689,0.00019261519],"about_ca_topic_score_codex":0.00010000558,"about_ca_topic_score_gemma":0.000028887598,"teacher_disagreement_score":0.90385526,"about_ca_system_score_codex":0.00003495735,"about_ca_system_score_gemma":0.000018498291,"threshold_uncertainty_score":0.71186495},"labels":[],"label_agreement":null},{"id":"W3101786559","doi":"10.1109/pedg48541.2020.9244395","title":"Long Short-term Memory Forecasting In Home Energy Management System","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of New Brunswick","funders":"","keywords":"Microgrid; Smart grid; Computer science; Power demand; Energy management; Long short term memory; Energy management system; Term (time); Focus (optics); Electric power system; Energy consumption; Demand forecasting; Work (physics); Home automation; Scale (ratio); Power consumption; Energy (signal processing); Power (physics); Artificial intelligence; Operations research; Engineering; Recurrent neural network; Artificial neural network; Telecommunications; Control (management)","score_opus":0.020219016792480983,"score_gpt":0.19124840638199064,"score_spread":0.17102938958950967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3101786559","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":[0.5539382,0.0006317904,0.06864763,0.000049456452,0.00080471626,0.00014021088,0.0000033962676,0.0015354183,0.3742492],"genre_scores_gemma":[0.9982014,0.000023325014,0.0011921816,0.00007099597,0.00019304336,0.000020481213,0.000010139244,0.000046217825,0.00024226996],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990185,0.000013236138,0.00029235525,0.00020693403,0.0001357316,0.00033322474],"domain_scores_gemma":[0.99970657,0.000023907141,0.000013963708,0.00012907536,0.000008291714,0.00011818999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007731211,0.00017660481,0.00019567508,0.00009513049,0.00003267532,0.000037754617,0.00016093819,0.00005667757,0.0000454271],"category_scores_gemma":[0.0000025918184,0.00017623855,0.00005296357,0.00026400975,0.000008556666,0.00011939816,0.000075354874,0.00010507997,0.000014917632],"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":[0.000027729437,0.000030067858,0.023533044,0.003079184,0.0002389582,0.0024370565,0.0016137778,0.6753311,0.0014589375,0.018227858,0.0012754028,0.2727469],"study_design_scores_gemma":[0.00045301218,0.000032669486,0.0024638202,0.00044044235,0.000022481629,0.00004118116,0.0005879351,0.98991215,0.004183976,0.000013408439,0.0013163236,0.00053257897],"about_ca_topic_score_codex":0.000020521407,"about_ca_topic_score_gemma":0.00008088673,"teacher_disagreement_score":0.44426316,"about_ca_system_score_codex":0.00007583053,"about_ca_system_score_gemma":0.000003127819,"threshold_uncertainty_score":0.71867996},"labels":[],"label_agreement":null},{"id":"W3106812737","doi":"10.1109/ccece47787.2020.9255747","title":"Diagnosing Fuel Pumps, Power Transducers, CTs, and PTs via Fuel-Power Function and 2oo3 Voting","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Quadratic function; Computer science; Control theory (sociology); Transformer; Electric power system; Automotive engineering; Power (physics); Quadratic equation; Engineering; Voltage; Electrical engineering; Mathematics; Control (management)","score_opus":0.010383091901141936,"score_gpt":0.18525113922281222,"score_spread":0.17486804732167027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106812737","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":[0.903417,0.008152726,0.05088371,0.00082519255,0.0011187808,0.00020608283,0.000009586971,0.0010752652,0.03431164],"genre_scores_gemma":[0.9983037,0.00019876321,0.0007625231,0.0004706333,0.00014941927,0.000005107661,0.000008204823,0.000055502416,0.00004613356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989997,0.000017905764,0.00024052772,0.00029116584,0.00012883985,0.0003218451],"domain_scores_gemma":[0.9995624,0.00008832151,0.000026532614,0.000086773405,0.000020441572,0.00021553213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010161306,0.00023545219,0.0002137257,0.000055924032,0.00008752713,0.00010262886,0.000057878686,0.00011238207,0.00022428264],"category_scores_gemma":[0.00002918091,0.00022915407,0.00004397535,0.00016456548,0.00003442629,0.0003243869,0.00003197811,0.00021954115,0.000012919575],"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":[0.000553907,0.00033050042,0.15494971,0.0069694133,0.0019031768,0.00039678856,0.07069751,0.06665635,0.3048397,0.028292513,0.014988933,0.3494215],"study_design_scores_gemma":[0.010262999,0.0021592411,0.13000162,0.0014198351,0.0006814126,0.00037943036,0.0050563407,0.55343986,0.02802685,0.0024459276,0.25914595,0.0069805235],"about_ca_topic_score_codex":0.000025237887,"about_ca_topic_score_gemma":0.00002991431,"teacher_disagreement_score":0.4867835,"about_ca_system_score_codex":0.000018035533,"about_ca_system_score_gemma":0.000005970109,"threshold_uncertainty_score":0.93446314},"labels":[],"label_agreement":null},{"id":"W3106963434","doi":"10.1109/ccece47787.2020.9255778","title":"ANN Daily Peak Forecast for Peak Demand Charges Management","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Electricity; Demand forecasting; Peak demand; Computer science; Load management; Load profile; Battery (electricity); Class (philosophy); Electricity market; Peak load; Operations research; Environmental economics; Engineering; Automotive engineering; Economics; Electrical engineering; Power (physics); Artificial intelligence","score_opus":0.019499468585711655,"score_gpt":0.20318414454996747,"score_spread":0.1836846759642558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3106963434","genre_codex":"other","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":[0.07562897,0.0015050109,0.27318564,0.0018812212,0.0013004681,0.00084187096,0.00007230329,0.0022418988,0.6433426],"genre_scores_gemma":[0.9891525,0.000089995316,0.0076076263,0.00047161046,0.0004031655,0.000053518885,0.00004259551,0.000055836525,0.0021231335],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992881,0.000004336115,0.00016486374,0.00017309496,0.0000885171,0.00028108523],"domain_scores_gemma":[0.9997154,0.000026293137,0.000015260432,0.00011011058,0.00001593306,0.000116992625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006373966,0.00014983321,0.00013939496,0.0000360454,0.00006203945,0.000040863502,0.00013739483,0.00004116704,0.0001568733],"category_scores_gemma":[0.000008501316,0.00013802238,0.00007371872,0.000104869134,0.000009582942,0.00010416526,0.000039516322,0.00006126013,0.000060108297],"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":[0.00016238586,0.00010192775,0.0015776658,0.004419452,0.0011954786,0.00010164439,0.00633267,0.22330846,0.00959611,0.08835083,0.50268453,0.16216883],"study_design_scores_gemma":[0.0009954958,0.00012362075,0.0006970585,0.000063255575,0.00005594394,0.0000054549055,0.00039416412,0.3981129,0.006145816,0.00032053437,0.59257984,0.00050593965],"about_ca_topic_score_codex":0.0000023325977,"about_ca_topic_score_gemma":0.000008979274,"teacher_disagreement_score":0.91352355,"about_ca_system_score_codex":0.00001291988,"about_ca_system_score_gemma":0.000002391978,"threshold_uncertainty_score":0.562839},"labels":[],"label_agreement":null},{"id":"W3107840026","doi":"10.1109/icee50131.2020.9260859","title":"Correlation based Convolutional Recurrent Network for Load Forecasting","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"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":"Computer science; Autocorrelation; Convolution (computer science); Recurrent neural network; Artificial neural network; Series (stratigraphy); Electrical load; Term (time); Time series; Convolutional neural network; Artificial intelligence; Deep learning; Correlation; Algorithm; Pattern recognition (psychology); Machine learning; Power (physics); Statistics; Mathematics","score_opus":0.03615869731927335,"score_gpt":0.21244007149035182,"score_spread":0.17628137417107848,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3107840026","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":[0.0050224075,0.0003618528,0.9745142,0.00023149498,0.0010273209,0.00016499148,0.000018574041,0.00049466465,0.018164502],"genre_scores_gemma":[0.96906644,0.000002877751,0.029496284,0.0002877591,0.00090392685,0.000027946526,0.00010780406,0.000028521605,0.00007846515],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999366,0.000006816002,0.00017819855,0.0001195805,0.00010622428,0.0002232183],"domain_scores_gemma":[0.99963814,0.00015299165,0.000024097879,0.00004745474,0.0000502981,0.00008702573],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009629583,0.00010364432,0.00009943835,0.000012171949,0.000075447104,0.000018214248,0.000055133223,0.000051480576,0.00012243426],"category_scores_gemma":[0.00007914122,0.00010480042,0.000062229585,0.00012559268,0.000009435654,0.00006919641,0.000008906921,0.00008711671,0.000016060938],"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":[0.000019363602,0.000002905674,0.00052801333,0.00004464052,0.000011358819,4.84989e-7,0.00005119429,0.971084,0.00007264098,0.002586993,0.017326403,0.008271981],"study_design_scores_gemma":[0.0003357259,0.000048090315,0.0000907141,0.000036748912,0.0000087616545,9.700748e-7,0.000006704829,0.95161927,0.00013824705,0.0001607657,0.04742728,0.00012672076],"about_ca_topic_score_codex":0.0000024812641,"about_ca_topic_score_gemma":0.000010286606,"teacher_disagreement_score":0.96404403,"about_ca_system_score_codex":0.00004945388,"about_ca_system_score_gemma":0.000030042102,"threshold_uncertainty_score":0.42736372},"labels":[],"label_agreement":null},{"id":"W3109044333","doi":"10.1109/ccece47787.2020.9255716","title":"A Deep Learning Approach to Predict Weather Data Using Cascaded LSTM Network","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":24,"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 Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Deep learning; Dew point; Artificial intelligence; Machine learning; Weather prediction; Numerical weather prediction; Artificial neural network; Wind speed; Weather forecasting; Convolution (computer science); Meteorology","score_opus":0.04970691521965234,"score_gpt":0.22791901285813504,"score_spread":0.1782120976384827,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109044333","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":[0.03533708,0.0006081162,0.83632725,0.000059400445,0.00027132223,0.0001415119,0.000004349984,0.0010626655,0.12618832],"genre_scores_gemma":[0.92944247,0.000012222753,0.06877258,0.00032533257,0.0011016196,0.000004298832,0.00005853457,0.00007633094,0.00020658925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990511,0.000023411138,0.00017359499,0.00027413614,0.00012578582,0.00035194837],"domain_scores_gemma":[0.9994914,0.000028108869,0.000015663292,0.00026048758,0.000010167229,0.00019417386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012513802,0.00015506003,0.00016483302,0.00002423874,0.00008235221,0.000052821437,0.0003369361,0.00006911878,0.000078902],"category_scores_gemma":[0.000042996486,0.00014625902,0.000028658525,0.00032573089,0.000008365726,0.00015421618,0.00021538726,0.0002443459,0.00003309443],"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":[0.000003997995,0.000003674552,0.0006829369,0.000026916725,0.000037524256,0.0000032657745,0.00064493937,0.9927339,0.00044054445,0.00014573222,0.0019889844,0.0032875745],"study_design_scores_gemma":[0.00010982991,0.000015994108,0.0000417918,0.000022839777,0.000017096343,0.000008796478,0.00010072647,0.9611001,0.0001139659,0.000004769797,0.03828426,0.00017981825],"about_ca_topic_score_codex":0.000028105798,"about_ca_topic_score_gemma":0.000007847764,"teacher_disagreement_score":0.89410543,"about_ca_system_score_codex":0.000018670276,"about_ca_system_score_gemma":0.0000075050466,"threshold_uncertainty_score":0.5964269},"labels":[],"label_agreement":null},{"id":"W3109612445","doi":"10.1109/ccece47787.2020.9255789","title":"Data Driven Approach for Reduced Value at Risk Forecasts in Renewable Power Supply Systems","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Manitoba","funders":"","keywords":"Electricity; Exponential smoothing; Electricity market; Electricity price forecasting; Renewable energy; Electricity generation; EWMA chart; Wind power; Mains electricity; Environmental economics; Electricity retailing; Production (economics); Econometrics; Economics; Microeconomics; Computer science; Power (physics); Engineering; Process (computing)","score_opus":0.039463725205768116,"score_gpt":0.2281737345608469,"score_spread":0.1887100093550788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109612445","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":[0.6070674,0.0019814074,0.21241325,0.0002443168,0.0018720775,0.0018928843,0.0018804006,0.0015725547,0.1710757],"genre_scores_gemma":[0.9816346,0.000038724807,0.01663571,0.000040552986,0.00020269683,0.000057531135,0.0006617675,0.00007029434,0.0006581215],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987217,0.000030281959,0.0003276187,0.00039490327,0.0001321214,0.00039334912],"domain_scores_gemma":[0.9992407,0.00008717424,0.000043120108,0.00046520386,0.000018703146,0.0001451246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002186653,0.00019705667,0.0002746925,0.000056788846,0.00006211097,0.0000511371,0.00044416296,0.00011147442,0.00003521817],"category_scores_gemma":[0.00008393056,0.00018402624,0.00004867053,0.00021991527,0.0000132816585,0.00022026122,0.00018687354,0.00013361692,0.000012197052],"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":[0.000025637439,0.000012929606,0.0011205716,0.00015370933,0.00004403917,0.0000030353183,0.00041239688,0.9697027,0.0018499434,0.00029291274,0.026070962,0.00031116544],"study_design_scores_gemma":[0.0005681205,0.000044238655,0.00007673071,0.000036248963,0.00001597371,0.000006208022,0.00014906806,0.9733179,0.0014209191,0.000014228257,0.024112433,0.00023792553],"about_ca_topic_score_codex":0.00032470192,"about_ca_topic_score_gemma":0.00012093357,"teacher_disagreement_score":0.3745672,"about_ca_system_score_codex":0.0000698659,"about_ca_system_score_gemma":0.00001745638,"threshold_uncertainty_score":0.75043726},"labels":[],"label_agreement":null},{"id":"W3110276151","doi":"10.1109/ccece47787.2020.9255819","title":"A Novel Approach for Seasonality and Trend Detection using Fast Fourier Transform in Box-Jenkins Algorithm","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Seasonality; Fast Fourier transform; Autocorrelation; Kalman filter; Computer science; Time series; Box–Jenkins; Series (stratigraphy); Algorithm; Statistics; Mathematics; Artificial intelligence; Autoregressive integrated moving average; Machine learning","score_opus":0.03211767440670514,"score_gpt":0.22311539531999955,"score_spread":0.19099772091329442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110276151","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":[0.08267667,0.00006480919,0.91415495,0.0000343844,0.000054340737,0.0001191078,0.00003363727,0.00011284711,0.0027492375],"genre_scores_gemma":[0.83414525,0.000005647262,0.16560422,0.000045425757,0.00012732347,0.000014934194,0.000014346654,0.000023983584,0.0000188436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943405,0.000005557266,0.00014324536,0.00015674814,0.00006841901,0.00019197779],"domain_scores_gemma":[0.9998331,0.000024817557,0.000010506794,0.0000433796,0.000007495894,0.00008066446],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000966038,0.00011751199,0.00013353693,0.000029691164,0.000050672457,0.000027126738,0.00003634912,0.000067689696,0.0000063577213],"category_scores_gemma":[0.000007753699,0.00011503728,0.000042556152,0.00016465622,0.000012577649,0.00012969114,0.000007627465,0.00011834924,1.3950542e-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":[0.00004214616,0.00005512404,0.0005540843,0.00038968743,0.000069769565,0.000002201797,0.0032277422,0.110241525,0.022500766,0.00024255516,0.000015810867,0.86265856],"study_design_scores_gemma":[0.0006125893,0.000023309283,0.00019988461,0.000010214302,0.00001161672,0.00001042678,0.0001708802,0.9919596,0.0056659975,0.000023349236,0.0011665798,0.00014556035],"about_ca_topic_score_codex":0.000060787595,"about_ca_topic_score_gemma":0.00019367528,"teacher_disagreement_score":0.88171804,"about_ca_system_score_codex":0.000031123713,"about_ca_system_score_gemma":0.0000061445758,"threshold_uncertainty_score":0.4691084},"labels":[],"label_agreement":null},{"id":"W3110953966","doi":"10.3390/en13246489","title":"Particle Filter-Based Electricity Load Prediction for Grid-Connected Microgrid Day-Ahead Scheduling","year":2020,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","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":"National Research Council Canada; Carleton University","funders":"Civil Aviation University of China; National Natural Science Foundation of China","keywords":"Microgrid; Scheduling (production processes); Electricity; Support vector machine; Computer science; Nonlinear system; Artificial neural network; Particle filter; Grid; Mathematical optimization; Real-time computing; Engineering; Filter (signal processing); Artificial intelligence; Mathematics","score_opus":0.01915145838075537,"score_gpt":0.20848151345148874,"score_spread":0.18933005507073336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110953966","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":[0.9667492,0.0015190261,0.029066619,0.00027067453,0.0006565792,0.00012271613,0.000068875444,0.0011997436,0.000346569],"genre_scores_gemma":[0.99449164,0.00003388218,0.0043100812,0.00020876824,0.0007592832,0.00005448082,0.00007502605,0.000045245524,0.000021590882],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990359,0.00002166641,0.00023804333,0.00021516107,0.00013085165,0.00035838797],"domain_scores_gemma":[0.99952215,0.00015123046,0.000032965894,0.00012577539,0.000059110214,0.00010879112],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000112299684,0.0001753863,0.00017605275,0.00003214731,0.00011082086,0.000057100475,0.00012065209,0.00008091786,0.000030392335],"category_scores_gemma":[0.00016572246,0.00018182994,0.00009048037,0.000279239,0.000020662248,0.00014380126,0.000016284277,0.00012905871,0.000010834273],"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":[0.000032882526,0.000014113068,0.00061221165,0.00007610425,0.00003693971,0.000002414226,0.00029028248,0.84319943,0.1495569,0.0001356028,0.0016096772,0.0044334116],"study_design_scores_gemma":[0.00044265293,0.00008003712,0.00015483367,0.000025951125,0.000017845729,8.8301897e-7,0.000026059928,0.55003875,0.43481705,0.000019831932,0.014231353,0.00014474403],"about_ca_topic_score_codex":0.00001958366,"about_ca_topic_score_gemma":0.00001654004,"teacher_disagreement_score":0.2931607,"about_ca_system_score_codex":0.00005743984,"about_ca_system_score_gemma":0.00003747132,"threshold_uncertainty_score":0.74148095},"labels":[],"label_agreement":null},{"id":"W3112065382","doi":"10.1109/smc42975.2020.9283250","title":"A Hybrid Deep Learning-Based Power System State Forecasting","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Guelph","funders":"","keywords":"Benchmark (surveying); Computer science; Recurrent neural network; Electric power system; Deep learning; Smart grid; Mean squared error; Convolutional neural network; Artificial intelligence; State (computer science); Mean absolute percentage error; Artificial neural network; Voltage; Power (physics); Machine learning; Algorithm; Engineering; Statistics; Mathematics","score_opus":0.013363807162941067,"score_gpt":0.1785160363818032,"score_spread":0.16515222921886213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112065382","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":[0.40604478,0.0003667711,0.4307985,0.000107970016,0.00057529163,0.00015691317,0.000008260278,0.004429492,0.15751202],"genre_scores_gemma":[0.9979548,0.0000016093494,0.0016030564,0.00014894502,0.00009024038,0.000007917156,0.000011050547,0.000064195105,0.00011818102],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99906945,0.000022266278,0.00023810443,0.0001821853,0.00014306173,0.0003449576],"domain_scores_gemma":[0.99959177,0.000070040376,0.000032070304,0.000094621435,0.000029335075,0.00018214273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009136925,0.00018228017,0.00018322037,0.000044325116,0.00007913899,0.00005584003,0.00011733605,0.0000311756,0.00014614609],"category_scores_gemma":[0.000048569316,0.00017356483,0.00007469157,0.0001438851,0.000012108046,0.000090313784,0.000024890394,0.0002320583,0.00011760212],"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":[0.000008550183,0.0000029405428,0.00041086928,0.00017696201,0.00002556385,0.000076302065,0.00032128353,0.9930678,0.00046868811,0.00015128376,0.00030477784,0.0049849954],"study_design_scores_gemma":[0.00025983143,0.000063217216,0.000022787048,0.00006359805,0.0000071224576,0.000014776801,0.00010045221,0.9838574,0.006649733,0.000005236409,0.008733105,0.00022273378],"about_ca_topic_score_codex":0.0000094389625,"about_ca_topic_score_gemma":0.0000049479468,"teacher_disagreement_score":0.59191,"about_ca_system_score_codex":0.000042273197,"about_ca_system_score_gemma":0.0000104946685,"threshold_uncertainty_score":0.7077768},"labels":[],"label_agreement":null},{"id":"W3112522617","doi":"","title":"Homogenization of Canadian hourly wind speed data","year":2010,"lang":"en","type":"article","venue":"EGU General Assembly Conference Abstracts","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Homogenization (climate); Meteorology; Wind speed; Environmental science; Climatology; Geology; Geography","score_opus":0.03780289858556506,"score_gpt":0.23444021123476944,"score_spread":0.19663731264920437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3112522617","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":[0.95384747,0.000036808084,0.000084582854,0.00010034395,0.001358344,0.00006906069,0.00014057977,0.000092769515,0.04427003],"genre_scores_gemma":[0.9967242,0.00003169554,0.0019745624,0.0000328719,0.00038712178,4.5135042e-7,0.0004807763,0.00003321368,0.00033512432],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9988984,0.000010399652,0.00031828642,0.00022939625,0.0001715698,0.00037194666],"domain_scores_gemma":[0.99895906,0.00002657872,0.00006899167,0.0005702525,0.000111338195,0.0002637732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018762029,0.00017982793,0.00018121707,0.00028197403,0.0000642298,0.00008158777,0.0005356737,0.00016173664,0.00017753101],"category_scores_gemma":[0.00006576413,0.0001877999,0.000029237657,0.0003292166,0.000033505366,0.00038312015,0.000045230045,0.00029354545,0.00004687566],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000041028497,0.000026338706,0.0030024422,0.000045820252,0.0000699029,0.000033806205,0.0003131686,0.120726876,0.847543,0.0027535371,0.0061514094,0.019329622],"study_design_scores_gemma":[0.0010018547,0.00010244792,0.23770614,0.0001742814,0.000102303406,0.00007832455,0.00012237886,0.2139178,0.4181817,0.0005955278,0.12634274,0.0016745077],"about_ca_topic_score_codex":0.031593077,"about_ca_topic_score_gemma":0.19859864,"teacher_disagreement_score":0.4293613,"about_ca_system_score_codex":0.000017701086,"about_ca_system_score_gemma":0.00022295791,"threshold_uncertainty_score":0.9748556},"labels":[],"label_agreement":null},{"id":"W3115161304","doi":"10.1109/access.2020.3047077","title":"Hybrid Deep Learning-Based Model for Wind Speed Forecasting Based on DWPT and Bidirectional LSTM Network","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Computer science; Deep learning; Wind speed; Wind power; Artificial intelligence; Recurrent neural network; Feed forward; Renewable energy; Artificial neural network; Machine learning; Real-time computing; Control engineering; Engineering; Meteorology","score_opus":0.04431293417205056,"score_gpt":0.24478139035436522,"score_spread":0.20046845618231465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115161304","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":[0.3136106,0.00015531531,0.68151516,0.00017327374,0.0009536194,0.00026308076,0.00002461275,0.0005692396,0.002735108],"genre_scores_gemma":[0.99604833,0.0000036301724,0.0023299442,0.00060038286,0.00085304485,0.000010160147,0.000039752606,0.00007341219,0.000041361945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897844,0.000019419003,0.00021189373,0.00027510716,0.00015479466,0.0003603271],"domain_scores_gemma":[0.99936354,0.00029666483,0.000054693275,0.00009216446,0.000043283064,0.00014968215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013606112,0.0002137349,0.00020588114,0.00006171052,0.00019260928,0.00012500673,0.00016365529,0.00006508835,0.000021321024],"category_scores_gemma":[0.000086132946,0.00022506421,0.00007755287,0.00017084554,0.000021922342,0.00017317799,0.000018389554,0.00023823786,0.0000029461808],"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":[0.00006911252,0.000007856073,0.0011885663,0.0000966704,0.000018630628,0.0000052541077,0.000041713145,0.9932218,0.0001699382,0.000015591206,0.0010304336,0.0041343984],"study_design_scores_gemma":[0.00065879256,0.000075200966,0.000083677194,0.00007689156,0.000020685378,0.000002142104,0.0000020669422,0.9942351,0.0031234182,0.0001594126,0.001304945,0.0002576948],"about_ca_topic_score_codex":0.000006191213,"about_ca_topic_score_gemma":0.0000135397595,"teacher_disagreement_score":0.6824377,"about_ca_system_score_codex":0.000030283529,"about_ca_system_score_gemma":0.000025125715,"threshold_uncertainty_score":0.9177852},"labels":[],"label_agreement":null},{"id":"W3115438719","doi":"10.1109/pesgm41954.2020.9281415","title":"The Cumulant Tensor Framework for the Probabilistic Power Flow","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Calgary","funders":"","keywords":"Cumulant; Univariate; Mathematics; Covariance matrix; Monte Carlo method; Applied mathematics; Random variable; Covariance; Robustness (evolution); Edgeworth series; Marginal distribution; Tensor (intrinsic definition); Probabilistic logic; Entropy (arrow of time); Joint probability distribution; Mathematical optimization; Statistical physics; Statistics; Multivariate statistics; Physics; Geometry","score_opus":0.022919785429475058,"score_gpt":0.2255941827691821,"score_spread":0.20267439733970705,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3115438719","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":[0.005109842,0.00138327,0.9644166,0.00959939,0.0016550786,0.00062180066,0.000022420123,0.0008148361,0.016376762],"genre_scores_gemma":[0.97725135,0.000048001526,0.020716814,0.0010356462,0.00047965354,0.00008418152,0.000002956827,0.00004575977,0.00033564388],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948037,0.0000070673223,0.00012340014,0.000094055045,0.00007819048,0.00021692197],"domain_scores_gemma":[0.9988173,0.00092977745,0.000011088796,0.0001663703,0.000022293156,0.000053137188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000906461,0.00009798639,0.00007604477,0.0000045315182,0.00018153564,0.00006551341,0.00019838558,0.00004567237,0.00007214857],"category_scores_gemma":[0.00034924052,0.00004570491,0.00006474915,0.000085735584,0.000027845199,0.00002511097,0.000022357328,0.00014687498,0.000031746604],"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":[0.000032057964,0.000008192291,0.00008050904,0.00007055201,0.0001281841,0.0000027933922,0.0015980494,0.7770101,0.0001800823,0.17700535,0.025253968,0.018630184],"study_design_scores_gemma":[0.00007360374,0.000029637986,0.000043797878,0.000014876511,0.000012636587,0.000001697617,0.00011665163,0.79363245,0.00023241331,0.0032574462,0.20248798,0.000096828524],"about_ca_topic_score_codex":0.0000020992009,"about_ca_topic_score_gemma":0.0000069485045,"teacher_disagreement_score":0.9721415,"about_ca_system_score_codex":0.000008909979,"about_ca_system_score_gemma":0.0000066590583,"threshold_uncertainty_score":0.18637921},"labels":[],"label_agreement":null},{"id":"W3116200246","doi":"10.1109/pesgm41954.2020.9282124","title":"Forecasting Electric Load by Aggregating Meteorological and History-based Deep Learning Modules","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Mean absolute percentage error; Mean squared error; Computer science; Electric power system; Electrical load; Artificial neural network; Metric (unit); Probabilistic logic; Probabilistic forecasting; Power (physics); Simulation; Real-time computing; Artificial intelligence; Statistics; Engineering; Mathematics","score_opus":0.019303855408241217,"score_gpt":0.18332694691838936,"score_spread":0.16402309151014813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116200246","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":[0.77665806,0.019576857,0.15448779,0.00029644606,0.00023517096,0.00015485552,0.0000020029793,0.0021837559,0.046405036],"genre_scores_gemma":[0.9901794,0.000039370367,0.009219383,0.00033397265,0.000103935614,0.000008276727,0.00000991109,0.000040615094,0.00006509845],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898326,0.000029973336,0.00022288895,0.00024727805,0.00015728327,0.00035929127],"domain_scores_gemma":[0.9995618,0.00013828852,0.00004610636,0.000060878738,0.00002643678,0.00016649048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017014827,0.00020053433,0.0002106878,0.000038837716,0.00009188672,0.000030965573,0.00009506715,0.00009993659,0.00013999555],"category_scores_gemma":[0.00021219079,0.00018773854,0.00005012575,0.00016432074,0.00002326692,0.00010128084,0.000028783948,0.00035511688,0.0000069924276],"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":[0.000028281063,0.000019338087,0.00606052,0.00024764574,0.000084473155,0.000049532373,0.0011645493,0.5417608,0.097309105,0.0005039747,0.0046105934,0.34816116],"study_design_scores_gemma":[0.0002644776,0.00010658663,0.000046240675,0.000018997483,0.000012939136,0.0000069401744,0.000029638866,0.97463614,0.006087044,0.000017886146,0.018539634,0.00023345761],"about_ca_topic_score_codex":0.000026293777,"about_ca_topic_score_gemma":0.000009334265,"teacher_disagreement_score":0.43287534,"about_ca_system_score_codex":0.00013239543,"about_ca_system_score_gemma":0.000011684009,"threshold_uncertainty_score":0.7655755},"labels":[],"label_agreement":null},{"id":"W3117702476","doi":"10.15353/rea.v13i1.1822","title":"Forecasting Price Spikes in Electricity Markets","year":2021,"lang":"en","type":"article","venue":"Review of Economic Analysis","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"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":"Electricity; Electricity price forecasting; Econometrics; Support vector machine; Economics; Electricity market; Commodity; Generalization; Artificial neural network; Electricity price; Pareto principle; Generalized Pareto distribution; Quantile; Sample (material); Computer science; Artificial intelligence; Statistics; Mathematics; Extreme value theory","score_opus":0.014325137211782291,"score_gpt":0.2268219642484462,"score_spread":0.2124968270366639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117702476","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":[0.4766974,0.3947361,0.0036025245,0.00009461511,0.00019952355,0.00014193813,0.000019276607,0.00008513773,0.12442348],"genre_scores_gemma":[0.9174554,0.0812998,0.00098511,0.0000652974,0.000041010906,0.000006736893,0.000029257048,0.000014012884,0.00010341059],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990677,0.000037883376,0.0005164609,0.00015740009,0.000041662977,0.00017888322],"domain_scores_gemma":[0.99950624,0.00013884745,0.00009619251,0.00019904177,0.000022240467,0.000037464742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003899767,0.00010839249,0.00056443363,0.00017159294,0.000013923232,0.000008566102,0.00009719549,0.000033981436,0.0005546381],"category_scores_gemma":[0.000117782605,0.00011487166,0.0002749554,0.00079553534,0.000007784439,0.00007814863,0.000022579721,0.00008431891,0.000009502338],"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":[0.000010410243,0.00011088525,0.18935032,0.026965527,0.0036775307,0.00011691361,0.00020934871,0.52249885,0.00085570075,0.0026880025,0.0022057502,0.2513108],"study_design_scores_gemma":[0.00022809954,0.000010726541,0.0060907924,0.004322406,0.0012145388,0.00002239997,0.000022470638,0.96850854,0.0038008841,0.0001279648,0.015165898,0.00048524915],"about_ca_topic_score_codex":0.000028315037,"about_ca_topic_score_gemma":0.00016066193,"teacher_disagreement_score":0.44600976,"about_ca_system_score_codex":0.00009798283,"about_ca_system_score_gemma":0.000030813546,"threshold_uncertainty_score":0.6072898},"labels":[],"label_agreement":null},{"id":"W3119066328","doi":"10.1109/tpwrs.2021.3050150","title":"Short-Term Demand Prediction Using an Ensemble of Linearly-Constrained Estimators","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Power Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Microgrid; Perceptron; Artificial neural network; Estimator; Stability (learning theory); Term (time); Ensemble learning; Grid; Mathematical optimization; Artificial intelligence; Machine learning; Control (management); Mathematics; Statistics","score_opus":0.022597094172229288,"score_gpt":0.23700113777293355,"score_spread":0.21440404360070425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119066328","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":[0.42821634,0.00014428826,0.5682999,0.0000011567469,0.0022309322,0.00007024156,0.0000596999,0.00018384475,0.0007936025],"genre_scores_gemma":[0.9989157,0.000016412068,0.00086356,0.0000033154342,0.000038340266,0.000012576631,0.000010415461,0.00005058263,0.000089109686],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988444,0.00005284201,0.00043912808,0.00021971944,0.00020336262,0.00024053661],"domain_scores_gemma":[0.99940497,0.00004866488,0.00003768334,0.00029423824,0.00008929065,0.00012516721],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013204254,0.00019557631,0.00027396396,0.00013948481,0.000104026054,0.000053474654,0.000079620986,0.00015286158,0.000035223842],"category_scores_gemma":[0.000002718244,0.00021041013,0.00010727269,0.00025854993,0.00003562312,0.00024756868,6.5764175e-7,0.00019288382,0.000004455319],"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":[0.000008184952,0.00008450234,0.0002235903,0.00013116533,0.00010785705,0.00002301037,0.0004810489,0.91224927,0.08585215,0.000032420376,0.000011255338,0.0007955685],"study_design_scores_gemma":[0.0004247711,0.00012505952,0.00018680606,0.00052823976,0.00010377574,0.00031723254,0.00034120952,0.7452153,0.25224897,0.000005214015,0.00018009779,0.00032333063],"about_ca_topic_score_codex":0.000027700315,"about_ca_topic_score_gemma":0.000024661604,"teacher_disagreement_score":0.57069933,"about_ca_system_score_codex":0.00006131991,"about_ca_system_score_gemma":0.0000422898,"threshold_uncertainty_score":0.8580276},"labels":[],"label_agreement":null},{"id":"W3119092950","doi":"10.1109/ssci47803.2020.9308333","title":"Regularized Probabilistic Forecasting of Electricity Wholesale Price and Demand","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Electricity price forecasting; Exponential smoothing; Volatility (finance); Electricity; Econometrics; Probabilistic forecasting; Economics; Electricity market; Probabilistic logic; Computer science; Engineering; Artificial intelligence","score_opus":0.018625877192396924,"score_gpt":0.18626932763829693,"score_spread":0.1676434504459,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119092950","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":[0.94363064,0.00037851284,0.03373417,0.000072455485,0.000046059475,0.00012191178,0.0000027420792,0.00024485745,0.021768631],"genre_scores_gemma":[0.9936342,0.000015703905,0.0061843996,0.000039138406,0.000050813593,0.0000034850975,0.0000024115425,0.00001786401,0.00005198778],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994278,0.000011624974,0.00019178296,0.00012201679,0.00007895779,0.00016781827],"domain_scores_gemma":[0.9997132,0.00008077208,0.000028769637,0.00006611561,0.000024980127,0.00008619145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008997591,0.00009918183,0.00016826922,0.00002664839,0.000029308238,0.0000140083785,0.000060600916,0.000045074747,0.000021076243],"category_scores_gemma":[0.00013185422,0.00009064334,0.000025823729,0.00020986087,0.000023661447,0.00007041314,0.000027024766,0.000090670255,0.0000013188322],"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":[0.00020235284,0.000110509325,0.013907725,0.005769417,0.00041805033,0.00005729715,0.00734362,0.4087417,0.46678334,0.029073672,0.0029251196,0.064667165],"study_design_scores_gemma":[0.00038588344,0.000071162816,0.0003921929,0.000047525078,0.000018935192,0.000012891577,0.00002593392,0.9684438,0.029247658,0.00042148144,0.0007679871,0.00016451563],"about_ca_topic_score_codex":0.0000059128,"about_ca_topic_score_gemma":0.0000041512512,"teacher_disagreement_score":0.5597021,"about_ca_system_score_codex":0.000008763215,"about_ca_system_score_gemma":0.00000825854,"threshold_uncertainty_score":0.3696328},"labels":[],"label_agreement":null},{"id":"W3119191403","doi":"10.18280/ejee.220606","title":"Mitigation of Certain Power Quality Issues in Wind Energy Conversion System Using UPQC and IUPQC Devices","year":2020,"lang":"en","type":"article","venue":"European Journal of Electrical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"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":"Harmonics; Voltage sag; Wind power; Renewable energy; Voltage; Engineering; Electric power system; Electrical engineering; Power (physics); Electronic engineering; Computer science; Automotive engineering; Power quality","score_opus":0.01375233073632845,"score_gpt":0.21257463237158794,"score_spread":0.1988223016352595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119191403","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":[0.9717022,0.0026471936,0.025049327,0.00003521731,0.00013424101,0.00002088826,0.0000011601687,0.000044727767,0.00036505447],"genre_scores_gemma":[0.9980837,0.00005908981,0.0016680184,0.000017763246,0.00013544253,6.1617065e-8,7.51229e-7,0.00003296477,0.0000021770193],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988765,0.000105304905,0.000579614,0.000091678536,0.0001678783,0.00017903808],"domain_scores_gemma":[0.99954474,0.000091254056,0.0001328916,0.000051299918,0.000049445625,0.0001303933],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003974275,0.00013512418,0.00029360334,0.00016125443,0.000016806778,0.00002060447,0.0001078845,0.000032293497,0.0000041015214],"category_scores_gemma":[0.00010200738,0.00013210956,0.000058294903,0.00033425583,0.000010274859,0.0001663815,0.000021038468,0.00022359985,4.9896744e-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":[0.0000498366,0.00001476739,0.004326355,0.00047758812,0.00008566376,0.00019534568,0.0010575652,0.8346168,0.1553084,0.0010183997,0.000032790824,0.0028165362],"study_design_scores_gemma":[0.00088927674,0.0002777786,0.006836812,0.00077093736,0.000031769036,0.00013360157,0.00019826012,0.96129036,0.027113257,0.0000025799889,0.0021349078,0.0003204765],"about_ca_topic_score_codex":0.000009491059,"about_ca_topic_score_gemma":3.365366e-7,"teacher_disagreement_score":0.12819514,"about_ca_system_score_codex":0.000057486926,"about_ca_system_score_gemma":0.000010950424,"threshold_uncertainty_score":0.53872716},"labels":[],"label_agreement":null},{"id":"W3119608174","doi":"10.3390/electronics10020151","title":"Data-Driven Trajectory Prediction of Grid Power Frequency Based on Neural Models","year":2021,"lang":"en","type":"article","venue":"Electronics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":23,"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":"Robustness (evolution); Computer science; Electric power system; Artificial neural network; Mean squared error; Phasor measurement unit; Control theory (sociology); Time series; Power (physics); Phasor; Artificial intelligence; Machine learning; Statistics; Mathematics","score_opus":0.02159183288869462,"score_gpt":0.21352641749062798,"score_spread":0.19193458460193336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119608174","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":[0.88003707,0.017236767,0.04081989,0.00013294641,0.0032377373,0.00021973217,0.0016119671,0.0010199726,0.05568391],"genre_scores_gemma":[0.9981842,0.00015846136,0.0009091446,0.00004498226,0.000114570714,0.0000035637859,0.00051985454,0.000033308817,0.00003190332],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991967,0.000024839734,0.00018119304,0.00018132398,0.00015702094,0.00025893774],"domain_scores_gemma":[0.9994336,0.00003969695,0.000025983434,0.00042540656,0.000034626573,0.000040682462],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007400179,0.00011629917,0.00012980597,0.000047540118,0.000030215782,0.00001093782,0.00016715612,0.0000687873,0.000062620544],"category_scores_gemma":[0.000016970815,0.0001243788,0.00004502117,0.00015718381,0.000011763391,0.00016196679,0.000018307297,0.00024828486,0.0000025743248],"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":[0.000005445727,0.00003093933,0.00009446877,0.000027296705,0.000029504969,0.000008364099,0.00005504237,0.9863365,0.009604772,0.0010432435,0.0010880542,0.0016763232],"study_design_scores_gemma":[0.00021671812,0.00010311514,0.00015361577,0.00003200196,0.000016691682,0.0000058454357,0.000006391752,0.985223,0.009027858,0.00019952522,0.0049104,0.000104857434],"about_ca_topic_score_codex":0.0000019692736,"about_ca_topic_score_gemma":0.000043927033,"teacher_disagreement_score":0.118147135,"about_ca_system_score_codex":0.00007065518,"about_ca_system_score_gemma":0.000095945245,"threshold_uncertainty_score":0.507202},"labels":[],"label_agreement":null},{"id":"W3120800000","doi":"10.15173/esr.v24i1.4135","title":"A New Hybrid Wavelet-Neural Network Approach for Forecasting Electricity","year":2020,"lang":"en","type":"article","venue":"Energy Studies Review","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"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":"Computer science; Artificial neural network; Electricity; Dual (grammatical number); Wavelet; Process (computing); Autoregressive conditional heteroskedasticity; Empirical research; Electricity market; Electricity price forecasting; Wavelet transform; Econometrics; Artificial intelligence; Machine learning; Economics; Mathematics; Volatility (finance); Statistics; Engineering","score_opus":0.06644680494165515,"score_gpt":0.2514001057935716,"score_spread":0.1849533008519164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120800000","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00013774248,0.7787839,0.20912199,0.0004903904,0.00044308713,0.00029610517,0.000007380667,0.0005111268,0.01020828],"genre_scores_gemma":[0.19097565,0.58537066,0.1918068,0.0168659,0.012065427,0.0008537559,0.0002698976,0.00047224428,0.0013196715],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99844897,0.00003664536,0.00046621414,0.00032471685,0.00014656814,0.00057690404],"domain_scores_gemma":[0.99934345,0.00017291747,0.000085587235,0.00016112486,0.000060470455,0.00017641977],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017817234,0.00033000342,0.0007347198,0.00002130495,0.00016722949,0.000020338255,0.00021109961,0.00003733417,0.0000171205],"category_scores_gemma":[0.0002495868,0.00028666077,0.00023783058,0.00046405167,0.000018575865,0.00009087969,0.00008475039,0.00015958077,0.0000024605263],"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":[0.000010332115,0.0000067881642,0.00001497682,0.004366278,0.00044183404,0.0000103904285,0.00011365748,0.14122474,0.000014237841,0.0015538199,0.29148015,0.5607628],"study_design_scores_gemma":[0.0002926809,0.00009622621,0.0000019648978,0.0007541219,0.00015489527,0.00002668513,0.000014030077,0.39204523,0.00029318122,0.00015441333,0.6057089,0.00045767459],"about_ca_topic_score_codex":0.0000083112245,"about_ca_topic_score_gemma":0.0000052482123,"teacher_disagreement_score":0.5603051,"about_ca_system_score_codex":0.00004713607,"about_ca_system_score_gemma":0.000018479464,"threshold_uncertainty_score":0.9999586},"labels":[],"label_agreement":null},{"id":"W3120802736","doi":"10.3390/en14010247","title":"Estimating Energy Forecasting Uncertainty for Reliable AI Autonomous Smart Grid Design","year":2021,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ballard Power Systems (Canada); Queen's University; Trent University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gradient boosting; Smart grid; Computer science; Electrical load; Artificial intelligence; Artificial neural network; Grid; Boosting (machine learning); Term (time); Machine learning; Data mining; Engineering; Random forest; Voltage","score_opus":0.023672074551308132,"score_gpt":0.2218904303412968,"score_spread":0.19821835578998867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3120802736","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":[0.045289733,0.0034979132,0.9245835,0.00022929798,0.00885117,0.00015754871,0.00004659878,0.0019702795,0.01537395],"genre_scores_gemma":[0.613542,0.0000655003,0.37933257,0.00035494912,0.0017187669,0.00023047034,0.00021487777,0.00018953565,0.00435134],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856246,0.000030002773,0.00037413265,0.00031387495,0.00014227745,0.0005772432],"domain_scores_gemma":[0.999075,0.000376673,0.000056900335,0.00027309547,0.00012122886,0.0000971316],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023185245,0.00027983432,0.00030413063,0.000082110004,0.0002630157,0.00013632349,0.00016489679,0.00012219991,0.00006201669],"category_scores_gemma":[0.00019777655,0.00029558825,0.00012937601,0.0002544802,0.000031481926,0.00023040982,0.00006289758,0.00014450154,0.0000040825153],"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":[0.0000072073753,0.000009352291,0.000028653241,0.0000784359,0.000050934334,0.000025183776,0.00017273829,0.97141176,0.0024135201,0.0029406408,0.010302724,0.01255883],"study_design_scores_gemma":[0.0002686756,0.000028935836,0.000003372408,0.00013542913,0.00002387832,0.00004367555,0.0000676692,0.85185546,0.06348978,0.0015561505,0.082201146,0.0003258366],"about_ca_topic_score_codex":0.000093560804,"about_ca_topic_score_gemma":0.00010180899,"teacher_disagreement_score":0.56825227,"about_ca_system_score_codex":0.00009754162,"about_ca_system_score_gemma":0.00010078323,"threshold_uncertainty_score":0.99994963},"labels":[],"label_agreement":null},{"id":"W3122204442","doi":"","title":"Emptying the Tank: Getting the Most Out of Limited Data","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","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 Calgary","funders":"","keywords":"Variation (astronomy); Value (mathematics); Marginal value; Simple (philosophy); Marginal cost; Data science; Computer science; Econometrics; Economics; Statistics; Mathematics; Microeconomics; Epistemology; Philosophy","score_opus":0.020092931788490868,"score_gpt":0.2396957507424095,"score_spread":0.21960281895391864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122204442","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":[0.89709604,0.018054621,0.05001078,0.002208468,0.003424577,0.0002398674,0.000025992662,0.000281905,0.02865777],"genre_scores_gemma":[0.99720556,0.0009865541,0.00012286694,0.00008365092,0.0012387476,0.0000010677539,0.000005600685,0.000033028635,0.00032289993],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981291,0.000055907705,0.00029320028,0.00011308631,0.00019740123,0.0012112985],"domain_scores_gemma":[0.9991532,0.00014828675,0.00010643183,0.0004963619,0.00006321256,0.00003252397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019785583,0.00012724345,0.00012020628,0.000041435585,0.00037511898,0.000060002865,0.0009662143,0.000047228976,0.000020066767],"category_scores_gemma":[0.0001449711,0.00007247247,0.00004683893,0.0002042374,0.00009085005,0.0001761539,0.00012552446,0.0013284987,0.000012286533],"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":[0.00010392528,0.000087622,0.0077458574,0.000094542185,0.00275458,0.00001418591,0.012061791,0.02455603,0.033438247,0.11017234,0.010843713,0.7981272],"study_design_scores_gemma":[0.002503286,0.0009532712,0.0016707456,0.00078510767,0.0005346962,0.0024474359,0.017107015,0.50508314,0.014788593,0.07673526,0.3756541,0.0017373227],"about_ca_topic_score_codex":0.000029183217,"about_ca_topic_score_gemma":0.0011324598,"teacher_disagreement_score":0.7963899,"about_ca_system_score_codex":0.00011540448,"about_ca_system_score_gemma":0.00025851754,"threshold_uncertainty_score":0.57717395},"labels":[],"label_agreement":null},{"id":"W3122377731","doi":"","title":"A looming revolution: Implications of self-generation for the risk exposure of retailers","year":2018,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Science Foundation Ireland; Egg Farmers of Canada","keywords":"Hedge; Revenue; Electricity; Renewable energy; Risk management; Business; Context (archaeology); Economics; Microeconomics; Finance; Engineering","score_opus":0.03616538740591313,"score_gpt":0.2808122170618411,"score_spread":0.244646829655928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122377731","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":[0.9726662,0.0015967191,0.0013733527,0.00012621457,0.00095336547,0.0012334312,0.00036022643,0.00009439912,0.021596096],"genre_scores_gemma":[0.9847745,0.009306399,0.004968093,0.0000032846742,0.0004467492,0.00029507856,0.00006441772,0.000047337504,0.000094161755],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859446,0.00008448292,0.00060497987,0.00030415147,0.00010296376,0.00030893457],"domain_scores_gemma":[0.99818915,0.00059949537,0.00020497126,0.0007655882,0.0001930463,0.00004776386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014725155,0.00016004396,0.0002975302,0.00019805513,0.00013518591,0.000025270288,0.00041014925,0.00025902968,0.00001546647],"category_scores_gemma":[0.00029925437,0.00014980657,0.0001607475,0.00011409426,0.00014507199,0.000052039813,0.00018888336,0.0005417477,6.1651315e-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":[0.00003574396,0.000055457574,0.012447542,0.000754512,0.00045693768,3.062703e-7,0.0015261181,0.86441255,0.00392027,0.0011633493,0.00052897335,0.11469825],"study_design_scores_gemma":[0.0005790924,0.00015498316,0.008441398,0.00042524684,0.000080333855,0.000004333569,0.00039365087,0.96833426,0.0064771697,0.0015024402,0.01319988,0.00040722397],"about_ca_topic_score_codex":0.0000367251,"about_ca_topic_score_gemma":0.0002807503,"teacher_disagreement_score":0.11429103,"about_ca_system_score_codex":0.0002919503,"about_ca_system_score_gemma":0.0001652753,"threshold_uncertainty_score":0.6108934},"labels":[],"label_agreement":null},{"id":"W3122847742","doi":"10.1115/power2020-16376","title":"Integration of Hydro and Renewable Energy Resources in Energy Planning","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Renewable energy; Hydropower; Wind power; Environmental economics; Intermittent energy source; Electricity generation; Energy development; Renewable resource; Environmental science; Computer science; Distributed generation; Engineering; Economics; Electrical engineering","score_opus":0.014600182845430373,"score_gpt":0.19474424674164156,"score_spread":0.18014406389621118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122847742","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":[0.7780996,0.0023924136,0.053950213,0.000098347635,0.00010102165,0.000016640553,0.0000022784493,0.00023082743,0.16510865],"genre_scores_gemma":[0.9987779,0.00006694067,0.0008212939,0.00009113959,0.000050067778,0.0000020323903,0.0000053911963,0.000011652344,0.000173569],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960434,0.0000095503665,0.00015134459,0.00008501476,0.00005235205,0.00009741658],"domain_scores_gemma":[0.9998576,0.00003373019,0.000016081673,0.000044186447,0.0000060268094,0.00004238039],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000033164717,0.00007182883,0.00010904444,0.000057201734,0.000011661053,0.000010621426,0.00004422208,0.000042678832,0.000020864558],"category_scores_gemma":[0.000013839547,0.00006660803,0.0000132691275,0.0001456826,0.000010910811,0.00007327027,0.000017617203,0.00003857093,1.8202698e-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":[0.000011962846,0.0000042100287,0.0011391228,0.000029789708,0.00001103604,0.0000063495418,0.002076513,0.90558445,0.077534124,0.0023263737,0.0009434744,0.010332576],"study_design_scores_gemma":[0.0001901536,0.000043625685,0.000194764,0.0000910996,0.0000031527588,0.0000022346962,0.00028546623,0.80466545,0.16254443,0.00026859864,0.03158409,0.00012693061],"about_ca_topic_score_codex":0.0013563868,"about_ca_topic_score_gemma":0.0005769643,"teacher_disagreement_score":0.22067831,"about_ca_system_score_codex":0.000006709151,"about_ca_system_score_gemma":0.0000027319388,"threshold_uncertainty_score":0.27161965},"labels":[],"label_agreement":null},{"id":"W3122888226","doi":"10.1016/j.cie.2021.107128","title":"Industry 4.0 and demand forecasting of the energy supply chain: A literature review","year":2021,"lang":"en","type":"review","venue":"Computers & Industrial Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":92,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Demand forecasting; Supply chain; Profitability index; Energy demand; Supply and demand; Energy (signal processing); Computer science; Operations research; Economics; Environmental economics; Business; Engineering; Marketing; Finance; Macroeconomics","score_opus":0.034615843416995765,"score_gpt":0.2273145253135129,"score_spread":0.19269868189651712,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122888226","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019757344,0.99582785,0.00068753684,0.000013476375,0.0028438421,0.00029669356,0.000051373012,0.00014633934,0.00011312382],"genre_scores_gemma":[0.000108153225,0.99791384,0.0002863652,0.000039936258,0.0013251418,0.00004496142,0.0000920015,0.00014005724,0.000049572893],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99780613,0.00010449743,0.0009624725,0.00039949673,0.00024965062,0.00047777165],"domain_scores_gemma":[0.9986736,0.00035205332,0.00024689472,0.0005064894,0.00005577676,0.00016514304],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00030866626,0.0007197446,0.001985464,0.00021341031,0.00007102441,0.00010321094,0.0005054735,0.001641951,0.000009136224],"category_scores_gemma":[0.0001766032,0.0005689132,0.00053636095,0.0012504513,0.000039303155,0.00011790087,0.00034299315,0.0028457802,2.976686e-7],"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":[5.360823e-7,0.0000069202456,0.0000028065488,0.03785455,0.00036347925,0.000064577805,0.00005714716,0.015563354,0.0000014060502,0.0004155812,0.002849087,0.94282055],"study_design_scores_gemma":[0.00014710487,0.000011001661,2.3542907e-7,0.32804546,0.00031357928,0.00037713774,0.0000015669775,0.013329706,0.000014342426,9.041498e-7,0.6573638,0.00039518764],"about_ca_topic_score_codex":0.0000044637077,"about_ca_topic_score_gemma":8.6235633e-7,"teacher_disagreement_score":0.9424254,"about_ca_system_score_codex":0.00009745957,"about_ca_system_score_gemma":0.00012019581,"threshold_uncertainty_score":0.9996762},"labels":[],"label_agreement":null},{"id":"W3122904967","doi":"10.2139/ssrn.2874333","title":"Electricity Prices, Large-Scale Renewable Integration, and Policy Implications","year":2016,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Wind power; Grid parity; Electricity; Renewable energy; Electricity market; Stand-alone power system; Volatility (finance); Electricity generation; Photovoltaic system; Electricity retailing; Economics; Solar power; Environmental economics; Natural resource economics; Business; Environmental science; Distributed generation; Econometrics; Power (physics); Electrical engineering; Engineering","score_opus":0.00707911291890805,"score_gpt":0.23197893185904558,"score_spread":0.22489981894013752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122904967","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":[0.06778003,0.021806113,0.87964314,0.0020970788,0.00073901756,0.00030176548,0.000088331675,0.00053487095,0.02700966],"genre_scores_gemma":[0.9643268,0.031477563,0.00070095883,0.000059653292,0.0015664825,0.000032003518,0.000043223466,0.00008762246,0.0017056965],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99693924,0.00003975748,0.00041794137,0.00029126683,0.0001610764,0.0021507186],"domain_scores_gemma":[0.9992083,0.00005142761,0.00016783758,0.0003063051,0.00011708512,0.00014908443],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00082052994,0.00032694577,0.00030323316,0.00034401673,0.000279577,0.00014268637,0.00035234177,0.00029262755,0.000011141107],"category_scores_gemma":[0.0000688577,0.00027271805,0.00012082566,0.00026198334,0.000028285149,0.00016812394,0.00013442835,0.0026254058,0.000007366112],"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":[0.00005144985,0.00017783353,0.004339797,0.00025803756,0.0014829556,0.000004780885,0.0017620077,0.024593435,0.013993306,0.7016389,0.005445858,0.24625166],"study_design_scores_gemma":[0.00056639675,0.000095860385,0.00075326953,0.0002712116,0.0000872674,0.0005563308,0.00016267822,0.005851354,0.001600753,0.97081774,0.018548865,0.00068827014],"about_ca_topic_score_codex":0.00015008857,"about_ca_topic_score_gemma":0.0026381107,"teacher_disagreement_score":0.8965468,"about_ca_system_score_codex":0.0013971315,"about_ca_system_score_gemma":0.0017098958,"threshold_uncertainty_score":0.9999725},"labels":[],"label_agreement":null},{"id":"W3122967885","doi":"10.2139/ssrn.720441","title":"Using Self-Organizing Maps to Adjust Intra-day Seasonality","year":2005,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"","keywords":"Seasonality; Smoothing; Econometrics; Volatility (finance); Artificial neural network; Component (thermodynamics); Computer science; Nonlinear system; Mathematics; Statistics; Artificial intelligence","score_opus":0.015692587785966855,"score_gpt":0.23851510439863616,"score_spread":0.2228225166126693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3122967885","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":[0.8460283,0.019688137,0.12186482,0.00042356836,0.0043391725,0.00036701153,0.00004782241,0.0011567447,0.0060844147],"genre_scores_gemma":[0.98448,0.0036415877,0.006795597,0.00011490266,0.004456029,0.0000068113563,0.000026946342,0.00021324663,0.00026483723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99492776,0.00012376619,0.0006317471,0.0004266008,0.0004472898,0.0034428136],"domain_scores_gemma":[0.9989281,0.000054442553,0.00017206144,0.00038849193,0.00012495392,0.00033190296],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0021054074,0.00058118015,0.0005322999,0.00020073322,0.00025448046,0.00022666545,0.00067035813,0.0004195449,0.00006747555],"category_scores_gemma":[0.00006774003,0.00062368816,0.00026278495,0.00030256115,0.000018509783,0.00018010517,0.00030556918,0.006986852,0.000051247735],"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":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000028452912,0.0001027491,0.0017407276,0.00029327677,0.0019052337,0.00005215562,0.0024294762,0.8846713,0.004312498,0.016545992,0.00085898483,0.08705919],"study_design_scores_gemma":[0.0058884774,0.00094166497,0.0032029222,0.008353657,0.0035154035,0.014962359,0.005837635,0.40685833,0.021322725,0.30793154,0.2036805,0.017504802],"about_ca_topic_score_codex":0.00006481806,"about_ca_topic_score_gemma":0.00079484173,"teacher_disagreement_score":0.47781295,"about_ca_system_score_codex":0.0044827014,"about_ca_system_score_gemma":0.0019300212,"threshold_uncertainty_score":0.99962145},"labels":[],"label_agreement":null},{"id":"W3123584373","doi":"","title":"Forecasting Short Term Power Prices in the Ontario Electricity Market (OEM) with a Fuzzy Logic Based Inference System","year":2008,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","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":"Original equipment manufacturer; Bidding; Artificial neural network; Econometrics; Term (time); Electricity price forecasting; Value (mathematics); Fuzzy inference system; Computer science; Adaptive neuro fuzzy inference system; Electricity; Inference; Economics; Fuzzy logic; Electricity market; Operations research; Engineering; Fuzzy control system; Microeconomics; Artificial intelligence; Machine learning; Electrical engineering","score_opus":0.015565297491943018,"score_gpt":0.19867572654143123,"score_spread":0.1831104290494882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3123584373","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":[0.9544711,0.00083190604,0.01168706,0.000030252691,0.00012011529,0.00016014813,9.821848e-7,0.00011037718,0.032588065],"genre_scores_gemma":[0.9990573,0.00013860305,0.0004774561,0.000040474442,0.00010807078,0.000017914603,0.000002984159,0.00003643437,0.00012075718],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99686486,0.00007244848,0.0003881578,0.00021014681,0.00038558687,0.0020788112],"domain_scores_gemma":[0.99940735,0.00016959134,0.00009149789,0.00020261308,0.0000554093,0.000073516494],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0013693895,0.00029592443,0.00027842078,0.00020106044,0.00029916523,0.00007749679,0.00043493687,0.0001017121,0.000019930078],"category_scores_gemma":[0.000032328164,0.00019565252,0.00008973501,0.000477566,0.000039152917,0.00024086663,0.000016822169,0.0024607985,0.0000026317537],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000744026,0.00041822178,0.74929595,0.00030786134,0.0008910198,0.0018117145,0.010974687,0.17914537,0.0006931611,0.037365664,0.0003101061,0.01804224],"study_design_scores_gemma":[0.011618761,0.009438254,0.31092334,0.004307998,0.0006426023,0.09523822,0.011451341,0.52032536,0.0023374571,0.019312622,0.0068174456,0.0075865733],"about_ca_topic_score_codex":0.0006805748,"about_ca_topic_score_gemma":0.043324746,"teacher_disagreement_score":0.4383726,"about_ca_system_score_codex":0.001712489,"about_ca_system_score_gemma":0.0011690914,"threshold_uncertainty_score":0.99984056},"labels":[],"label_agreement":null},{"id":"W3126256729","doi":"10.1109/epec48502.2020.9319923","title":"Peak-Load Forecasting of Nova Scotia During Winter Using Support Vector Machine with Optimally Configured Hyperparameters","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Electric Power and Energy Conference (EPEC)","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Hyperparameter; Support vector machine; Nova scotia; Computer science; Local optimum; Process (computing); Machine learning; Artificial intelligence; Hyperparameter optimization; Simple (philosophy); Mathematical optimization; Mathematics","score_opus":0.01901396959480218,"score_gpt":0.1985271251821307,"score_spread":0.17951315558732853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126256729","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":[0.96857774,0.00058301457,0.018633453,0.00006249216,0.0003836602,0.00008298791,0.000024230625,0.00022726879,0.011425165],"genre_scores_gemma":[0.9979117,0.00007540746,0.0015415844,0.00012591218,0.00014282053,0.0000042239512,0.000017980867,0.00008962685,0.00009072632],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793386,0.000035901005,0.00051446387,0.0004945187,0.00034310532,0.00067813275],"domain_scores_gemma":[0.99910474,0.00006971427,0.00014186474,0.00021631594,0.0001536771,0.000313715],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00009906315,0.00047648296,0.00060660543,0.00012996767,0.000088571476,0.00009015315,0.00026916206,0.00014485304,0.00029152303],"category_scores_gemma":[0.000045417277,0.0004421795,0.00010833325,0.00058873097,0.00007092614,0.0002613856,0.000048917376,0.00035253208,0.0000048454012],"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":[0.00078627537,0.000088959234,0.0033696627,0.00054859254,0.0009237416,0.0004753963,0.0033048668,0.16172488,0.8207765,0.00086660817,0.00039606527,0.0067384127],"study_design_scores_gemma":[0.0023896766,0.0011265823,0.0010615907,0.0003917992,0.00022223109,0.00047894995,0.00015483756,0.7834243,0.20837447,0.000023556246,0.0009335223,0.0014184832],"about_ca_topic_score_codex":0.00032122555,"about_ca_topic_score_gemma":0.00018585345,"teacher_disagreement_score":0.6216994,"about_ca_system_score_codex":0.00007547798,"about_ca_system_score_gemma":0.00018383292,"threshold_uncertainty_score":0.999803},"labels":[],"label_agreement":null},{"id":"W3126704758","doi":"10.3390/smartcities4010014","title":"Transfer Learning by Similarity Centred Architecture Evolution for Multiple Residential Load Forecasting","year":2021,"lang":"en","type":"article","venue":"Smart Cities","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"London Hydro; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Secretaría de Educación Superior, Ciencia, Tecnología e Innovación","keywords":"Neighbourhood (mathematics); Computer science; Transfer of learning; Architecture; Artificial intelligence; Demand response; Artificial neural network; Machine learning; Operations research; Engineering; Geography; Electricity","score_opus":0.016004720375298428,"score_gpt":0.19072617885641935,"score_spread":0.17472145848112092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3126704758","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":[0.85668635,0.004415322,0.13254689,0.00017187002,0.001316429,0.00018139619,0.00015536281,0.0006263168,0.0039000623],"genre_scores_gemma":[0.996603,0.000024433983,0.0013031955,0.00003144973,0.00033156388,0.000025369212,0.00015664956,0.000050033374,0.0014743046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989368,0.00003977468,0.00022233318,0.00020946935,0.00017919327,0.00041244805],"domain_scores_gemma":[0.99951255,0.0002048354,0.000015745709,0.00010992979,0.000089909096,0.00006700358],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010084065,0.00018316967,0.00018723773,0.000044135642,0.00024807238,0.00007265729,0.00007767895,0.00011658021,0.00005741087],"category_scores_gemma":[0.000206057,0.00020576571,0.00013026907,0.00011876081,0.00003578258,0.00015584765,0.000021602758,0.00027667676,0.0000025998704],"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":[0.00013818873,0.000044662764,0.014256294,0.000937121,0.00027041032,0.000048908034,0.007176535,0.84215575,0.10643512,0.001019963,0.012706345,0.014810702],"study_design_scores_gemma":[0.0034813215,0.00014728335,0.0011077526,0.00057724095,0.00015354782,0.00011605252,0.0043852,0.46222743,0.28399548,0.0031360425,0.2392409,0.0014317589],"about_ca_topic_score_codex":0.00007602386,"about_ca_topic_score_gemma":0.0009454287,"teacher_disagreement_score":0.37992832,"about_ca_system_score_codex":0.00009060396,"about_ca_system_score_gemma":0.00005184222,"threshold_uncertainty_score":0.8390882},"labels":[],"label_agreement":null},{"id":"W3127021943","doi":"10.48550/arxiv.2102.05657","title":"A Hybrid Deep Learning-Based State Forecasting Method for Smart Power Grids","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Energy Load and Power Forecasting","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 Guelph","funders":"","keywords":"Deep learning; Computer science; Artificial intelligence; State (computer science); Smart grid; Machine learning; Engineering; Algorithm; Electrical engineering","score_opus":0.044739671349613576,"score_gpt":0.18593976246321275,"score_spread":0.14120009111359919,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127021943","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":[0.27293527,0.00018373728,0.7221289,0.00000736381,0.0009829868,0.00022411096,0.000043619886,0.00052998634,0.0029639741],"genre_scores_gemma":[0.9845932,0.000059378122,0.01384864,0.000043136082,0.00011142076,0.0000065728186,0.00026729802,0.00014495177,0.000925399],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979035,0.00013439431,0.00032719862,0.0008620864,0.00009284256,0.0006799268],"domain_scores_gemma":[0.9984328,0.00044601312,0.00017330486,0.0005218841,0.00020709918,0.00021889378],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004328256,0.0005389866,0.0005681049,0.00028713339,0.00021029983,0.00013459129,0.00045494066,0.00025618487,0.000107501866],"category_scores_gemma":[0.00014252204,0.00068977283,0.00048777592,0.00030399195,0.000047079182,0.00016276012,0.0003284378,0.0010063689,0.000009541241],"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":[0.000042110914,0.000025036366,0.0006412816,0.0003711306,0.00021830139,0.0003252982,0.0001932977,0.9952921,0.00010407711,0.00032147183,0.000090586145,0.0023753084],"study_design_scores_gemma":[0.00067573646,0.00006702592,0.000060132315,0.00030848387,0.00014640647,0.0000117089685,0.000099417004,0.9908625,0.0023149222,0.0014450621,0.0032728713,0.0007357673],"about_ca_topic_score_codex":0.00008489628,"about_ca_topic_score_gemma":0.00012121282,"teacher_disagreement_score":0.71165794,"about_ca_system_score_codex":0.00025080895,"about_ca_system_score_gemma":0.00012007651,"threshold_uncertainty_score":0.99955535},"labels":[],"label_agreement":null},{"id":"W3127355168","doi":"10.1109/epec48502.2020.9320082","title":"Modelling Prime Diesel Electric Generator Fuel Consumption across Genset Sizings","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Electric Power and Energy Conference (EPEC)","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Yukon University","funders":"","keywords":"Diesel generator; Diesel fuel; Prime mover; Sizing; Fuel efficiency; Generator (circuit theory); Automotive engineering; Population; Engineering; Power (physics); Physics; Chemistry","score_opus":0.02274533829615303,"score_gpt":0.21536619096328408,"score_spread":0.19262085266713105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127355168","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":[0.79591817,0.011543391,0.18636099,0.00016763463,0.00078315416,0.000110518726,0.000037577418,0.00093092484,0.0041476646],"genre_scores_gemma":[0.9905115,0.0076282993,0.00047644763,0.0005028142,0.00043893515,0.000032727712,0.000038138107,0.000091153066,0.00028001814],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974372,0.000055157157,0.0004955944,0.00063679833,0.0003210669,0.0010541875],"domain_scores_gemma":[0.99899995,0.00009311413,0.000095170995,0.0002433338,0.000117173,0.0004512639],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014677562,0.0005211493,0.0005124136,0.000108096094,0.0002405108,0.00022423045,0.0002984301,0.0002682761,0.00009399921],"category_scores_gemma":[0.00003139813,0.0005400011,0.00012177818,0.00072415033,0.000040704268,0.0003090983,0.000054115546,0.00048427354,0.00004205477],"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":[0.00027578592,0.00012446566,0.00096617016,0.0004892157,0.0007481601,0.00022935079,0.007118214,0.3397396,0.5778875,0.008993064,0.008476166,0.0549523],"study_design_scores_gemma":[0.00058245315,0.00019405647,0.00007538635,0.0000412459,0.000047679314,0.000047297966,0.000035092966,0.93405914,0.046703726,0.00025674474,0.017158162,0.0007990179],"about_ca_topic_score_codex":0.00008226059,"about_ca_topic_score_gemma":0.000023300012,"teacher_disagreement_score":0.5943195,"about_ca_system_score_codex":0.00007700911,"about_ca_system_score_gemma":0.0000940924,"threshold_uncertainty_score":0.99970514},"labels":[],"label_agreement":null},{"id":"W3128928242","doi":"10.1109/epec48502.2020.9320075","title":"Bayesian Optimization Based ANN Model for Short Term Wind Speed Forecasting in Newfoundland, Canada","year":2020,"lang":"en","type":"article","venue":"2020 IEEE Electric Power and Energy Conference (EPEC)","topic":"Energy Load and Power Forecasting","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":"Memorial University of Newfoundland","funders":"","keywords":"Hyperparameter; Wind speed; Artificial neural network; Mean squared error; Support vector machine; Wind power; Computer science; Artificial intelligence; Random forest; Machine learning; Bayesian probability; Mean absolute percentage error; Bayesian inference; Term (time); Meteorology; Statistics; Engineering; Mathematics; Geography","score_opus":0.019422214674611825,"score_gpt":0.1969165871096679,"score_spread":0.17749437243505606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3128928242","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":[0.064248465,0.0003485593,0.9260036,0.00030841096,0.00054483744,0.00016914273,0.000042282038,0.00019220861,0.0081425365],"genre_scores_gemma":[0.99750775,0.000068984504,0.0015667784,0.00040428631,0.00016019211,0.000011394654,0.00008552131,0.0000573151,0.00013776263],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845874,0.000020867155,0.0003683925,0.0003886185,0.00019158395,0.0005718027],"domain_scores_gemma":[0.9994079,0.00008881815,0.00004294056,0.00013080558,0.00006637331,0.00026318082],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0000816854,0.00031805932,0.00034553985,0.00010329803,0.000101427984,0.00008789721,0.00017855626,0.00013235556,0.000039150415],"category_scores_gemma":[0.000042226042,0.00034008318,0.000054915938,0.00044683262,0.000014933945,0.00017772347,0.000016184686,0.00020345046,2.3159956e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004264468,0.00000902236,0.0012998496,0.000042882242,0.000024917166,0.000021094747,0.00017684572,0.9925487,0.0011511398,0.00011523293,0.00065039255,0.0039172806],"study_design_scores_gemma":[0.00054952485,0.00007980045,0.00009558991,0.000044075936,0.000020175625,0.000007155101,0.000016148271,0.99757254,0.00063021615,0.000047390982,0.0005351655,0.0004022207],"about_ca_topic_score_codex":0.01081033,"about_ca_topic_score_gemma":0.090206325,"teacher_disagreement_score":0.9332593,"about_ca_system_score_codex":0.00015361783,"about_ca_system_score_gemma":0.00040384757,"threshold_uncertainty_score":0.9999051},"labels":[],"label_agreement":null},{"id":"W3129054759","doi":"10.1109/tii.2021.3056867","title":"A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":388,"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 British Columbia","funders":"Fundamental Research Funds for the Central Universities","keywords":"Recurrent neural network; Computer science; Smart grid; Robustness (evolution); Renewable energy; Electricity; Electricity generation; Wind power; Artificial intelligence; Machine learning; Engineering; Artificial neural network; Power (physics)","score_opus":0.03356084192651567,"score_gpt":0.2200618236794241,"score_spread":0.1865009817529084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129054759","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":[0.010312566,0.000027011914,0.98656636,0.000020382035,0.0010583268,0.0003358319,0.00024570394,0.000206147,0.0012276701],"genre_scores_gemma":[0.96105546,0.000048925715,0.03677676,0.00018200092,0.0005920907,0.00049625325,0.0003326888,0.000066980196,0.00044881867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998819,0.000020773228,0.00053250504,0.00012520656,0.00017628264,0.00032623735],"domain_scores_gemma":[0.99925256,0.00026514684,0.0000729452,0.0001610239,0.00014786707,0.00010046445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026987042,0.0002045703,0.00024232667,0.00011770399,0.00033919327,0.00013662147,0.00007545547,0.0002715081,0.00001005197],"category_scores_gemma":[0.000043902313,0.0002173917,0.000101753896,0.00027612934,0.00002223918,0.0003207765,9.988679e-7,0.00021043852,3.708175e-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":[0.00008663509,0.000045265886,0.0000066861408,0.0001246119,0.00007091297,2.1141379e-7,0.0002725771,0.987008,0.0005239136,0.000038593545,0.0014910041,0.010331611],"study_design_scores_gemma":[0.0019314963,0.00015802335,2.60225e-7,0.000048010384,0.000065421555,0.0000054319075,0.00018456286,0.8308698,0.1577666,0.00001771167,0.008752911,0.00019979003],"about_ca_topic_score_codex":0.00009935938,"about_ca_topic_score_gemma":0.00015631037,"teacher_disagreement_score":0.9507429,"about_ca_system_score_codex":0.00015268967,"about_ca_system_score_gemma":0.00022709786,"threshold_uncertainty_score":0.8864976},"labels":[],"label_agreement":null},{"id":"W3129379202","doi":"10.1038/s41597-021-00851-9","title":"Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data","year":2021,"lang":"en","type":"article","venue":"Scientific Data","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":113,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Manitoba; Centre de Géomatique du Québec; University of Saskatchewan; Canadian Forest Service; Environment and Climate Change Canada; University of British Columbia; University of Waterloo; Global Institute for Water Security; Université Laval; Natural Resources Canada; Ministère des Ressources naturelles et des Forêts; Ontario Drive & Gear (Canada); McMaster University","funders":"Natural Environment Research Council; Sight Research UK","keywords":"Pipeline (software); Eddy covariance; Covariance; Computer science; Data science; Statistics; Mathematics; Biology; Ecosystem; Ecology; Programming language","score_opus":0.08306270731006536,"score_gpt":0.3033050218793115,"score_spread":0.22024231456924614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129379202","genre_codex":"methods","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000558262,0.017615054,0.7144811,0.008220402,0.035518635,0.0010534474,0.21924752,0.00051582465,0.0027897395],"genre_scores_gemma":[0.09012617,0.00019470796,0.04371051,0.00096807367,0.003489571,0.00010326856,0.8074135,0.00016031809,0.053833876],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99865556,0.00004817297,0.00022881465,0.0006008586,0.0002143088,0.00025227206],"domain_scores_gemma":[0.99642557,0.00026173246,0.00004919301,0.003133091,0.00007621141,0.00005419329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024793914,0.00012382734,0.0001341094,0.00002496981,0.00072980224,0.0009195416,0.0016819346,0.000036888025,0.00004762658],"category_scores_gemma":[0.0006500531,0.000076622695,0.000012663341,0.00042146075,0.00030708025,0.0006699727,0.0011853502,0.00016648269,0.000016256881],"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":[0.000009428001,0.000008347152,0.0000027754463,0.000045944395,0.000014257164,0.0000017301826,0.00014313002,0.0023954767,0.0001435724,0.00018521727,0.9530935,0.04395657],"study_design_scores_gemma":[0.00020217014,0.0000010478961,0.000003861239,0.000033182147,0.000028105724,0.000020867872,0.00010455331,0.4882306,0.00020058785,0.00007272739,0.51104236,0.00005995887],"about_ca_topic_score_codex":0.000017230244,"about_ca_topic_score_gemma":0.00030092447,"teacher_disagreement_score":0.6707706,"about_ca_system_score_codex":0.000009272072,"about_ca_system_score_gemma":0.00010449799,"threshold_uncertainty_score":0.88671607},"labels":[],"label_agreement":null},{"id":"W3131110071","doi":"10.1109/access.2021.3060290","title":"On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":287,"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":"Mean absolute percentage error; Computer science; Mean squared error; Artificial intelligence; Artificial neural network; Term (time); Electrical load; Deep learning; Scheduling (production processes); Convolutional neural network; Energy consumption; Machine learning; Power (physics); Statistics; Mathematical optimization; Engineering","score_opus":0.05566551343136726,"score_gpt":0.27342957954530106,"score_spread":0.21776406611393379,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3131110071","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":[0.77232015,0.0010338244,0.21148854,0.000006370802,0.00029848117,0.00011771889,0.0000062130903,0.0005361306,0.014192555],"genre_scores_gemma":[0.9745242,0.000066190274,0.024884233,0.000035730194,0.0002798416,0.000021256496,0.00002387604,0.000082461695,0.00008220844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987176,0.000025838175,0.00028173535,0.00035270205,0.00021793593,0.00040418122],"domain_scores_gemma":[0.9994809,0.00010383958,0.00004718811,0.00020158575,0.00006341802,0.000103080034],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001730141,0.0002808942,0.0002789734,0.00010261517,0.0001962128,0.00024704362,0.00020762425,0.00013605494,0.000016848342],"category_scores_gemma":[0.000050626262,0.00028580907,0.000065883185,0.0002743341,0.00003282273,0.00036914315,0.00011164987,0.00044890062,0.0000010949454],"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":[0.000053580312,0.00014571942,0.01851216,0.0007005866,0.00020487032,0.000378999,0.0010438767,0.84051675,0.08009749,0.0008350716,0.00003615587,0.057474732],"study_design_scores_gemma":[0.0003539846,0.000031586307,0.00011244896,0.00028242977,0.00003525804,0.00039315896,0.000034532117,0.939545,0.05800952,0.00012824942,0.0006105022,0.00046333985],"about_ca_topic_score_codex":0.00005985984,"about_ca_topic_score_gemma":0.00005755123,"teacher_disagreement_score":0.20220403,"about_ca_system_score_codex":0.0000849047,"about_ca_system_score_gemma":0.000025915837,"threshold_uncertainty_score":0.9999594},"labels":[],"label_agreement":null},{"id":"W3135200607","doi":"10.3390/app11052387","title":"Application of Long-Short-Term-Memory Recurrent Neural Networks to Forecast Wind Speed","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Long short term memory; Wind speed; Recurrent neural network; Artificial neural network; Computer science; Term (time); Meteorology; Wind power forecasting; Wind power; Electric power system; Artificial intelligence; Power (physics); Engineering; Geography","score_opus":0.020117368481807056,"score_gpt":0.24265453329141928,"score_spread":0.22253716480961222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135200607","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":[0.9565481,0.00028163672,0.023282262,0.000031938333,0.0006735301,0.00014254422,0.0000030483293,0.00009621898,0.018940708],"genre_scores_gemma":[0.9989499,0.000011569235,0.0007670388,0.000039183044,0.00017266697,0.0000058304126,0.000013033432,0.000011843298,0.000028950762],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990341,0.000008264401,0.00022234835,0.00025266857,0.0002078364,0.0002747508],"domain_scores_gemma":[0.9996246,0.00004880645,0.00002899297,0.00017755888,0.0000298136,0.00009021294],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018886727,0.00012088966,0.00015271698,0.000064342785,0.000097662705,0.000043100088,0.00022701545,0.00004728412,0.000021578897],"category_scores_gemma":[0.0000072315343,0.00011532067,0.00004034138,0.00063498016,0.000086642845,0.0000742179,0.000060801012,0.000098260636,0.000007324053],"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":[0.000003067992,0.000013563771,0.002556746,0.00002180795,0.000007367615,0.0000027845103,0.00020497214,0.8329932,0.015291454,0.0006113771,0.00012466706,0.14816897],"study_design_scores_gemma":[0.00014868882,0.00005119018,0.008022411,0.00004899186,0.000016710821,0.000019264397,0.00021569389,0.9262953,0.06427087,0.000103277955,0.0004576114,0.0003500381],"about_ca_topic_score_codex":0.0000038751837,"about_ca_topic_score_gemma":0.00003883862,"teacher_disagreement_score":0.14781892,"about_ca_system_score_codex":0.000017951872,"about_ca_system_score_gemma":0.000015800168,"threshold_uncertainty_score":0.47026402},"labels":[],"label_agreement":null},{"id":"W3138801212","doi":"10.1109/iv51561.2020.00101","title":"Interactive Data Driven Visualization for COVID-19 with Trends, Analytics and Forecasting","year":2020,"lang":"en","type":"article","venue":"2020 24th International Conference Information Visualisation (IV)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"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":"University of Alberta","keywords":"Python (programming language); Computer science; Dashboard; Visualization; Analytics; Data visualization; Raw data; Coronavirus disease 2019 (COVID-19); Data science; World Wide Web; Data mining; Programming language","score_opus":0.12241292403336819,"score_gpt":0.33364876251717696,"score_spread":0.21123583848380878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138801212","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":[0.016453272,0.000014835733,0.96673393,0.0021496406,0.00044965654,0.00033347946,0.0008923453,0.0004276145,0.012545214],"genre_scores_gemma":[0.98686844,0.000043445765,0.0037961225,0.0014383926,0.0002498566,0.000041231862,0.0074900985,0.000023584756,0.000048818023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986278,0.000028766366,0.0005873302,0.00023088124,0.00034587862,0.00017938389],"domain_scores_gemma":[0.99877095,0.00018015162,0.00028766948,0.00015898912,0.0003802505,0.00022197701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018319543,0.00021323105,0.00019815202,0.00021118787,0.00013371007,0.0004010419,0.000345603,0.000080346115,0.00023250074],"category_scores_gemma":[0.0007154579,0.0002098469,0.00003213323,0.00031011002,0.000046003355,0.0032083387,0.00010714576,0.00013542973,0.000011899816],"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":[0.0011913304,0.00007432628,0.008368261,0.0012192179,0.0011091501,0.000009773826,0.04389901,0.6085388,0.0009838417,0.14966126,0.045539286,0.13940574],"study_design_scores_gemma":[0.00090728264,0.00009373749,0.00025360647,0.00004767496,0.000029042692,0.000013804384,0.0011745412,0.9446642,0.00013280738,0.00010582228,0.05232927,0.0002482418],"about_ca_topic_score_codex":0.00002221681,"about_ca_topic_score_gemma":0.000050943363,"teacher_disagreement_score":0.9704152,"about_ca_system_score_codex":0.00011492538,"about_ca_system_score_gemma":0.000103494196,"threshold_uncertainty_score":0.85573083},"labels":[],"label_agreement":null},{"id":"W3141657608","doi":"10.1109/mper.2002.4312499","title":"An Approach to Implement Electricity Metering in Real-Time Using Artificial Neural Networks","year":2002,"lang":"en","type":"article","venue":"IEEE Power Engineering Review","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Metering mode; Electricity; Artificial neural network; Computer science; Electric power system; Electric power; Electricity market; Real-time computing; Automotive engineering; Operations research; Power (physics); Engineering; Artificial intelligence; Electrical engineering","score_opus":0.03465412165681438,"score_gpt":0.25360342655137114,"score_spread":0.21894930489455677,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3141657608","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":[0.6605289,0.039252914,0.28968602,0.00003960361,0.0022216062,0.0014665485,0.000016765853,0.0020634478,0.004724172],"genre_scores_gemma":[0.9894722,0.002317662,0.0076690568,0.00008928292,0.0002529097,0.0000587209,0.000009828726,0.00012205466,0.000008287718],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980111,0.000042144016,0.00062749063,0.00035269308,0.00019955379,0.00076699274],"domain_scores_gemma":[0.99926853,0.000043239776,0.000041747404,0.00039396522,0.000024043811,0.00022849147],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00042414016,0.00037967687,0.0005570705,0.00022077975,0.000043549,0.000059882295,0.0002724585,0.00008809659,0.00008746253],"category_scores_gemma":[0.0000248631,0.00040558027,0.000117692915,0.00087286584,0.0000052694877,0.00021836854,0.000026669848,0.0003181226,0.000017521135],"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":[0.0000012559872,0.00003360501,0.000022874825,0.00040645184,0.000019475254,0.00001088835,0.00007616104,0.979799,0.013668767,0.00006635081,0.00014922263,0.005745973],"study_design_scores_gemma":[0.00007543106,0.000038911392,0.00006231911,0.0007116849,0.000026244354,0.000031738105,0.0000017732465,0.99660605,0.000667051,0.0000015095826,0.0013265528,0.00045075986],"about_ca_topic_score_codex":0.000027033426,"about_ca_topic_score_gemma":0.0000033363422,"teacher_disagreement_score":0.32894328,"about_ca_system_score_codex":0.00017864205,"about_ca_system_score_gemma":0.000003947822,"threshold_uncertainty_score":0.9998396},"labels":[],"label_agreement":null},{"id":"W3142218652","doi":"10.18280/ria.350101","title":"Deep Learning Based Recurrent Neural Networks to Enhance the Performance of Wind Energy Forecasting: A Review","year":2021,"lang":"en","type":"review","venue":"Revue d intelligence artificielle","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":34,"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":"Recurrent neural network; Deep learning; Computer science; Artificial intelligence; Artificial neural network; Machine learning; Wind power; Feature (linguistics); Feature engineering; Energy (signal processing); Engineering","score_opus":0.047655443344918356,"score_gpt":0.2845316941387411,"score_spread":0.2368762507938227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142218652","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000022536053,0.9433114,0.05469205,0.000020912452,0.00086162967,0.00033891352,0.0000033296096,0.000092815426,0.00065640284],"genre_scores_gemma":[0.0032938423,0.99539375,0.00029236783,0.00007664084,0.0003011144,0.00011348846,0.000082550214,0.00010718343,0.00033905325],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971132,0.00021887379,0.0013372456,0.00050223665,0.00023360088,0.00059483264],"domain_scores_gemma":[0.99801964,0.0006286062,0.00035999648,0.00071293814,0.00013777472,0.00014104349],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00062916544,0.0005563487,0.0014903987,0.00013121428,0.00017421131,0.00005104878,0.0006873898,0.00019670914,0.00018403716],"category_scores_gemma":[0.00023862734,0.00043771227,0.00058247295,0.0014034614,0.000060312235,0.000072567134,0.00013968207,0.00084978633,0.00002543726],"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":[5.4692254e-7,0.00000816969,5.9398684e-7,0.009765555,0.000022091142,0.000004278186,0.0000350628,0.47933125,4.6824925e-7,0.000017881901,0.00009701867,0.5107171],"study_design_scores_gemma":[0.0000033040592,0.000044189088,1.9777941e-8,0.052829426,0.00010966763,0.000025003195,0.0000096196545,0.51474595,0.00013105242,2.9014126e-7,0.43186474,0.00023676931],"about_ca_topic_score_codex":0.00000701519,"about_ca_topic_score_gemma":0.000010697305,"teacher_disagreement_score":0.51048034,"about_ca_system_score_codex":0.00008611504,"about_ca_system_score_gemma":0.000055074797,"threshold_uncertainty_score":0.9998075},"labels":[],"label_agreement":null},{"id":"W3142554399","doi":"10.2172/1038916","title":"Wind Energy Forecasting with the Weather Research and Forecasting Model, CRADA No. TC02123.0","year":2011,"lang":"en","type":"report","venue":"","topic":"Energy Load and Power Forecasting","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":"Siemens (Canada)","funders":"Lawrence Livermore National Laboratory; U.S. Department of Energy","keywords":"Wind speed; Wind power; Offshore wind power; Meteorology; Wind power forecasting; Environmental science; Renewable energy; Terrain; Siemens; Upgrade; Computer science; Engineering; Electric power system; Geography; Power (physics)","score_opus":0.13600795263667356,"score_gpt":0.27084009626477934,"score_spread":0.13483214362810578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142554399","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008798021,0.002980022,0.007985802,0.000028255272,0.00070093357,0.00033608457,0.000027542641,0.00041629994,0.97872704],"genre_scores_gemma":[0.78390574,0.002577024,0.012251976,0.00008465518,0.0036616942,0.000267694,0.00012645981,0.0010482525,0.19607651],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959385,0.00007649188,0.0006534834,0.00072606665,0.0013344926,0.0012709688],"domain_scores_gemma":[0.99725765,0.00056958664,0.00018375143,0.00074131496,0.0010088641,0.00023882737],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019260992,0.000753456,0.0007130713,0.0003647124,0.0005675602,0.00024127075,0.00054423785,0.0005534917,0.00018387707],"category_scores_gemma":[0.00018940655,0.00047856782,0.00012469412,0.00048591977,0.0002956349,0.00024439456,0.00031849262,0.0015026395,0.000013102578],"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":[0.0003538221,0.00021130792,0.0022694045,0.0047003487,0.0033778506,0.00091038056,0.005030581,0.3575766,0.0011564562,0.008560458,0.2750168,0.340836],"study_design_scores_gemma":[0.00041692864,0.00027490486,0.00002137413,0.0014200119,0.000097677716,0.00070789043,0.00022257629,0.65882915,0.0005093837,0.0006955181,0.3355459,0.0012586968],"about_ca_topic_score_codex":0.0017071987,"about_ca_topic_score_gemma":0.002132819,"teacher_disagreement_score":0.78265053,"about_ca_system_score_codex":0.00021474247,"about_ca_system_score_gemma":0.0003897235,"threshold_uncertainty_score":0.9997666},"labels":[],"label_agreement":null},{"id":"W3143924650","doi":"10.3390/app11073048","title":"Wind Turbine Power Curve Modelling with Logistic Functions Based on Quantile Regression","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"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 Calgary","funders":"Program for Changjiang Scholars and Innovative Research Team in University; National Natural Science Foundation of China","keywords":"Quantile; Wind power; Outlier; Quantile regression; Turbine; Probabilistic logic; SCADA; Logistic regression; Logistic distribution; Wind power forecasting; Computer science; Statistics; Power (physics); Econometrics; Engineering; Mathematics; Electric power system","score_opus":0.03502635051081653,"score_gpt":0.23603279906099042,"score_spread":0.20100644855017388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3143924650","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":[0.42956823,0.000243047,0.3299616,0.000105437975,0.00062473497,0.00009090984,0.000009370345,0.0003394679,0.2390572],"genre_scores_gemma":[0.9939416,0.000004061188,0.0057361275,0.00007315438,0.00004619135,0.0000056562576,0.000010742869,0.000013112116,0.0001693768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999136,0.0000094479965,0.00011327056,0.00025134475,0.00025290402,0.00023704968],"domain_scores_gemma":[0.9996203,0.0001156363,0.000023980554,0.00015762281,0.000024444427,0.000057975652],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015302637,0.00012705915,0.00011194115,0.00007120995,0.00024800934,0.00006915527,0.000108767534,0.00004192186,0.000119924574],"category_scores_gemma":[0.00000763396,0.00008978468,0.00002395102,0.0005053401,0.00010752814,0.00006686607,0.000013638356,0.00013044153,0.000029067181],"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":[0.0000067176697,0.000015056318,0.0001528087,0.000007992513,0.0000037543764,0.000007618217,0.0000665118,0.99358207,0.0018024839,0.003851764,0.00018262067,0.0003206203],"study_design_scores_gemma":[0.00021141347,0.00007699408,0.000094494026,0.0001285872,0.000009556084,0.000005491472,0.00026202254,0.98501027,0.009973316,0.00024284706,0.0037633504,0.0002216516],"about_ca_topic_score_codex":0.0000059406802,"about_ca_topic_score_gemma":0.00001426383,"teacher_disagreement_score":0.5643733,"about_ca_system_score_codex":0.000016855667,"about_ca_system_score_gemma":0.000048358826,"threshold_uncertainty_score":0.36613128},"labels":[],"label_agreement":null},{"id":"W3144893223","doi":"10.1109/mper.2002.4311771","title":"A Study on Transformer Loading in Manitoba: Peak-Load Ambient Temperature","year":2002,"lang":"en","type":"article","venue":"IEEE Power Engineering Review","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Manitoba Hydro","funders":"","keywords":"Transformer; Environmental science; Distribution transformer; Bivariate analysis; Load distribution; Peak load; Atmospheric sciences; Electrical engineering; Voltage; Structural engineering; Engineering; Mathematics; Geology; Statistics; Automotive engineering","score_opus":0.0197466592391399,"score_gpt":0.21928026601798745,"score_spread":0.19953360677884754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3144893223","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":[0.8652904,0.1173479,0.00020246585,0.00013131602,0.0031359016,0.0012940265,0.000011908257,0.0010452227,0.011540832],"genre_scores_gemma":[0.98955595,0.009786582,0.00006936269,0.00015822248,0.00011420617,0.00011649473,0.0000022275174,0.000100545614,0.000096394615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9982843,0.000024391346,0.0005106923,0.0003187546,0.0003361537,0.00052570144],"domain_scores_gemma":[0.9994031,0.000052061067,0.000026635438,0.00036272,0.000028295346,0.00012714714],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002892065,0.00042220706,0.0005238848,0.00017261642,0.00003733587,0.000043533193,0.00022986419,0.000097668526,0.00014001198],"category_scores_gemma":[0.000037641985,0.00039750387,0.00014423537,0.0006014902,0.000007811777,0.00015327509,0.00000818506,0.00057794864,0.00018059158],"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":[0.000032367385,0.0023936476,0.0034464924,0.020623015,0.0010440524,0.0028299575,0.018142896,0.7766861,0.091531865,0.0005672197,0.043723855,0.03897856],"study_design_scores_gemma":[0.010350278,0.0027953298,0.017797286,0.1426821,0.0008555943,0.0009190119,0.0013420198,0.063955136,0.038837984,0.000021133985,0.7084194,0.01202475],"about_ca_topic_score_codex":0.000009128126,"about_ca_topic_score_gemma":0.000034180634,"teacher_disagreement_score":0.71273094,"about_ca_system_score_codex":0.00024610624,"about_ca_system_score_gemma":0.0000061948804,"threshold_uncertainty_score":0.9998477},"labels":[],"label_agreement":null},{"id":"W3145208339","doi":"10.3390/designs5020027","title":"Medium-Term Regional Electricity Load Forecasting through Machine Learning and Deep Learning","year":2021,"lang":"en","type":"article","venue":"Designs","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Computer science; Support vector machine; Mean absolute percentage error; Demand response; Random forest; Artificial intelligence; Electricity; Renewable energy; Term (time); Artificial neural network; Electrical load; Machine learning; Environmental economics; Engineering","score_opus":0.03787378881506236,"score_gpt":0.23559350022486183,"score_spread":0.19771971140979946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3145208339","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":[0.72966754,0.0263146,0.20130299,0.0001516725,0.0006436084,0.00016146577,0.000003948542,0.0014276252,0.04032657],"genre_scores_gemma":[0.9917388,0.0010104134,0.0061599463,0.00006214517,0.00025830732,0.000008614308,0.000044737357,0.000063318876,0.00065374444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987358,0.00008697729,0.00023420848,0.00027212335,0.00023378103,0.00043710312],"domain_scores_gemma":[0.9993992,0.0002867411,0.000051654137,0.00009246598,0.00007413699,0.00009579738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002356202,0.00021883534,0.00023048297,0.000048604885,0.00028064524,0.00007768787,0.00007994586,0.00011262745,0.000111851215],"category_scores_gemma":[0.0002516027,0.00023314358,0.00006403436,0.00030613385,0.000033289572,0.00018972118,0.000051107636,0.0006180516,0.000010221879],"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":[0.00009879517,0.00012179297,0.10278348,0.0008272579,0.0006613689,0.0016527333,0.015638245,0.37404823,0.21000974,0.00362593,0.000804613,0.2897278],"study_design_scores_gemma":[0.0010866312,0.00017197333,0.0019999184,0.00029319015,0.00008361026,0.0010015117,0.0003099358,0.89037454,0.03689022,0.0006785685,0.06613829,0.00097158924],"about_ca_topic_score_codex":0.000026867194,"about_ca_topic_score_gemma":0.00008133042,"teacher_disagreement_score":0.5163263,"about_ca_system_score_codex":0.000078682846,"about_ca_system_score_gemma":0.000041509193,"threshold_uncertainty_score":0.95073193},"labels":[],"label_agreement":null},{"id":"W3158005764","doi":"10.1088/1742-6596/1886/1/012014","title":"Regional differentiation of energy consumption in urban households","year":2021,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Energy Load and Power Forecasting","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":"Energy consumption; Consumption (sociology); Energy (signal processing); Energy demand; Quarter (Canadian coin); Economics; Business; Environmental economics; Natural resource economics; Geography; Engineering","score_opus":0.028987896643192512,"score_gpt":0.21673431666181614,"score_spread":0.18774642001862363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158005764","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":[0.9711567,0.0004885976,0.027146816,0.00003955914,0.00035268723,0.000009100027,0.000005102496,0.000014881124,0.00078656303],"genre_scores_gemma":[0.99873704,0.00064392004,0.00039537472,0.000009101009,0.0001479995,6.2936954e-7,0.000009290644,0.000010143057,0.000046490808],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993804,0.000025959818,0.0003032006,0.000049741455,0.0001473922,0.00009329602],"domain_scores_gemma":[0.9995833,0.000031695272,0.00013969748,0.00006907426,0.00014624986,0.000029982642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000049872397,0.00008180349,0.0001943228,0.000041435585,0.000014290617,0.000020543775,0.00006626363,0.0000431839,0.00003256152],"category_scores_gemma":[0.000010958921,0.00008091733,0.00006104152,0.000109101085,0.000033086548,0.00032144075,0.000012147698,0.000115945935,3.7782854e-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":[0.0001234973,0.0002581716,0.07512834,0.0004655157,0.00028678906,0.00008641478,0.004469327,0.054042984,0.43378836,0.33172008,0.0010326788,0.09859782],"study_design_scores_gemma":[0.00065024,0.00009574445,0.036582455,0.00060521055,0.000032899567,0.000070091904,0.0002581246,0.0031097978,0.94670725,0.010176418,0.0014869208,0.00022484359],"about_ca_topic_score_codex":0.0000040971063,"about_ca_topic_score_gemma":0.000037689235,"teacher_disagreement_score":0.5129189,"about_ca_system_score_codex":0.000023383496,"about_ca_system_score_gemma":0.00005296747,"threshold_uncertainty_score":0.3299713},"labels":[],"label_agreement":null},{"id":"W3159311622","doi":"10.1016/j.jtice.2021.04.048","title":"Economic dispatch optimization of SOFC/GT-based cogeneration systems using flexible fuel purchasing strategy","year":2021,"lang":"en","type":"article","venue":"Journal of the Taiwan Institute of Chemical Engineers","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Ministry of Science and Technology, Taiwan","keywords":"Cogeneration; Natural gas; Economic dispatch; Coal; Purchasing; Electricity generation; Computer science; Electric power system; Process engineering; Engineering; Environmental science; Power (physics); Waste management; Operations management","score_opus":0.014664616399371033,"score_gpt":0.21511404566047207,"score_spread":0.20044942926110104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159311622","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":[0.76079124,0.0009660652,0.23560089,0.00004462135,0.002037699,0.000055580356,0.000016329823,0.000025363013,0.0004622228],"genre_scores_gemma":[0.98842156,0.00002361894,0.011276722,0.0000052880227,0.00022926266,6.4366566e-7,0.000010131198,0.000024353656,0.000008430441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893415,0.00001760637,0.00063101633,0.00007727237,0.0001910739,0.00014885813],"domain_scores_gemma":[0.99933493,0.000025712992,0.0002795668,0.00015108529,0.00014126355,0.00006746328],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016500146,0.00013913163,0.0003133421,0.000091790534,0.00002466205,0.00002994648,0.00017640383,0.00010014285,0.000009666193],"category_scores_gemma":[0.00005905221,0.00011983173,0.00016757772,0.0001823911,0.00005652455,0.0002483237,0.000019476776,0.00018521276,1.7986085e-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":[0.000008707777,0.000012317211,0.00003607145,0.00018033187,0.000059878173,0.0000028058307,0.000041104577,0.76626915,0.23291904,0.00029211218,0.00002673139,0.0001517842],"study_design_scores_gemma":[0.00032690322,0.000011024993,0.0000028241334,0.00030735336,0.000048793674,0.000043944987,0.000032415217,0.66493225,0.33403426,0.000013071203,0.00016392682,0.00008320926],"about_ca_topic_score_codex":0.0000103394605,"about_ca_topic_score_gemma":0.0000017326605,"teacher_disagreement_score":0.22763033,"about_ca_system_score_codex":0.00018216397,"about_ca_system_score_gemma":0.00023051575,"threshold_uncertainty_score":0.48865962},"labels":[],"label_agreement":null},{"id":"W3159654789","doi":"10.18280/mmep.080206","title":"Forecasting Wind Speed Data by Using a Combination of ARIMA Model with Single Exponential Smoothing","year":2021,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"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":"Universiti Teknologi Malaysia","keywords":"Autoregressive integrated moving average; Exponential smoothing; Wind speed; Mean absolute percentage error; Mean squared error; Wind power; Statistics; Autoregressive model; Moving average; Exponential function; Mathematics; Econometrics; Meteorology; Time series; Engineering; Geography","score_opus":0.0698074477671799,"score_gpt":0.21259519149915973,"score_spread":0.14278774373197983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3159654789","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":[0.36914325,0.00035127578,0.62999684,0.000006874589,0.00005151711,0.000056539888,0.00001258678,0.00011465989,0.0002664308],"genre_scores_gemma":[0.8687563,0.000017752778,0.13103895,0.0000028072145,0.00002761913,0.0000010447111,0.000053568267,0.00006940716,0.000032588687],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987682,0.000008758963,0.00040193423,0.00027844956,0.00022086804,0.00032175664],"domain_scores_gemma":[0.99938583,0.00009866544,0.000056936824,0.00030899997,0.000055402867,0.000094152776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022059646,0.00023109169,0.000329175,0.00007186005,0.00006394909,0.00008364706,0.00014277449,0.000098729564,0.000004611099],"category_scores_gemma":[0.00003451863,0.00022570911,0.000030971707,0.00017395426,0.00002933555,0.00028626848,0.00009344172,0.00020892541,3.7373823e-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":[0.0000032901683,0.00005145514,0.000004971823,0.0009137411,0.000048576778,0.000004254577,0.00052528625,0.96317583,0.03439893,0.00057159434,0.000007768171,0.00029427893],"study_design_scores_gemma":[0.0003397213,0.00002440462,9.979641e-8,0.0009967831,0.000055387325,0.00006105684,0.000034225377,0.98890716,0.008137675,0.0011575036,0.000024655792,0.00026130024],"about_ca_topic_score_codex":0.000007902364,"about_ca_topic_score_gemma":6.217102e-7,"teacher_disagreement_score":0.49961302,"about_ca_system_score_codex":0.000029212344,"about_ca_system_score_gemma":0.000016396789,"threshold_uncertainty_score":0.92041504},"labels":[],"label_agreement":null},{"id":"W3160946652","doi":"10.3389/fceng.2021.665415","title":"Machine Learning and Metaheuristic Methods for Renewable Power Forecasting: A Recent Review","year":2021,"lang":"en","type":"review","venue":"Frontiers in Chemical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":64,"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":"Renewable energy; Wind power; Computer science; Artificial neural network; Metaheuristic; Support vector machine; Machine learning; Artificial intelligence; Wind power forecasting; Electric power system; Power (physics); Engineering","score_opus":0.03211926684735253,"score_gpt":0.3023493626802353,"score_spread":0.2702300958328828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3160946652","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[1.326531e-7,0.8511417,0.14653277,0.0000061170226,0.0011751655,0.00043059644,0.000018993433,0.00022876983,0.00046577552],"genre_scores_gemma":[3.7577266e-7,0.7155985,0.28358436,0.000009638892,0.00009736696,0.00024372486,0.00020330341,0.00017979836,0.000082881335],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976016,0.000077837736,0.0009560138,0.00056631205,0.00013120094,0.0006670322],"domain_scores_gemma":[0.9987843,0.0005854635,0.0001268812,0.00026347875,0.000045361394,0.00019453559],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00078266236,0.0007101985,0.0027212072,0.0002615951,0.00003723319,0.000050023482,0.00024068658,0.00039902815,0.000026917736],"category_scores_gemma":[0.0017279834,0.00070928497,0.00040242492,0.00069590064,0.000022730781,0.000075875025,0.00012419878,0.00099761,5.585228e-7],"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":[0.0000012223596,0.000008192207,9.161739e-7,0.065547965,0.00024128775,0.000015660311,0.000017319513,0.005379909,0.000016052836,0.000018324577,0.0014708724,0.9272823],"study_design_scores_gemma":[0.00013821111,0.000010353004,4.903079e-9,0.047794364,0.0004228083,0.000073511466,0.0000020300774,0.121848315,0.00005552786,0.000016633081,0.82906365,0.00057459075],"about_ca_topic_score_codex":0.000002718204,"about_ca_topic_score_gemma":2.1665818e-7,"teacher_disagreement_score":0.9267077,"about_ca_system_score_codex":0.00026532775,"about_ca_system_score_gemma":0.000049405702,"threshold_uncertainty_score":0.9995358},"labels":[],"label_agreement":null},{"id":"W3161067985","doi":"10.14288/1.0397471","title":"Short term electric load forecasting for British Columbia, Canada: an exploration of the use of numerical weather prediction data as a predictor in an artificial neural network","year":2021,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial neural network; Term (time); Meteorology; Numerical weather prediction; Weather forecasting; Computer science; Climatology; Environmental science; Artificial intelligence; Machine learning; Geography; Geology","score_opus":0.04610923476527818,"score_gpt":0.19311476749389117,"score_spread":0.147005532728613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3161067985","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":[0.9961456,0.00008762252,0.0022422923,0.000006363876,0.0003366418,0.00025685912,0.00084050535,0.000042537722,0.00004158861],"genre_scores_gemma":[0.9989197,0.000022970438,0.0005561548,0.000008322742,0.00011310028,0.0000020312596,0.00029597752,0.000025914469,0.00005579561],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9987629,0.00007502993,0.00028324447,0.00032162303,0.0002824955,0.00027468926],"domain_scores_gemma":[0.9991563,0.00006770666,0.00010107578,0.00037310016,0.00021763862,0.00008419295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016556286,0.00005174822,0.00027005764,0.0000171932,0.00015182115,0.0001282667,0.00032392863,0.00010408451,0.000020586616],"category_scores_gemma":[0.00009220379,0.00017228739,0.00006124002,0.00048995356,0.00005772994,0.0012920537,0.000102767866,0.00014871122,4.604366e-8],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000027762686,0.00021665945,0.33690295,0.00021281702,0.000089528214,0.000111971494,0.00042901628,0.08832801,0.0016416507,0.000001200817,0.0028535386,0.5691849],"study_design_scores_gemma":[0.0002816018,0.000084457264,0.6626087,0.0002254481,0.000044553697,0.000045376633,0.000275204,0.3361571,0.00000780651,0.000043939497,0.00010576672,0.00012004772],"about_ca_topic_score_codex":0.82410026,"about_ca_topic_score_gemma":0.9980784,"teacher_disagreement_score":0.56906486,"about_ca_system_score_codex":0.00014790204,"about_ca_system_score_gemma":0.00034342014,"threshold_uncertainty_score":0.7025676},"labels":[],"label_agreement":null},{"id":"W3162962255","doi":"10.1016/j.egyai.2021.100087","title":"A Conditional Generative adversarial Network for energy use in multiple buildings using scarce data","year":2021,"lang":"en","type":"article","venue":"Energy and AI","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":59,"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 Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Canarie","keywords":"Computer science; USable; Set (abstract data type); Machine learning; Data set; Divergence (linguistics); Generative grammar; Data mining; Grid; Artificial intelligence; Deep learning","score_opus":0.04282596453987297,"score_gpt":0.24344802854197073,"score_spread":0.20062206400209776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162962255","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":[0.26718575,0.0024213553,0.7278065,0.0000839041,0.0014322703,0.00005040673,0.00046598612,0.0001296953,0.00042413297],"genre_scores_gemma":[0.9678911,0.00011584503,0.028819341,0.00045699076,0.0011011354,0.000012391947,0.0013884964,0.000032403514,0.00018232333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991889,0.000030150468,0.00017555109,0.00026992874,0.000073558804,0.00026191742],"domain_scores_gemma":[0.9994997,0.00020542904,0.000023294497,0.00017582717,0.000034816723,0.0000609781],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008015812,0.00013857336,0.00016353672,0.00004292871,0.00012852986,0.00006451421,0.00009473103,0.00009074706,0.000020584537],"category_scores_gemma":[0.00005638147,0.00015282248,0.000031582214,0.00015123263,0.000031428648,0.0003615199,0.00009893233,0.00007072168,1.2340551e-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":[0.000028008057,0.000016920694,0.0012439088,0.000013933841,0.000069593385,0.00003144852,0.00006518337,0.942713,0.008587911,0.041723534,0.0029367844,0.0025697295],"study_design_scores_gemma":[0.00083798775,0.000011212601,0.00012816531,0.0000681668,0.00001621223,0.000022261263,0.00001790618,0.86222225,0.010648756,0.0017683348,0.12403212,0.00022662914],"about_ca_topic_score_codex":0.00029843982,"about_ca_topic_score_gemma":0.0019693095,"teacher_disagreement_score":0.70070535,"about_ca_system_score_codex":0.000025367726,"about_ca_system_score_gemma":0.000040425588,"threshold_uncertainty_score":0.62319195},"labels":[],"label_agreement":null},{"id":"W3164104738","doi":"10.1155/2021/5573650","title":"A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":18,"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":"Ministry of Science and Technology of the People's Republic of China; Energy Foundation","keywords":"Coal; Index (typography); Computer science; China; Econometrics; Deep learning; Work (physics); Scale (ratio); Environmental science; Operations research; Environmental economics; Artificial intelligence; Economics; Engineering; Geography","score_opus":0.0069292907271656275,"score_gpt":0.201418518093974,"score_spread":0.19448922736680838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164104738","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":[0.46636415,0.0001838485,0.5327524,0.000009428648,0.00035031242,0.000048941147,0.000027209378,0.00004467682,0.00021902776],"genre_scores_gemma":[0.9736253,0.000054513475,0.025588043,0.000010275191,0.00023490492,0.000008278238,0.00039952734,0.000035190547,0.000043945573],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990595,0.000015692634,0.00043279195,0.000109142886,0.00020884673,0.00017405671],"domain_scores_gemma":[0.9995035,0.00004343264,0.0001650411,0.000057504116,0.00015656107,0.000073994976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013015569,0.00013038867,0.00020621489,0.00009496758,0.000079932026,0.000021089318,0.0000517296,0.00005424691,0.000010792932],"category_scores_gemma":[0.000023205137,0.00013430727,0.00014427019,0.00010960829,0.000011465646,0.00032570784,7.8576585e-7,0.00028861067,2.1145159e-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":[0.00011589299,0.00003901569,0.0015713436,0.00013245617,0.000044817276,0.00002811415,0.00042813248,0.9686016,0.024483128,0.00005407847,0.000027026603,0.004474413],"study_design_scores_gemma":[0.0064603365,0.00059787073,0.06152668,0.00044319071,0.00020761999,0.0001606995,0.0005844111,0.7275706,0.19279519,0.00052084523,0.008640635,0.0004919334],"about_ca_topic_score_codex":0.0000017580835,"about_ca_topic_score_gemma":0.000015986518,"teacher_disagreement_score":0.50726116,"about_ca_system_score_codex":0.000035509398,"about_ca_system_score_gemma":0.000058450238,"threshold_uncertainty_score":0.54768914},"labels":[],"label_agreement":null},{"id":"W3166673518","doi":"10.37099/mtu.dc.etdr/1206","title":"Prediction of coincident peak days in electricity system: a case study for classification on imbalanced data","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Energy Load and Power Forecasting","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":"Electricity; Consumption (sociology); Mains electricity; Computer science; Predictive modelling; Econometrics; Operations research; Environmental economics; Engineering; Economics; Machine learning; Electrical engineering; Sociology","score_opus":0.0591271970426548,"score_gpt":0.2843964889726318,"score_spread":0.22526929192997702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3166673518","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":[0.9884829,0.0002105203,0.0021191493,0.0000011233932,0.0010415276,0.0008631314,0.0002675643,0.00018405974,0.006830014],"genre_scores_gemma":[0.9915756,0.000027187034,0.00014377547,0.0000012404781,0.000105681225,0.00016375337,0.0075801215,0.00004445336,0.00035817272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986677,0.000042894517,0.0005639503,0.00035880765,0.00019898589,0.00016761884],"domain_scores_gemma":[0.9990625,0.000112987844,0.00014114904,0.0005456402,0.00010546402,0.000032270484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003273522,0.00020119837,0.00034614716,0.00021694621,0.0000419139,0.000025156283,0.00018342727,0.00017250227,0.000005822925],"category_scores_gemma":[0.00007799135,0.0002066562,0.0000442812,0.00029383105,0.0000028804577,0.00009836049,0.000014126416,0.00020333115,8.36891e-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":[0.0031766938,0.010112971,0.056093775,0.08066239,0.005718935,0.0070372852,0.06792768,0.34468743,0.2518895,0.014555572,0.020728042,0.13740972],"study_design_scores_gemma":[0.0013696625,0.00036840036,0.0127014965,0.0011309785,0.00022046655,0.00013211693,0.034740705,0.9444316,0.004393521,0.0000044998933,0.00013560306,0.00037094418],"about_ca_topic_score_codex":0.00066218094,"about_ca_topic_score_gemma":0.0094479015,"teacher_disagreement_score":0.5997442,"about_ca_system_score_codex":0.00018898213,"about_ca_system_score_gemma":0.000058733323,"threshold_uncertainty_score":0.8427195},"labels":[],"label_agreement":null},{"id":"W3170585989","doi":"10.71781/10026","title":"European day-ahead electricity price forecasting","year":2020,"lang":"en","type":"dissertation","venue":"Open MIND","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Mitacs","keywords":"Electricity price forecasting; Electricity price; Electricity; Economics; Econometrics; Financial economics; Engineering; Electrical engineering","score_opus":0.036470462367999106,"score_gpt":0.25507839163031387,"score_spread":0.21860792926231476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170585989","genre_codex":"other","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":[0.112383164,0.00037952067,0.00032773986,0.00000841812,0.0008695051,0.00024069558,0.000020168854,0.00003241693,0.8857384],"genre_scores_gemma":[0.96757215,0.000079383855,0.007845202,0.000035597917,0.0009598692,0.000021467034,0.002703912,0.0003281447,0.020454243],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998621,0.00005919353,0.00038341523,0.00038242148,0.00017855868,0.00037541872],"domain_scores_gemma":[0.9994109,0.00007181899,0.0001233243,0.00021142187,0.000043468044,0.00013901622],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002958268,0.00034297703,0.00034610348,0.000087416156,0.00012091652,0.00029265214,0.00066920544,0.00014753576,0.00073508575],"category_scores_gemma":[0.00011526868,0.00037255447,0.00008602006,0.0003490176,0.0000065965987,0.00019734267,0.00007303723,0.0005600832,0.0006057727],"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":[0.000034721474,0.00002418788,0.000047687157,0.0002162429,0.00016960308,0.00020251708,0.0051822388,0.003897473,0.006019565,0.000035340592,0.0031123902,0.98105806],"study_design_scores_gemma":[0.0006126486,0.00013255164,0.00046928274,0.0010583872,0.00016933514,0.00003071318,0.0004934173,0.075276725,0.052498475,0.000037634778,0.8676152,0.0016056239],"about_ca_topic_score_codex":0.000016465367,"about_ca_topic_score_gemma":0.0001372078,"teacher_disagreement_score":0.97945243,"about_ca_system_score_codex":0.000057860434,"about_ca_system_score_gemma":0.00005349414,"threshold_uncertainty_score":0.9998726},"labels":[],"label_agreement":null},{"id":"W3170635306","doi":"10.1109/tste.2021.3087018","title":"A Multilevel Modeling Approach Towards Wind Farm Aggregated Power Curve","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Sustainable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Manitoba Hydro; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Manitoba Hydro; University of Manitoba","keywords":"Wind power; Cluster analysis; Wind power forecasting; Turbine; Power (physics); Hierarchical clustering; Set (abstract data type); Computer science; Mathematics; Algorithm; Mathematical optimization; Electric power system; Statistics; Engineering; Electrical engineering; Aerospace engineering","score_opus":0.014421495084025886,"score_gpt":0.21038555539247125,"score_spread":0.19596406030844538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170635306","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":[0.027938299,0.00056361296,0.9329383,0.00002304345,0.00065596914,0.000068489615,0.00002052047,0.0005875671,0.037204217],"genre_scores_gemma":[0.9842544,0.00016293032,0.001996246,0.00009030737,0.000073316965,0.00005061512,0.00002613116,0.000116656454,0.013229365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809843,0.00005079893,0.00035799382,0.0004285754,0.00028439416,0.0007798037],"domain_scores_gemma":[0.9990534,0.000042434214,0.000031226893,0.0004133087,0.0002710812,0.00018851717],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012704662,0.00036838948,0.00031850406,0.00026289816,0.0002910943,0.00011031494,0.00018105176,0.00023474342,0.00018682284],"category_scores_gemma":[0.00001053751,0.00039821808,0.00020250714,0.00066980364,0.000032468255,0.0002388921,0.000003832005,0.0003992297,0.0000075636917],"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":[0.00002000602,0.00013450696,5.35077e-7,0.00007871616,0.00012809252,0.0001483638,0.00045594637,0.9772034,0.0009178161,0.0014516565,0.00005175733,0.019409196],"study_design_scores_gemma":[0.00065001263,0.000039164268,0.0000015228741,0.00005461745,0.0000394801,0.00006574073,0.001934081,0.9375409,0.04901683,0.00024861484,0.009911721,0.0004973458],"about_ca_topic_score_codex":0.0005127944,"about_ca_topic_score_gemma":0.000062253705,"teacher_disagreement_score":0.9563161,"about_ca_system_score_codex":0.00026343993,"about_ca_system_score_gemma":0.00016296598,"threshold_uncertainty_score":0.999847},"labels":[],"label_agreement":null},{"id":"W3172104835","doi":"10.5194/egusphere-egu21-1449","title":"Time scale dependence of wind speed patterns - implications for wind power site assessment","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Saint Mary's University","funders":"","keywords":"Wind speed; Wind power; Environmental science; Meteorology; Statistics; Interval (graph theory); Scale (ratio); Detrended fluctuation analysis; Mathematics; Engineering; Geography; Scaling; Cartography","score_opus":0.012865139442360023,"score_gpt":0.25251159944434,"score_spread":0.23964646000197995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172104835","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":[0.9456498,0.000055987468,0.020334851,0.0001335048,0.00018806786,0.000094977324,0.00013315139,0.000096630545,0.033313032],"genre_scores_gemma":[0.99266094,0.000007499494,0.005169593,0.00004069741,0.000040687464,0.0000016500748,0.00007349023,0.000021589964,0.001983826],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994267,0.000006978994,0.00018627547,0.00013622969,0.00007325321,0.00017055598],"domain_scores_gemma":[0.9995636,0.00007369261,0.000024541601,0.00021334847,0.00007116309,0.000053676784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006522194,0.00009166108,0.00012853471,0.00003062039,0.000036061618,0.00001965385,0.00007991747,0.000048577058,0.0006156387],"category_scores_gemma":[0.000008449373,0.00009235411,0.0000652024,0.000098975535,0.000011783325,0.000088412875,0.000030162733,0.000059753114,0.000016113077],"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":[0.0000055395885,0.00014217994,0.092315145,0.00019816268,0.00017852477,0.000005903477,0.000629386,0.14244041,0.7542001,0.0025268765,0.0032565212,0.004101263],"study_design_scores_gemma":[0.0017853098,0.00018477615,0.4581211,0.00036575945,0.00014677766,0.00008994847,0.00039887291,0.14848948,0.37030607,0.0009837935,0.01791617,0.0012119377],"about_ca_topic_score_codex":0.000009525918,"about_ca_topic_score_gemma":0.000036013684,"teacher_disagreement_score":0.383894,"about_ca_system_score_codex":0.000024190238,"about_ca_system_score_gemma":0.000026264643,"threshold_uncertainty_score":0.6740812},"labels":[],"label_agreement":null},{"id":"W3172913032","doi":"10.5539/ep.v10n2p33","title":"Accuracy of Local Knowledge in Prediction Seasonal Weather: Empirical Evidence from North eastern Nigeria","year":2021,"lang":"en","type":"article","venue":"Environment and Pollution","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Environmental science; Climatology; Dry season; Latitude; Wet season; Seasonality; Population; Maximum temperature; Cold weather; Geography; Atmospheric sciences; Biology; Demography; Ecology; Geology; Cartography","score_opus":0.023302611315378405,"score_gpt":0.2267671683402305,"score_spread":0.2034645570248521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3172913032","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":[0.9829622,0.0060833027,0.010455032,0.000036242105,0.0001557554,0.000030755156,0.000029525785,0.000022883336,0.0002243405],"genre_scores_gemma":[0.9984322,0.0011595691,0.00016337863,0.000008294738,0.00010299227,0.0000042707265,0.00003793556,0.0000087545595,0.00008258237],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9994807,0.000036592857,0.00015704126,0.00012898027,0.00008641258,0.00011022777],"domain_scores_gemma":[0.9997987,0.000048294296,0.000022100587,0.000085199244,0.0000027958465,0.00004289877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000054008924,0.00008563936,0.00010038583,0.000024950337,0.000021931159,0.0000075002586,0.000031230124,0.000057481426,0.00011191573],"category_scores_gemma":[0.000008433304,0.0000885003,0.00002609731,0.000061005037,0.00003393957,0.00012860763,0.000029595409,0.00008739266,0.00001454407],"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":[0.000024067844,0.000054222503,0.8571798,0.00003647868,0.000025135962,0.0000070574442,0.0017564655,0.048736047,0.010217397,0.000010126757,0.0001053426,0.08184785],"study_design_scores_gemma":[0.00030820756,0.000032727137,0.88968575,0.00020319136,0.000013483452,0.0000035860865,0.00014929067,0.09825517,0.0066628163,0.000027404218,0.0045364858,0.00012186855],"about_ca_topic_score_codex":0.00002040803,"about_ca_topic_score_gemma":0.00014003739,"teacher_disagreement_score":0.08172598,"about_ca_system_score_codex":0.00005682162,"about_ca_system_score_gemma":0.000011107117,"threshold_uncertainty_score":0.36089376},"labels":[],"label_agreement":null},{"id":"W3173957380","doi":"10.1093/jrsssc/qlac006","title":"Aggregated functional data model applied on clustering and disaggregation of UK electrical load profiles","year":2023,"lang":"en","type":"article","venue":"Journal of the Royal Statistical Society Series C (Applied Statistics)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Cluster analysis; Similarity (geometry); Computer science; Covariance; Electrical load; Data mining; Electric potential energy; Consumption (sociology); Energy (signal processing); Statistics; Mathematics; Engineering; Artificial intelligence; Voltage; Electrical engineering","score_opus":0.021163486472465232,"score_gpt":0.22504290968525384,"score_spread":0.2038794232127886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3173957380","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":[0.024005944,0.000101313184,0.9712361,0.000102477636,0.00048807767,0.00023181128,0.0020988144,0.000105804196,0.0016296377],"genre_scores_gemma":[0.93781775,0.00013442092,0.06138772,0.00005368886,0.00019283436,0.000008557735,0.00019507257,0.000051692125,0.00015827677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981263,0.000025740072,0.00063853594,0.00020961056,0.00066734565,0.0003324622],"domain_scores_gemma":[0.9986775,0.0005619599,0.0002612336,0.00026062,0.00010751037,0.00013118914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042737072,0.0002335718,0.00037484244,0.00003752442,0.00019517797,0.000058654932,0.0003236591,0.000117552765,0.00004365883],"category_scores_gemma":[0.0002105074,0.00017647407,0.00006176306,0.00027831507,0.00021558416,0.000069397414,0.00019699892,0.0004758823,0.000004565732],"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":[0.00039270186,0.00004879577,0.00008383739,0.00026315023,0.00030059167,0.0000070985593,0.0005801304,0.8389432,0.002471474,0.09564214,0.040701512,0.02056539],"study_design_scores_gemma":[0.00062737043,0.000089316236,0.0017735735,0.000079058445,0.00010299919,0.000010572318,0.00014257249,0.98523057,0.00065143965,0.01064847,0.00043195146,0.00021208254],"about_ca_topic_score_codex":0.0000061891565,"about_ca_topic_score_gemma":0.000009783812,"teacher_disagreement_score":0.9138118,"about_ca_system_score_codex":0.00015442095,"about_ca_system_score_gemma":0.00010310291,"threshold_uncertainty_score":0.7196404},"labels":[],"label_agreement":null},{"id":"W3174813448","doi":"10.1109/naps50074.2021.9449743","title":"Using Prophet Algorithm for Pattern Recognition and Short Term Forecasting of Load Demand Based on Seasonality and Exogenous Features","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"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":"Ontario Innovation Trust","keywords":"Mean absolute percentage error; Computer science; Electrical load; Seasonality; Term (time); Smart meter; Electric power system; Data mining; Electricity; Econometrics; Artificial intelligence; Engineering; Machine learning; Power (physics); Artificial neural network; Voltage; Mathematics","score_opus":0.05721731279507572,"score_gpt":0.24648111738227568,"score_spread":0.18926380458719996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174813448","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":[0.8621784,0.0004081395,0.13621663,0.000008409553,0.00009751835,0.00013990208,0.00010043669,0.000053639327,0.00079696387],"genre_scores_gemma":[0.9568237,0.00001661048,0.042918146,0.00004128311,0.00008888858,0.000010128978,0.00006072901,0.000025356669,0.000015135494],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993419,0.000018476707,0.00016181466,0.00018747893,0.0001117157,0.0001786345],"domain_scores_gemma":[0.9996309,0.00011492113,0.000023633285,0.000082233564,0.00008879887,0.00005951075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016060408,0.00013613101,0.00017307163,0.000024825888,0.00007173152,0.000036064746,0.000024092975,0.00007260366,0.000012370527],"category_scores_gemma":[0.000032637556,0.00012799464,0.000038259957,0.000063275234,0.00002277498,0.00006678958,0.000018159983,0.00007518073,9.26313e-8],"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":[0.000015773083,0.000035700672,0.005743538,0.0006272069,0.000053733358,0.000025092262,0.00022193741,0.006815107,0.010242279,0.0000027122483,0.000020554035,0.97619635],"study_design_scores_gemma":[0.0004347466,0.000070502385,0.0020336031,0.00032657373,0.000046006655,0.000083019404,0.00004853285,0.93091506,0.065712035,0.00009808926,0.000030366464,0.00020144004],"about_ca_topic_score_codex":0.000022792985,"about_ca_topic_score_gemma":0.000062481675,"teacher_disagreement_score":0.97599494,"about_ca_system_score_codex":0.000027406344,"about_ca_system_score_gemma":0.000026429849,"threshold_uncertainty_score":0.521947},"labels":[],"label_agreement":null},{"id":"W3175763617","doi":"10.1016/j.epsr.2021.107436","title":"Energy management of hybrid energy system sources based on machine learning classification algorithms","year":2021,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Algorithm; Computer science; Naive Bayes classifier; Decision tree; Gaussian; Machine learning; Random forest; Energy (signal processing); Statistical classification; Renewable energy; Artificial intelligence; Energy management; Data mining; Support vector machine; Engineering; Mathematics; Statistics","score_opus":0.02691848212310659,"score_gpt":0.2570791681536534,"score_spread":0.2301606860305468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175763617","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":[0.06129526,0.047906835,0.51786286,0.00010098208,0.0032387169,0.0005250016,0.000054033782,0.0017115499,0.36730474],"genre_scores_gemma":[0.99539435,0.0003291434,0.0001273075,0.000005622566,0.00010647796,0.00010512291,0.00008710716,0.0000799389,0.0037649462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996603,0.0005777849,0.00052478653,0.00043105584,0.0011325399,0.00073081494],"domain_scores_gemma":[0.9986606,0.00031094858,0.00008778813,0.0004986491,0.00029137515,0.0001506354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011040351,0.00024064786,0.0003609872,0.0006622897,0.00021539498,0.00011115834,0.00032645426,0.00009401414,0.000029382689],"category_scores_gemma":[0.000030464944,0.00023855106,0.00010570619,0.0015730202,0.00002855736,0.00007064738,0.000060061757,0.00044013985,0.00001689835],"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":[0.00014189677,0.0003810796,0.0007678555,0.0037952526,0.0009452547,0.0015511353,0.00025817475,0.7882777,0.03431204,0.094004676,0.004453498,0.07111145],"study_design_scores_gemma":[0.0003375637,0.00014258354,0.000053335843,0.00056723703,0.000011602565,0.00004789365,0.00022353865,0.90825003,0.039118685,0.0000070020583,0.05102751,0.00021303371],"about_ca_topic_score_codex":0.000312781,"about_ca_topic_score_gemma":0.000007342084,"teacher_disagreement_score":0.9340991,"about_ca_system_score_codex":0.0003751143,"about_ca_system_score_gemma":0.00007744153,"threshold_uncertainty_score":0.97278297},"labels":[],"label_agreement":null},{"id":"W3176151014","doi":"10.3390/jrfm14070294","title":"Forecasting Volatility and Tail Risk in Electricity Markets","year":2021,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"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":"Volatility (finance); Econometrics; Estimator; Expected shortfall; Autoregressive conditional heteroskedasticity; Economics; Jump; Electricity; Value at risk; Realized variance; Electricity market; Electricity price; Statistics; Risk management; Mathematics; Engineering; Finance","score_opus":0.0066789797081171255,"score_gpt":0.18205025723335053,"score_spread":0.1753712775252334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176151014","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":[0.98225415,0.0043361704,0.0113450615,0.000007080146,0.00023235127,0.000034341065,0.000004452235,0.000009727532,0.0017766567],"genre_scores_gemma":[0.9881069,0.009057156,0.0026844437,0.000011136697,0.00010524148,9.252581e-7,5.426252e-7,0.00000827318,0.000025370833],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99926174,0.000045433833,0.00031601393,0.000096236625,0.000106817875,0.00017376973],"domain_scores_gemma":[0.99966365,0.00007966817,0.00010531609,0.000060900078,0.000035273442,0.000055202825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005527835,0.00009953995,0.00019328056,0.00012049757,0.00006865391,0.000033611843,0.00004444402,0.000045246758,0.000006849056],"category_scores_gemma":[0.00017611716,0.0000947649,0.000041601754,0.00022535319,0.000013859069,0.00011568188,0.000044965484,0.00029663104,1.8196525e-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":[0.000045078967,0.000025343877,0.1503068,0.00010046702,0.00002166086,0.00026315064,0.0003489167,0.00178362,0.000014343169,0.00022781131,0.0001624621,0.8467003],"study_design_scores_gemma":[0.001406813,0.000078856,0.8466586,0.00027941322,0.000105709354,0.00010684154,0.0001732244,0.10974026,0.00026973212,0.0042681913,0.036649976,0.00026238954],"about_ca_topic_score_codex":0.000018043185,"about_ca_topic_score_gemma":0.00012161001,"teacher_disagreement_score":0.84643793,"about_ca_system_score_codex":0.00003154317,"about_ca_system_score_gemma":0.000010038295,"threshold_uncertainty_score":0.38644004},"labels":[],"label_agreement":null},{"id":"W3176181793","doi":"10.1109/tsg.2021.3093515","title":"Spatial-Temporal Residential Short-Term Load Forecasting via Graph Neural Networks","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":200,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Term (time); Margin (machine learning); Artificial neural network; Electrical load; Geographic information system; Graph; Data mining; Machine learning; Engineering; Voltage; Geography","score_opus":0.01875905799342236,"score_gpt":0.21625814752514416,"score_spread":0.1974990895317218,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176181793","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":[0.17136179,0.00021467576,0.81701714,0.000031581923,0.009290002,0.00009963877,0.000030456495,0.0004962967,0.0014584323],"genre_scores_gemma":[0.9979986,0.00006115079,0.00055086316,0.000050316965,0.00094955275,0.00003240003,0.000040938467,0.00008775039,0.00022845632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982672,0.000056822722,0.00044663178,0.00036502563,0.0003206822,0.00054364523],"domain_scores_gemma":[0.9992485,0.00009482822,0.000035199446,0.00034846584,0.00009214878,0.00018085203],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013496405,0.00032860038,0.00029292403,0.00013323297,0.0003030285,0.00010916253,0.0001547094,0.00017890078,0.00022449363],"category_scores_gemma":[0.000004200351,0.00036840924,0.00027969174,0.00042347604,0.000047810627,0.00024416373,0.0000030061697,0.000595395,0.000017003435],"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":[0.0000354587,0.00006296291,0.00066108553,0.00004300537,0.00011497284,0.00022595977,0.00014050816,0.9489908,0.0021643136,0.0000027622764,0.0005876204,0.046970546],"study_design_scores_gemma":[0.00051843293,0.00010258113,0.00047128694,0.0001387079,0.0001076407,0.00026862504,0.000029655248,0.95316887,0.04310061,0.000027556018,0.001454955,0.000611076],"about_ca_topic_score_codex":0.00026471322,"about_ca_topic_score_gemma":0.0042369096,"teacher_disagreement_score":0.8266368,"about_ca_system_score_codex":0.00009042145,"about_ca_system_score_gemma":0.000038742804,"threshold_uncertainty_score":0.9998768},"labels":[],"label_agreement":null},{"id":"W3176582181","doi":"10.18280/mmep.080313","title":"Modeling Wind Speed with a Long-Term Horizon and High-Time Interval with a Hybrid Fourier-Neural Network Model","year":2021,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"Universidad Militar Nueva Granada","keywords":"Autoregressive model; Moment (physics); Interval (graph theory); Term (time); Time horizon; Fourier transform; Horizon; Wind speed; Nonlinear system; Computer science; Fourier series; Artificial neural network; Fourier analysis; Scale (ratio); Meteorology; Control theory (sociology); Econometrics; Mathematics; Mathematical optimization; Physics; Artificial intelligence; Control (management); Mathematical analysis","score_opus":0.014714391304070026,"score_gpt":0.18030225336008213,"score_spread":0.16558786205601211,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176582181","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":[0.4860057,0.00043702722,0.5131102,0.000013694673,0.000034193905,0.00007379961,0.0000037674688,0.00023369868,0.00008789147],"genre_scores_gemma":[0.9056582,0.00007449896,0.093902595,0.000006993154,0.000111510526,0.0000073038445,0.000014636669,0.00011968882,0.000104569546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852103,0.000009959994,0.0003314322,0.00036548002,0.00021411822,0.0005579768],"domain_scores_gemma":[0.99940753,0.00006627612,0.00002534722,0.0002284179,0.000052183288,0.00022025147],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001598987,0.00039238326,0.00047033685,0.000063132095,0.000091483875,0.00015220161,0.00008790281,0.00008909756,0.000006757664],"category_scores_gemma":[0.000007420189,0.00032142573,0.000042408174,0.00014629932,0.000035576788,0.00020280335,0.000060859566,0.00036192304,0.000002460342],"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":[0.00001726495,0.000019228317,0.000017930406,0.0007951163,0.00010741891,0.000056435816,0.00033932645,0.99711204,0.00021944419,0.00079266395,0.000003452469,0.00051970757],"study_design_scores_gemma":[0.0004590431,0.00011106472,0.0000015190022,0.0015216541,0.000085896354,0.00037023085,0.000009103849,0.99520576,0.00016140577,0.0016157738,0.0000026740329,0.00045584913],"about_ca_topic_score_codex":0.000003676478,"about_ca_topic_score_gemma":0.0000017567555,"teacher_disagreement_score":0.41965252,"about_ca_system_score_codex":0.000025124595,"about_ca_system_score_gemma":0.000014954209,"threshold_uncertainty_score":0.99992377},"labels":[],"label_agreement":null},{"id":"W3176805851","doi":"10.1109/icc42927.2021.9500767","title":"Short-Term Load Forecasting for Smart Home Appliances with Sequence to Sequence Learning","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Ottawa","funders":"","keywords":"Sequence (biology); Term (time); Computer science; Convolutional neural network; Artificial intelligence; Recurrent neural network; Time sequence; Deep learning; Energy (signal processing); Long short term memory; Machine learning; Sequence learning; Artificial neural network; Statistics","score_opus":0.06227825128187564,"score_gpt":0.26556563743358286,"score_spread":0.20328738615170722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3176805851","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":[0.7167485,0.0006776711,0.2634689,0.000049321006,0.0012946816,0.00081057573,0.000052344763,0.0013062438,0.015591784],"genre_scores_gemma":[0.9345547,0.00007795543,0.063117914,0.000051824416,0.00037713477,0.00051979977,0.00018390853,0.00015419182,0.00096258445],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974511,0.00002460681,0.0004850426,0.00084606954,0.00039321507,0.0007999554],"domain_scores_gemma":[0.9987798,0.00018292996,0.000081127735,0.00046177887,0.000246878,0.00024753663],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033883838,0.0006240486,0.0006375922,0.00012091957,0.00018960988,0.00037067445,0.00048667734,0.00030182008,0.00004251158],"category_scores_gemma":[0.00007708661,0.0005873574,0.0001668604,0.00028762722,0.000052647625,0.00020240732,0.00035469202,0.0009073757,0.000010253726],"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":[0.000022193646,0.000009970603,0.005063955,0.0013407962,0.00014772847,0.00009535458,0.0010068078,0.942917,0.005464876,0.00013302098,0.000074397125,0.04372391],"study_design_scores_gemma":[0.0007814346,0.0004153165,0.0007339771,0.01017126,0.00024762744,0.00038885835,0.0013889925,0.93584216,0.0326862,0.00033961187,0.012225924,0.0047786604],"about_ca_topic_score_codex":0.00009891003,"about_ca_topic_score_gemma":0.00050143484,"teacher_disagreement_score":0.21780619,"about_ca_system_score_codex":0.0003221246,"about_ca_system_score_gemma":0.00019579321,"threshold_uncertainty_score":0.9996578},"labels":[],"label_agreement":null},{"id":"W3177942030","doi":"10.1016/j.ijforecast.2021.05.013","title":"Spatio-temporal probabilistic forecasting of wind power for multiple farms: A copula-based hybrid model","year":2021,"lang":"en","type":"article","venue":"International Journal of Forecasting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Copula (linguistics); Probabilistic forecasting; Probabilistic logic; Wind power; Residual; Replicate; Computer science; Econometrics; Grid; Wind speed; Statistics; Mathematics; Meteorology; Artificial intelligence; Engineering; Algorithm","score_opus":0.037746014309384984,"score_gpt":0.24738695085286183,"score_spread":0.20964093654347685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177942030","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":[0.81614125,0.00026886386,0.180695,0.00007191221,0.0014998339,0.00012801992,0.00014290212,0.000040763756,0.0010114785],"genre_scores_gemma":[0.9302051,0.000004131824,0.069215044,0.00004407011,0.00035128935,0.0000060165507,0.000077392055,0.00005888524,0.00003804977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99780405,0.000027665252,0.0011216805,0.00018516519,0.0005414234,0.00032000264],"domain_scores_gemma":[0.9972039,0.00055840326,0.00059372943,0.00013278669,0.0013948503,0.00011637246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005064653,0.00023897411,0.00040119153,0.0002600201,0.00006746993,0.00007413092,0.0003567427,0.00007391037,0.000037829817],"category_scores_gemma":[0.001592039,0.00023999526,0.00033790973,0.00015305271,0.000049290185,0.00031046986,0.000051581177,0.00026202295,6.399592e-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":[0.00013964238,0.00007296561,0.0047018053,0.00013188751,0.0001952335,0.00012426567,0.0002814112,0.9837165,0.00183323,0.00019461078,0.00021426944,0.008394213],"study_design_scores_gemma":[0.0015063835,0.0001091495,0.000052798507,0.0007879221,0.000044476812,0.0004372233,0.00008443339,0.97238773,0.022107776,0.0011349652,0.0011269576,0.00022019212],"about_ca_topic_score_codex":0.000008293136,"about_ca_topic_score_gemma":0.0000395909,"teacher_disagreement_score":0.11406389,"about_ca_system_score_codex":0.00016926747,"about_ca_system_score_gemma":0.00024190267,"threshold_uncertainty_score":0.97867227},"labels":[],"label_agreement":null},{"id":"W3179637715","doi":"10.5539/jmr.v13n4p50","title":"Volatility Analysis and Visualization of Climate Data Based on Wavelets","year":2021,"lang":"en","type":"article","venue":"Journal of Mathematics Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Mean squared error; Wavelet; Heteroscedasticity; Mathematics; Statistics; Volatility (finance); Econometrics; Autoregressive model; Autoregressive integrated moving average; Time series; Autoregressive–moving-average model; Computer science","score_opus":0.11081238504218141,"score_gpt":0.39087470084098536,"score_spread":0.2800623157988039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3179637715","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":[0.91659623,0.00033537508,0.07809231,0.000055613247,0.00007250004,0.000044022072,0.000037890746,0.000012554788,0.0047534877],"genre_scores_gemma":[0.98762876,0.0003036983,0.011989548,0.0000032865657,0.0000325576,2.6462396e-7,0.000015782978,0.000011431168,0.000014662645],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874324,0.00008903054,0.00040181197,0.00007372672,0.0005488165,0.00014337846],"domain_scores_gemma":[0.9986151,0.0005612197,0.000091490525,0.00032166444,0.0003484538,0.00006209376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023867495,0.00005716147,0.00023655436,0.00035775464,0.00003869651,0.00003708528,0.00017228095,0.00004090497,0.000075770455],"category_scores_gemma":[0.000646751,0.000047746675,0.00004834157,0.0006910551,0.000028263596,0.0000973097,0.00007852994,0.0001932761,9.1896055e-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":[0.00031894955,0.0043034893,0.13255772,0.022658948,0.0070859273,0.00091315014,0.010302792,0.5846787,0.111235924,0.018520018,0.005131529,0.10229288],"study_design_scores_gemma":[0.00015464253,0.000036703503,0.0013906856,0.00018619062,0.00006823099,0.000008610625,0.000116015,0.9895824,0.00798471,0.0002932382,0.00013749002,0.000041035655],"about_ca_topic_score_codex":0.0000017912403,"about_ca_topic_score_gemma":0.000013202238,"teacher_disagreement_score":0.40490377,"about_ca_system_score_codex":0.000022641236,"about_ca_system_score_gemma":0.0000423757,"threshold_uncertainty_score":0.19470528},"labels":[],"label_agreement":null},{"id":"W3180293405","doi":"10.1109/access.2021.3095420","title":"Load Forecasting Under Concept Drift: Online Ensemble Learning With Recurrent Neural Network and ARIMA","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Hydro; Western University","funders":"Ontario Centres of Excellence","keywords":"Computer science; Concept drift; Autoregressive integrated moving average; Recurrent neural network; Ensemble learning; Artificial intelligence; Machine learning; Energy consumption; Adaptation (eye); Adaptive learning; Artificial neural network; Energy (signal processing); Electricity; Time series; Data stream mining; Engineering","score_opus":0.033713584592484094,"score_gpt":0.2559262365046391,"score_spread":0.222212651912155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3180293405","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":[0.98157316,0.0025345618,0.01113601,0.000059789567,0.0010249815,0.0000607901,0.000004505059,0.00026364464,0.0033425663],"genre_scores_gemma":[0.99760437,0.00009550317,0.0012433657,0.0001143532,0.0006533732,0.000005868626,0.00003444528,0.000053485146,0.00019526322],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988805,0.00003678378,0.00021336094,0.00026386912,0.00017688947,0.00042861816],"domain_scores_gemma":[0.9994741,0.00013862169,0.00005473418,0.00014191678,0.000082774284,0.00010782201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000084787265,0.000212577,0.00022894239,0.00002856252,0.00016265489,0.00019627817,0.0001320162,0.00007452788,0.00003090283],"category_scores_gemma":[0.00002676358,0.00019667271,0.000036146554,0.00029711553,0.000040196912,0.00031217968,0.00006934084,0.00041179615,0.0000015895924],"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":[0.0000102814265,0.000012402237,0.005197811,0.000049746468,0.000045727462,0.000078369485,0.00028874766,0.9491531,0.00040988738,0.000084645064,0.00038339317,0.04428586],"study_design_scores_gemma":[0.0012785925,0.00013962423,0.00454204,0.00074263266,0.000085109445,0.0003625279,0.00032212297,0.9744757,0.0077725314,0.0002971464,0.009111534,0.00087043387],"about_ca_topic_score_codex":0.000033281918,"about_ca_topic_score_gemma":0.00043548446,"teacher_disagreement_score":0.043415427,"about_ca_system_score_codex":0.00004408915,"about_ca_system_score_gemma":0.00004170639,"threshold_uncertainty_score":0.80200803},"labels":[],"label_agreement":null},{"id":"W3181713895","doi":"10.17762/turcomat.v12i8.3414","title":"Particle Swarm Optimization for Least Square Support Vector Machine in MediumTerm Electricity Price Prediction","year":2021,"lang":"en","type":"article","venue":"Turkish Journal of Computer and Mathematics Education (TURCOMAT)","topic":"Energy Load and Power Forecasting","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":"Particle swarm optimization; Electricity market; Support vector machine; Electricity price forecasting; Electricity; Computer science; Term (time); Index (typography); Econometrics; Mathematical optimization; Economics; Artificial intelligence; Machine learning; Engineering; Mathematics","score_opus":0.009193083841574079,"score_gpt":0.22560014761106537,"score_spread":0.2164070637694913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181713895","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":[0.4141791,0.001340076,0.58251494,0.00016384231,0.0012634302,0.00014583756,0.00000939605,0.00005321452,0.00033018735],"genre_scores_gemma":[0.9246963,0.00020719355,0.074494526,0.00007649414,0.00040540347,0.00001009985,0.00003855154,0.000026054806,0.0000453279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990799,0.00002228585,0.00050717505,0.00008989203,0.00014177234,0.00015901364],"domain_scores_gemma":[0.9993657,0.0001103796,0.00015142611,0.000091213005,0.00020252145,0.000078761135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002495834,0.00012124034,0.00021742043,0.00010220813,0.000045569603,0.00007348751,0.000078674675,0.000056335262,0.000029625642],"category_scores_gemma":[0.00004240198,0.000114593415,0.000056518104,0.00018689851,0.000010365679,0.00022213241,0.000015600614,0.00013579539,7.16961e-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":[0.00007115754,0.0021451013,0.0062458,0.0039806655,0.00033593996,0.00003640016,0.019791458,0.832032,0.0043739383,0.00592217,0.009825389,0.11523993],"study_design_scores_gemma":[0.0008204804,0.00012234497,0.002783827,0.0003559273,0.0000538417,0.0006226548,0.0001966021,0.98498774,0.0071985377,0.0009802105,0.0016967764,0.00018106218],"about_ca_topic_score_codex":9.993024e-7,"about_ca_topic_score_gemma":0.0000023053324,"teacher_disagreement_score":0.51051724,"about_ca_system_score_codex":0.00006354657,"about_ca_system_score_gemma":0.000096242235,"threshold_uncertainty_score":0.4672984},"labels":[],"label_agreement":null},{"id":"W3183522257","doi":"10.1016/j.compeleceng.2021.107333","title":"The Impact of Technological Advancements on Educational Innovation (VSI-tei)","year":2021,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Knowledge management; Business; Engineering management; Process management; Computer science; Engineering","score_opus":0.008121080740417138,"score_gpt":0.2328709732185313,"score_spread":0.22474989247811417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183522257","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":[0.7284267,0.0018765556,0.26458386,0.00024842494,0.00089982647,0.0001396061,0.000004946859,0.00054506486,0.003275002],"genre_scores_gemma":[0.99730754,0.000060411996,0.0024491115,0.000012038846,0.00008811511,0.000009720587,0.000016477088,0.000018618204,0.000037988346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922746,0.000008569518,0.00024087341,0.00012509704,0.00013552052,0.00026245773],"domain_scores_gemma":[0.9994344,0.00028335114,0.000028606823,0.00015406132,0.00006538575,0.00003416019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008027051,0.0001340201,0.00013706088,0.00016590903,0.000052820455,0.000025576077,0.00014491628,0.00006468965,0.000009691321],"category_scores_gemma":[0.00015501025,0.00010390326,0.00006707755,0.001161829,0.000016801083,0.000050057195,0.000028611243,0.0002455648,0.000004097946],"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":[0.0000045966995,0.00003653315,0.00017746494,0.000011205375,0.000065367494,0.00000354952,0.000012000168,0.88535696,0.019864386,0.037966862,0.00044097353,0.056060094],"study_design_scores_gemma":[0.00024858245,0.00012165896,0.003936599,0.000083762745,0.00000566145,0.000023507224,0.0000017570854,0.9632473,0.027386649,0.000756111,0.003963173,0.00022526011],"about_ca_topic_score_codex":8.6632775e-7,"about_ca_topic_score_gemma":1.1892596e-7,"teacher_disagreement_score":0.2688808,"about_ca_system_score_codex":0.00014024897,"about_ca_system_score_gemma":0.000036183617,"threshold_uncertainty_score":0.4237052},"labels":[],"label_agreement":null},{"id":"W3184434808","doi":"10.1109/icjece.2021.3076124","title":"Short-Term Load Forecasting for Jordan Power System Based on NARX-ELMAN Neural Network and ARMA Model","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":39,"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":"Nonlinear autoregressive exogenous model; Artificial neural network; Autoregressive–moving-average model; Autoregressive model; MATLAB; Computer science; Backpropagation; Term (time); Electric power system; Moving average; Power (physics); Toolbox; Artificial intelligence; Machine learning; Econometrics; Mathematics","score_opus":0.011675595161600012,"score_gpt":0.17309979979723028,"score_spread":0.16142420463563026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184434808","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":[0.6305624,0.0026062077,0.36557776,0.000047907146,0.00083384925,0.00007760056,0.000006932091,0.000057256777,0.00023002263],"genre_scores_gemma":[0.99157274,0.000007650005,0.007796583,0.00006954838,0.0005025988,0.0000028199834,0.0000025290676,0.000038705057,0.000006838743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895424,0.000010597254,0.0003037192,0.00014313462,0.00010678763,0.00048152145],"domain_scores_gemma":[0.99913144,0.00014863789,0.000031745898,0.00007829592,0.000092944,0.00051693985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013886696,0.00019676963,0.00028111308,0.00013966335,0.00010465731,0.00011235903,0.0000896964,0.000076131415,0.000001237725],"category_scores_gemma":[0.000023831974,0.00019529251,0.000087239925,0.00018704389,0.000010210303,0.00008791083,0.000009446613,0.00029805573,8.741902e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000064941514,0.0000026055504,0.00045867576,0.000085673564,0.000035315057,0.00020125014,0.000054204622,0.98777324,0.00009245554,0.00076301896,0.00020741942,0.01031963],"study_design_scores_gemma":[0.00029299458,0.00013088746,0.00049041025,0.00030330886,0.000022577273,0.0005406726,0.0000032666628,0.9973289,0.0001334924,0.000019163625,0.00052804954,0.00020629365],"about_ca_topic_score_codex":0.000008392882,"about_ca_topic_score_gemma":0.00006661512,"teacher_disagreement_score":0.36101028,"about_ca_system_score_codex":0.00013998804,"about_ca_system_score_gemma":0.00012406587,"threshold_uncertainty_score":0.79637975},"labels":[],"label_agreement":null},{"id":"W3184945381","doi":"10.1109/siu53274.2021.9477869","title":"A hybrid deep learning algorithm for short-term electric load forecasting","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Discovery Centre","funders":"","keywords":"Computer science; Electricity; Electrical load; Mean absolute percentage error; Feature selection; Electric power system; Artificial neural network; Artificial intelligence; Term (time); Electric power; Feature (linguistics); Algorithm; Machine learning; Power (physics); Engineering; Voltage; Electrical engineering","score_opus":0.01549045768573133,"score_gpt":0.2162893943395264,"score_spread":0.20079893665379508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184945381","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":[0.09102787,0.0020480922,0.85566443,0.000011048626,0.0006107548,0.00012531283,0.0000052917417,0.00084614445,0.04966103],"genre_scores_gemma":[0.94048065,0.00010130091,0.056770895,0.000040855542,0.00050232775,0.00005155777,0.00007088504,0.000097624594,0.0018839111],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880826,0.000015149089,0.0002532055,0.00024584925,0.00015463021,0.0005228724],"domain_scores_gemma":[0.99947083,0.00016311751,0.000019374833,0.00013252576,0.00011526561,0.0000988965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015703466,0.00019756665,0.00020888886,0.00006743451,0.00014826334,0.000081156,0.000097954966,0.000060912134,0.000115456096],"category_scores_gemma":[0.00009711541,0.00020856733,0.00012790317,0.00022413064,0.0000073793262,0.00013063879,0.000034482382,0.00023394013,0.000011585418],"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":[0.000001993751,0.0000120680825,0.00030088637,0.000060247476,0.00006496754,0.00007564065,0.00010917892,0.022604221,0.0072614555,0.000160099,0.00025743825,0.96909183],"study_design_scores_gemma":[0.0002209847,0.00003903883,0.000041096922,0.000039711536,0.000022430071,0.00018792514,0.000031192016,0.928743,0.061009753,0.00013491264,0.009250018,0.00027990894],"about_ca_topic_score_codex":0.00000454187,"about_ca_topic_score_gemma":0.000021373991,"teacher_disagreement_score":0.96881187,"about_ca_system_score_codex":0.000095986376,"about_ca_system_score_gemma":0.000037576305,"threshold_uncertainty_score":0.85051286},"labels":[],"label_agreement":null},{"id":"W3184963121","doi":"10.2514/6.2021-3003","title":"Electric Motor Modeling using Artificial Neural Networks: Application for Drones","year":2021,"lang":"en","type":"article","venue":"AIAA AVIATION 2021 FORUM","topic":"Energy Load and Power Forecasting","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":"Université du Québec","funders":"","keywords":"Artificial neural network; MATLAB; Computer science; Torque; Universality (dynamical systems); Test data; Test bench; Control engineering; Drone; Artificial intelligence; Engineering; Embedded system","score_opus":0.016246990663497746,"score_gpt":0.22908614522976434,"score_spread":0.2128391545662666,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3184963121","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":[0.14104266,0.0006157296,0.8571495,0.000099531404,0.00069119636,0.00014843704,0.0000068049467,0.00012139885,0.00012475064],"genre_scores_gemma":[0.9963619,0.00003038074,0.0026586028,0.00006233147,0.0005713557,0.00006081264,0.00016925552,0.000043686934,0.000041687843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908215,0.000014795296,0.00027379516,0.00019619899,0.00010351028,0.00032954806],"domain_scores_gemma":[0.99961436,0.000045836598,0.00004422222,0.00014822937,0.0000997396,0.00004760334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008914577,0.00013061626,0.00013416173,0.00006916809,0.00016298065,0.000059146972,0.0000633523,0.000095283154,0.000021104917],"category_scores_gemma":[0.000030386846,0.00015560641,0.00008614884,0.00033999165,0.0000040191935,0.00019485483,0.00001738622,0.000100396755,0.000004591732],"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":[0.0000059872345,0.00001001282,0.0001635101,0.000015673857,0.000016733324,7.0017956e-7,0.000026273316,0.9410475,0.031179989,0.0035681417,0.000037538313,0.023927977],"study_design_scores_gemma":[0.00011446963,0.00001367769,0.000019414492,0.00001331469,0.000023056109,0.0000050227595,0.000052110227,0.9901831,0.0070998706,0.0019343394,0.00037513307,0.00016648864],"about_ca_topic_score_codex":0.0000133509075,"about_ca_topic_score_gemma":0.000038721482,"teacher_disagreement_score":0.8553192,"about_ca_system_score_codex":0.00007332671,"about_ca_system_score_gemma":0.000022835191,"threshold_uncertainty_score":0.63454455},"labels":[],"label_agreement":null},{"id":"W3187911394","doi":"10.24963/ijcai.2021/374","title":"Residential Electric Load Forecasting via Attentive Transfer of Graph Neural Networks","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial neural network; Process (computing); Electrical load; Transfer of learning; Electric power system; Graph; Artificial intelligence; Machine learning; Time series; Transfer (computing); Data mining; Power (physics); Theoretical computer science","score_opus":0.0115148762542675,"score_gpt":0.19114902205150192,"score_spread":0.1796341457972344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3187911394","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":[0.6941388,0.0014876099,0.28760058,0.000017181532,0.000730843,0.000059998965,0.0000022482202,0.00021428804,0.015748398],"genre_scores_gemma":[0.9990331,0.000052220457,0.00044879055,0.000026540214,0.0001796356,0.0000043066907,0.0000115523335,0.000035070094,0.00020880805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989484,0.000028084032,0.00030815013,0.0001757586,0.00018204685,0.00035757772],"domain_scores_gemma":[0.9995976,0.00007565497,0.000016592736,0.00013989146,0.00010574348,0.00006452701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009978182,0.00015957461,0.00021106264,0.00007727878,0.000054147527,0.000024635363,0.00009562949,0.00008922897,0.00018514278],"category_scores_gemma":[0.000017511948,0.00016104129,0.00015299479,0.00059578376,0.000017641058,0.000128233,0.000019512347,0.00020801723,0.0000021527694],"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":[0.00002967677,0.000034802517,0.0020129562,0.00011142178,0.00021627688,0.00013298746,0.00040402822,0.8920263,0.065312706,0.000691839,0.0006014673,0.03842555],"study_design_scores_gemma":[0.0003525731,0.000034902656,0.0006804477,0.00004414177,0.00004386284,0.0000696938,0.00003555528,0.9143877,0.083866656,0.00012891961,0.00012900827,0.00022650733],"about_ca_topic_score_codex":0.000055033437,"about_ca_topic_score_gemma":0.00023816495,"teacher_disagreement_score":0.30489424,"about_ca_system_score_codex":0.000026205227,"about_ca_system_score_gemma":0.000018410243,"threshold_uncertainty_score":0.65670735},"labels":[],"label_agreement":null},{"id":"W3188922109","doi":"10.1109/mcom.001.2001140","title":"Intelligent Photovoltaic Power Forecasting Methods for a Sustainable Electricity Market of Smart Micro-Grid","year":2021,"lang":"en","type":"article","venue":"IEEE Communications Magazine","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":35,"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":"Software deployment; Standardization; Electricity; Smart grid; Computer science; Electricity market; Sustainability; Grid; Environmental economics; Profit (economics); Photovoltaic system; Electricity generation; Telecommunications; Operations research; Power (physics); Electrical engineering; Economics; Engineering; Microeconomics","score_opus":0.03487550747510392,"score_gpt":0.2973580302791826,"score_spread":0.2624825228040787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188922109","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056614622,0.022822715,0.76239353,0.00030422836,0.0014313437,0.0011560984,0.00010223153,0.00066829374,0.15450692],"genre_scores_gemma":[0.43190673,0.001183587,0.56070846,0.000086927765,0.00009433422,0.0002287427,0.000116448275,0.00010502501,0.005569729],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.998624,0.00015903961,0.0005180778,0.00018600487,0.00008242297,0.0004304456],"domain_scores_gemma":[0.99702215,0.0009966141,0.00010731929,0.0012470094,0.00054890354,0.00007800066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078163325,0.00019849582,0.00032337566,0.00016823769,0.00018937311,0.000044914854,0.0005829991,0.00009466976,0.00013118026],"category_scores_gemma":[0.0004783758,0.00022305718,0.0001653753,0.0006685231,0.00007105196,0.00014041456,0.00019017418,0.00026797896,0.000006797058],"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":[0.00010305833,0.0005854855,0.00090835924,0.0013190748,0.00062998134,0.000017030192,0.0014555674,0.013292209,0.86338675,0.00450683,0.0606322,0.053163473],"study_design_scores_gemma":[0.00033954912,0.000074771204,0.0001035923,0.000104538056,0.000063868065,0.000044876408,0.00018192762,0.16633253,0.3752562,0.00083356653,0.45635828,0.00030631144],"about_ca_topic_score_codex":0.000021849006,"about_ca_topic_score_gemma":0.00006573348,"teacher_disagreement_score":0.48813054,"about_ca_system_score_codex":0.000119446675,"about_ca_system_score_gemma":0.000087924775,"threshold_uncertainty_score":0.90960085},"labels":[],"label_agreement":null},{"id":"W3191423952","doi":"10.36227/techrxiv.15124416.v1","title":"LSTM-based Multi-Step SOC Forecasting of Battery Energy Storage in Grid Ancillary Services","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Artificial Intelligence in Medicine (Canada)","funders":"Chonnam National University","keywords":"Computer science; Mean squared error; Battery (electricity); Grid; Hyperparameter optimization; Artificial intelligence; State of charge; Machine learning; Deep learning; Time series; Domain (mathematical analysis); State (computer science); Data mining; Support vector machine; Algorithm","score_opus":0.024301096338618478,"score_gpt":0.2154813348640905,"score_spread":0.191180238525472,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191423952","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":[0.90988225,0.0048656743,0.06860813,0.00003369656,0.005109247,0.00017822169,0.00012504045,0.0005657508,0.010631991],"genre_scores_gemma":[0.97673935,0.00013236595,0.021679362,0.00016118432,0.00045693666,0.000043223125,0.00053138885,0.00012623661,0.00012996563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978595,0.00006179082,0.00078362826,0.0005035933,0.0002846243,0.00050690904],"domain_scores_gemma":[0.99889916,0.00016912408,0.00017701568,0.0005467912,0.000100620775,0.00010729091],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026522554,0.0005132417,0.0007260327,0.0003339231,0.00003764237,0.00007310833,0.00039914704,0.0004923405,0.00013798581],"category_scores_gemma":[0.000021358228,0.0005536549,0.00024222757,0.00027439126,0.000029729023,0.00012941187,0.0004045925,0.00061464903,0.0000015593298],"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":[0.0000065155546,0.000052417912,0.003206348,0.0026905348,0.00008234727,0.00013418774,0.00038373118,0.98542744,0.0010480852,0.000019811196,0.00012369311,0.0068248934],"study_design_scores_gemma":[0.00049912086,0.0000140392085,0.00062368595,0.0020656341,0.00002608257,0.0000066518023,0.00028667264,0.9832372,0.011279898,0.0000067011347,0.0013709202,0.00058340444],"about_ca_topic_score_codex":0.0016042398,"about_ca_topic_score_gemma":0.006404752,"teacher_disagreement_score":0.066857085,"about_ca_system_score_codex":0.00013944258,"about_ca_system_score_gemma":0.00011585459,"threshold_uncertainty_score":0.9996915},"labels":[],"label_agreement":null},{"id":"W3191813552","doi":"10.1145/3466684","title":"Mining Customers’ Changeable Electricity Consumption for Effective Load Forecasting","year":2021,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electricity; Computer science; Consumption (sociology); Exploit; Task (project management); Artificial neural network; Electricity market; Energy consumption; Artificial intelligence; Machine learning; Computer security; Economics","score_opus":0.02931521277631001,"score_gpt":0.24188804453331433,"score_spread":0.2125728317570043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3191813552","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":[0.3082429,0.005134959,0.68317086,0.00010106953,0.0014027507,0.0005483051,0.000028367276,0.00071062555,0.00066018244],"genre_scores_gemma":[0.99729884,0.00042521983,0.0014997866,0.000010995202,0.000052619995,0.00039185653,0.000007582711,0.000034772824,0.00027835387],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99904835,0.000019521121,0.0002472853,0.00026593133,0.00008892803,0.00032996992],"domain_scores_gemma":[0.9992456,0.0002940416,0.000043166234,0.0002400287,0.00012701255,0.000050145587],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015337161,0.00018118149,0.00025509534,0.00028318606,0.00021286182,0.000036367863,0.00009820657,0.00024381837,0.000014969634],"category_scores_gemma":[0.00007897586,0.00018979324,0.000058518654,0.00041915043,0.000042198168,0.000067053494,0.000006039593,0.00022303352,0.000008640893],"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":[0.00005417916,0.00012517924,0.0010458635,0.0012398245,0.0006310957,0.000040800493,0.0009239423,0.07152692,0.01741382,0.0045350357,0.00021718036,0.9022462],"study_design_scores_gemma":[0.001047469,0.0005752575,0.000030446005,0.0009833743,0.0001974337,0.0007503703,0.0017823087,0.3436731,0.589782,0.00083385187,0.059507616,0.00083677686],"about_ca_topic_score_codex":0.000024480982,"about_ca_topic_score_gemma":0.000077358236,"teacher_disagreement_score":0.9014094,"about_ca_system_score_codex":0.00013777308,"about_ca_system_score_gemma":0.000019497082,"threshold_uncertainty_score":0.7739544},"labels":[],"label_agreement":null},{"id":"W3192429335","doi":"10.35833/mpce.2020.00647","title":"Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm","year":2021,"lang":"en","type":"article","venue":"Journal of Modern Power Systems and Clean Energy","topic":"Energy Load and Power Forecasting","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":"Particle swarm optimization; Inertia; Artificial neural network; Convergence (economics); Computer science; Energy (signal processing); Local optimum; Mathematical optimization; Multi-swarm optimization; Jump; Algorithm; Control theory (sociology); Artificial intelligence; Mathematics; Statistics","score_opus":0.007516345431435144,"score_gpt":0.1746521456579459,"score_spread":0.16713580022651076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192429335","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":[0.040230855,0.0012999225,0.9576749,0.000009514266,0.00045372042,0.000041267886,0.00002553405,0.00005631371,0.00020796248],"genre_scores_gemma":[0.9935194,0.00020711405,0.0059860977,0.000019998193,0.00013600253,0.0000060722723,0.000030138901,0.000054464363,0.0000406727],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985557,0.00007743321,0.0006318546,0.00019485419,0.00030044417,0.00023966878],"domain_scores_gemma":[0.9990619,0.000033869503,0.00023447632,0.00016121202,0.00035597588,0.00015259327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021334292,0.00023410952,0.00041115237,0.0000800032,0.000076855074,0.00008420238,0.00006956716,0.00013315216,0.000002176507],"category_scores_gemma":[0.000006771152,0.00019044802,0.00006194353,0.00018826738,0.000029478831,0.00023619855,0.000017781183,0.00013640974,1.620366e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008702687,0.000035268098,0.000103285995,0.00008293247,0.00010086145,0.000027239406,0.00012350398,0.9865683,0.00057758583,0.00027859473,0.000026840678,0.011988577],"study_design_scores_gemma":[0.0008379884,0.00030343494,0.0000319329,0.0006375815,0.00006281392,0.0002150053,0.00018320204,0.9956036,0.0019219758,0.0000033428946,0.00003294474,0.00016618407],"about_ca_topic_score_codex":0.000042165746,"about_ca_topic_score_gemma":0.000022360387,"teacher_disagreement_score":0.95328856,"about_ca_system_score_codex":0.000112883106,"about_ca_system_score_gemma":0.00007297975,"threshold_uncertainty_score":0.77662444},"labels":[],"label_agreement":null},{"id":"W3192516459","doi":"10.32920/ryerson.14649129.v1","title":"Day-Ahead Electricity Price And Spike Forecasting Using Machine Learning Techniques","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Windsor; Toronto Metropolitan University","funders":"","keywords":"Electricity price forecasting; Electricity; Spike (software development); Electricity market; Artificial neural network; Econometrics; Computer science; Artificial intelligence; Economics; Machine learning; Engineering","score_opus":0.026516066469268452,"score_gpt":0.23392156610097983,"score_spread":0.20740549963171137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192516459","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":[0.7548341,0.0063829687,0.20560019,0.00001586478,0.00061430625,0.00028308845,0.00000983543,0.002245532,0.030014075],"genre_scores_gemma":[0.9040414,0.0006800464,0.0943455,0.000034008026,0.00037376376,0.000019334295,0.00009568516,0.00014465039,0.00026559873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817705,0.00007617612,0.0004633065,0.00052196754,0.00021374511,0.0005477247],"domain_scores_gemma":[0.9992357,0.00014918849,0.00012330395,0.00027775104,0.00008350092,0.00013059338],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004808514,0.00049877784,0.0005394471,0.00023981907,0.00018810331,0.00027192768,0.0001932947,0.00042746204,0.000070988426],"category_scores_gemma":[0.0001660388,0.0005240522,0.00012218814,0.00029052357,0.000030065874,0.00015591248,0.00059275725,0.0016652094,8.2174444e-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":[0.000022317721,0.00010342921,0.029369285,0.0051174364,0.0007894877,0.00045782293,0.0030051458,0.57606363,0.13635245,0.0006431429,0.00012807603,0.2479478],"study_design_scores_gemma":[0.0000886569,0.000025131465,0.0000830936,0.00072433945,0.000055428158,0.00012518937,0.000042233027,0.9287009,0.06734022,0.00010343421,0.0020479052,0.00066343485],"about_ca_topic_score_codex":0.00059629045,"about_ca_topic_score_gemma":0.00015346847,"teacher_disagreement_score":0.35263732,"about_ca_system_score_codex":0.00015176133,"about_ca_system_score_gemma":0.000055781195,"threshold_uncertainty_score":0.9997211},"labels":[],"label_agreement":null},{"id":"W3193034144","doi":"10.1109/tii.2021.3097716","title":"Deep Spatial-Temporal 2-D CNN-BLSTM Model for Ultrashort-Term LiDAR-Assisted Wind Turbine's Power and Fatigue Load Forecasting","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":56,"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; Canada First Research Excellence Fund","keywords":"Lidar; Turbine; Wind power; Computer science; Deep learning; Convolutional neural network; Wind speed; Renewable energy; Artificial intelligence; Recurrent neural network; Artificial neural network; Simulation; Remote sensing; Meteorology; Engineering; Aerospace engineering; Geology; Electrical engineering","score_opus":0.06418369217881079,"score_gpt":0.24751464975701898,"score_spread":0.1833309575782082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3193034144","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":[0.18146537,0.000038431765,0.81411,0.00002955988,0.0015248609,0.0003296499,0.00014697415,0.00024060767,0.0021145488],"genre_scores_gemma":[0.99163806,0.00002738699,0.0076302546,0.000068563124,0.00018572062,0.000043889613,0.0000690269,0.00006626156,0.00027086033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806744,0.000022044413,0.0009146094,0.0001809342,0.00031751758,0.00049744773],"domain_scores_gemma":[0.9989413,0.00024004345,0.000138231,0.00028055755,0.00018150873,0.0002183657],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000223167,0.00038563562,0.00041152642,0.00016133743,0.00030573018,0.00020330942,0.0001404131,0.00044573678,0.00006660239],"category_scores_gemma":[0.00004698834,0.00040455547,0.0001840098,0.0003075485,0.000057841553,0.00052452117,0.000003430876,0.00064815645,0.0000050019316],"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":[0.00007065763,0.00006355849,0.00006826986,0.00013162666,0.00016778689,0.0000069428797,0.0028290087,0.93519396,0.00040640103,0.000022031507,0.00023218628,0.060807575],"study_design_scores_gemma":[0.0018804874,0.0001252921,0.000012524607,0.00023966472,0.00010748528,0.000063444575,0.00047464942,0.97935754,0.016596586,0.000032922442,0.000638399,0.00047100955],"about_ca_topic_score_codex":0.000030430918,"about_ca_topic_score_gemma":0.00023584077,"teacher_disagreement_score":0.8101727,"about_ca_system_score_codex":0.0001619308,"about_ca_system_score_gemma":0.000193394,"threshold_uncertainty_score":0.9998406},"labels":[],"label_agreement":null},{"id":"W3194311536","doi":"10.1080/0305215x.2021.1961762","title":"Impact of coronavirus disease 2019 on electricity demand and the unit commitment problem: a long–short-term memory-based machine learning approach","year":2021,"lang":"en","type":"article","venue":"Engineering Optimization","topic":"Energy Load and Power Forecasting","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":"Electricity; Coronavirus disease 2019 (COVID-19); Pandemic; Electricity demand; Environmental economics; Scheduling (production processes); Mains electricity; Computer science; Peak demand; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Operations research; Business; Electricity generation; Economics; Operations management; Power (physics); Engineering; Medicine; Disease; Infectious disease (medical specialty); Electrical engineering","score_opus":0.013460968682857104,"score_gpt":0.22883358770815138,"score_spread":0.21537261902529428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194311536","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":[0.25497115,0.0024417294,0.74152136,0.000019076988,0.00007791291,0.00026305424,0.000020849007,0.00023582652,0.00044906855],"genre_scores_gemma":[0.99312836,0.0001964323,0.006232275,0.000008136643,0.000032890013,0.000025265797,0.00028340897,0.00004561275,0.000047610036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992429,0.000053018095,0.00020637066,0.00015638774,0.00014314573,0.00019816658],"domain_scores_gemma":[0.99953485,0.00011579104,0.000036572317,0.00016675875,0.000041894134,0.00010415301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017828593,0.00019488441,0.00020575637,0.00008916601,0.000075284755,0.00004058641,0.00007298446,0.00005050872,0.000018317236],"category_scores_gemma":[0.00006259831,0.00015479863,0.00007857793,0.00027471577,0.000022586222,0.000067880515,0.00002201913,0.00022574757,4.384556e-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":[0.000056828074,0.00003914869,0.0044785086,0.00012329176,0.00007482583,0.000005874343,0.000096679636,0.9940248,0.0002033354,0.00010432171,0.0000055920614,0.00078677846],"study_design_scores_gemma":[0.00085918274,0.00004658385,0.004407038,0.000070846785,0.000049780996,0.000006053225,0.0000021461603,0.9936472,0.0007303751,0.000002131994,0.000019056431,0.00015959614],"about_ca_topic_score_codex":0.000018971028,"about_ca_topic_score_gemma":0.0000017743916,"teacher_disagreement_score":0.7381572,"about_ca_system_score_codex":0.000066214656,"about_ca_system_score_gemma":0.000034504537,"threshold_uncertainty_score":0.6312505},"labels":[],"label_agreement":null},{"id":"W3194760197","doi":"10.21203/rs.3.rs-779973/v1","title":"Improving Hybrid Models For Precipitation Forecasting By Combining Nonlinear Machine Learning Methods","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Energy Load and Power Forecasting","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 British Columbia","funders":"","keywords":"Nonlinear system; Computer science; Artificial intelligence; Machine learning; Precipitation; Meteorology; Geography; Physics","score_opus":0.08464056346507971,"score_gpt":0.3721472994856457,"score_spread":0.287506736020566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194760197","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":[0.08143687,0.0102276,0.90283567,0.00004074505,0.0010223427,0.0011103295,0.00031906468,0.00074542954,0.0022619683],"genre_scores_gemma":[0.6814499,0.0004935471,0.31179646,0.000009091294,0.00058350124,0.0006521314,0.004213748,0.0003605747,0.00044102923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957069,0.0006953003,0.00072524714,0.0008640468,0.00075694354,0.0012515427],"domain_scores_gemma":[0.99602973,0.0022433836,0.0001650868,0.00051920814,0.0007791435,0.00026345442],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004441486,0.0005210448,0.0006736553,0.00047105842,0.0005846114,0.0006368165,0.0005088441,0.00040032715,0.000031838215],"category_scores_gemma":[0.0019694795,0.000591264,0.00032385445,0.0003968331,0.000068962225,0.0003679544,0.0008853664,0.0033498032,0.00000260463],"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":[0.000024664034,0.00003420727,0.00007641175,0.0034318266,0.00013214673,0.000015426942,0.0014679075,0.8403162,0.004964082,0.00006254651,0.0002299733,0.14924462],"study_design_scores_gemma":[0.00044605517,0.00014545897,0.0000018077402,0.0015798118,0.000031502175,0.000011704144,0.0008503228,0.97728443,0.016280524,0.000806888,0.0020186575,0.00054285245],"about_ca_topic_score_codex":0.0003480099,"about_ca_topic_score_gemma":0.000044555312,"teacher_disagreement_score":0.600013,"about_ca_system_score_codex":0.00041675076,"about_ca_system_score_gemma":0.00022682126,"threshold_uncertainty_score":0.9996539},"labels":[],"label_agreement":null},{"id":"W3195205837","doi":"10.5539/ijsp.v10n5p20","title":"Fitting Compound Archimedean Copulas to Data for Modeling Electricity Demand","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Energy Load and Power Forecasting","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":"Copula (linguistics); Mathematics; Econometrics; Tail dependence; Statistical physics; Mathematical optimization; Statistics; Physics; Multivariate statistics","score_opus":0.04207752067156798,"score_gpt":0.2918518358083031,"score_spread":0.24977431513673512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195205837","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":[0.18204287,0.0002332796,0.81636274,0.00017536989,0.00046152365,0.00004774781,0.0005102661,0.000009024352,0.00015719101],"genre_scores_gemma":[0.8004643,0.000068473746,0.1990968,0.000058043486,0.00021160289,0.0000010659007,0.00008409881,0.000008708003,0.000006915615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917465,0.000022658438,0.00037886028,0.00011924116,0.0001882575,0.00011630236],"domain_scores_gemma":[0.998971,0.00032010642,0.00006470669,0.00011248101,0.000428137,0.000103584534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005362677,0.00007650524,0.00014392233,0.0000462815,0.00004799783,0.000085030275,0.00022450066,0.000023733193,0.000012753883],"category_scores_gemma":[0.0008079018,0.00007428661,0.000023554807,0.000045583256,0.000013539118,0.000099690864,0.00008244544,0.00012144543,2.5961924e-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":[0.00022037156,0.00014354958,0.0044138874,0.0003056931,0.0005779711,0.00013886369,0.00083015644,0.82958496,0.0029851783,0.028056413,0.004492749,0.12825021],"study_design_scores_gemma":[0.0003262798,0.000044625198,0.00024449415,0.00006947469,0.000021999676,0.00013341432,0.000019202194,0.9525181,0.0004520052,0.041970816,0.004100659,0.000098948236],"about_ca_topic_score_codex":0.0000107083615,"about_ca_topic_score_gemma":0.00007351599,"teacher_disagreement_score":0.61842144,"about_ca_system_score_codex":0.00005001772,"about_ca_system_score_gemma":0.000066571934,"threshold_uncertainty_score":0.30293202},"labels":[],"label_agreement":null},{"id":"W3197697246","doi":"10.32920/ryerson.14655294.v1","title":"Techno-Economic Models for Integration of Wind Energy","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Toronto Metropolitan University; Toronto Public Health","funders":"","keywords":"Wind power; Probabilistic logic; Wind speed; Electric power system; Computer science; Induction generator; Electricity generation; Control theory (sociology); Power (physics); Engineering; Electrical engineering; Meteorology","score_opus":0.020701840992281242,"score_gpt":0.21607150809466683,"score_spread":0.19536966710238557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197697246","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":[0.052464843,0.0005869867,0.9023993,0.000013598318,0.0010132237,0.00008446236,0.000045265482,0.00024359662,0.043148696],"genre_scores_gemma":[0.97895634,0.000154869,0.019948455,0.000008378788,0.00013166989,0.000034887922,0.00024195998,0.00004165251,0.00048177704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993622,0.00000398905,0.0002858199,0.00018031207,0.000042676576,0.0001250333],"domain_scores_gemma":[0.9996061,0.00003318667,0.00005134672,0.0002472527,0.00003674622,0.000025401749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059833565,0.00016739074,0.00025836815,0.0000971707,0.000013018537,0.000030038023,0.000137979,0.00025868896,0.000048995036],"category_scores_gemma":[0.000005376675,0.00017196969,0.0001365349,0.000028450424,0.000011227452,0.00007423263,0.000090625355,0.00013561164,4.512587e-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":[0.0000015655618,0.0000039398237,0.0000014959609,0.00011557262,0.00004999332,2.3401991e-7,0.000078095196,0.96035767,0.002719822,0.022230804,0.0001689636,0.0142718665],"study_design_scores_gemma":[0.00007024217,0.000007747867,0.0000010561938,0.00015957023,0.000015330474,0.0000010956055,0.00005102665,0.89074624,0.10131548,0.0067782053,0.00069386757,0.00016011296],"about_ca_topic_score_codex":0.00035255353,"about_ca_topic_score_gemma":0.00034172923,"teacher_disagreement_score":0.9264915,"about_ca_system_score_codex":0.00006966935,"about_ca_system_score_gemma":0.00004308498,"threshold_uncertainty_score":0.701272},"labels":[],"label_agreement":null},{"id":"W3197767108","doi":"10.1007/978-3-030-63591-6_40","title":"The Impact of External Features on Prediction Accuracy in Short-Term Energy Forecasting","year":2021,"lang":"en","type":"book-chapter","venue":"Springer proceedings in mathematics & statistics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Trent University","funders":"","keywords":"Univariate; Multivariate statistics; Mean absolute percentage error; Benchmark (surveying); Mean squared error; Term (time); Computer science; Data mining; Feature (linguistics); Energy (signal processing); Time series; Series (stratigraphy); Energy consumption; Statistics; Algorithm; Machine learning; Artificial intelligence; Engineering; Mathematics","score_opus":0.022137710374520423,"score_gpt":0.25293151229247773,"score_spread":0.23079380191795731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3197767108","genre_codex":"other","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":[0.098449744,0.006595936,0.036282282,0.000013253744,0.0024235665,0.0012702822,0.0012288778,0.00053617713,0.8531999],"genre_scores_gemma":[0.8863443,0.009078072,0.07272871,0.000015330495,0.0013637296,0.00015437503,0.00019827236,0.0009685461,0.029148683],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99772495,0.000004996026,0.0010296901,0.00031263757,0.00046229616,0.00046540008],"domain_scores_gemma":[0.99860525,0.0006741151,0.00024420227,0.00022810129,0.00016877893,0.00007953158],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037118178,0.00054125127,0.00063925976,0.00031061354,0.00007681779,0.00014419241,0.0003448386,0.00031978037,0.0000312724],"category_scores_gemma":[0.00037250534,0.0004456376,0.00015426551,0.0000973781,0.000081581136,0.00010521602,0.00011442484,0.0008544671,0.0000015476227],"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":[0.00007064006,0.00016262862,0.0019818163,0.0034812645,0.00040188318,0.00027610676,0.0046391226,0.02954156,0.0029683267,0.8764405,0.0015736425,0.07846249],"study_design_scores_gemma":[0.0017158792,0.00085841096,0.006564286,0.042652622,0.00037583284,0.0004346662,0.0005750189,0.54862255,0.008431012,0.3825442,0.0036532714,0.003572236],"about_ca_topic_score_codex":0.000024522002,"about_ca_topic_score_gemma":0.00009232515,"teacher_disagreement_score":0.8240512,"about_ca_system_score_codex":0.00034248724,"about_ca_system_score_gemma":0.00006378469,"threshold_uncertainty_score":0.99979955},"labels":[],"label_agreement":null},{"id":"W3199011366","doi":"10.1109/icisce50968.2020.00049","title":"Air Temperature Forecasting using Traditional and Deep Learning Algorithms","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Manitoba; University of Toronto","funders":"","keywords":"Mean squared error; Machine learning; Artificial intelligence; Computer science; Air temperature; Deep learning; Algorithm; Atmospheric model; Meteorology; Mathematics; Statistics","score_opus":0.03766111911098595,"score_gpt":0.20174246724017406,"score_spread":0.16408134812918812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199011366","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":[0.960342,0.00084892014,0.022989834,0.00014987937,0.0002673376,0.00006370548,0.000005834057,0.0007248201,0.014607676],"genre_scores_gemma":[0.9821585,0.0000184115,0.017074125,0.0001870588,0.00047778938,0.0000018364971,0.000016391223,0.000032976375,0.00003293819],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994227,0.00001100291,0.00013168696,0.00014402211,0.00009530477,0.00019527801],"domain_scores_gemma":[0.9997624,0.000050600498,0.000014576837,0.00003253446,0.000015340827,0.00012451356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004709401,0.00013100283,0.0001178392,0.000030359477,0.00014699457,0.00003970404,0.00004570725,0.00006933072,0.00007864608],"category_scores_gemma":[0.000030008663,0.00012734198,0.00003237933,0.0001394139,0.00001773108,0.00016323754,0.000013662075,0.00027281116,0.0000031871045],"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":[0.0000054975567,0.000006929603,0.0010573346,0.00012983948,0.000060461873,0.000046560257,0.0017866275,0.92065656,0.0351755,0.0009913604,0.00018142512,0.039901905],"study_design_scores_gemma":[0.0001652224,0.000030331992,0.0001621284,0.00003193258,0.000008669245,0.00006981044,0.00019983346,0.9936702,0.0037370878,0.00004643649,0.0016989148,0.00017942299],"about_ca_topic_score_codex":0.0000043430887,"about_ca_topic_score_gemma":0.000002473711,"teacher_disagreement_score":0.07301365,"about_ca_system_score_codex":0.0000150208525,"about_ca_system_score_gemma":0.00000573015,"threshold_uncertainty_score":0.5192855},"labels":[],"label_agreement":null},{"id":"W3199041733","doi":"10.1007/978-3-030-87101-7_23","title":"A Comparative Study of Deep Learning Approaches for Day-Ahead Load Forecasting of an Electric Car Fleet","year":2021,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Computer science; Electric cars; Artificial intelligence; Automotive engineering; Engineering","score_opus":0.09492248453676443,"score_gpt":0.2860456767467332,"score_spread":0.19112319220996876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199041733","genre_codex":"other","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":[0.21214907,0.0043500634,0.38162595,0.000021220518,0.0005486954,0.0023692495,0.00004862013,0.00022775908,0.39865938],"genre_scores_gemma":[0.97988564,0.00016244705,0.019772338,0.000005585168,0.000017040984,0.000026847152,0.00007012459,0.00000919048,0.00005081683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988001,0.00003169636,0.0006521303,0.00012946004,0.00023829006,0.00014830289],"domain_scores_gemma":[0.9984812,0.00028608486,0.0002651447,0.00056370295,0.00036031622,0.00004356489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079041487,0.00015858325,0.0003528699,0.00046908713,0.00020094674,0.00007216958,0.00065335736,0.00007176667,0.0000018143859],"category_scores_gemma":[0.000040874635,0.00016863555,0.000035552304,0.00034955653,0.00018149232,0.001361102,0.00027859025,0.00028712847,3.4461382e-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":[0.0000066043135,0.00006476281,0.00008588693,0.0002079926,0.00004257977,1.621015e-7,0.040803496,0.57224375,0.00002933738,0.023376493,0.0000049993328,0.3631339],"study_design_scores_gemma":[0.00027651316,0.00018747107,0.0001792969,0.00015930594,0.000013695887,0.000004995762,0.000942998,0.9962947,0.00010244118,0.00016097522,0.0015220746,0.00015557247],"about_ca_topic_score_codex":0.000010930169,"about_ca_topic_score_gemma":0.00007174464,"teacher_disagreement_score":0.76773655,"about_ca_system_score_codex":0.00007607701,"about_ca_system_score_gemma":0.00008581713,"threshold_uncertainty_score":0.68767583},"labels":[],"label_agreement":null},{"id":"W3199393712","doi":"10.1016/j.enbuild.2021.111459","title":"A compositional kernel based gaussian process approach to day-ahead residential load forecasting","year":2021,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Probabilistic forecasting; Computer science; Gaussian process; Probabilistic logic; Kernel (algebra); Set (abstract data type); Process (computing); Bayesian probability; Electricity; Kriging; Demand forecasting; Electric power system; Multivariate statistics; Gaussian; Data mining; Mathematical optimization; Power (physics); Machine learning; Artificial intelligence; Engineering; Operations research; Mathematics","score_opus":0.012944819117599655,"score_gpt":0.21292167879265506,"score_spread":0.1999768596750554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199393712","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":[0.7125337,0.0011187047,0.20960215,0.0002605175,0.0005880149,0.00007411439,0.000025899708,0.00045388425,0.07534299],"genre_scores_gemma":[0.98678786,0.000011592052,0.012060063,0.00030259375,0.0003532334,0.00002621385,0.00006697106,0.00004068069,0.00035079208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988322,0.00002448465,0.00022112504,0.0003261902,0.00024931403,0.00034667313],"domain_scores_gemma":[0.9995144,0.00007322525,0.000029520976,0.00012706053,0.000079227175,0.00017655388],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001481289,0.00020502684,0.00019500668,0.00008492675,0.00019105215,0.00011627449,0.000113675815,0.00010135381,0.000040808292],"category_scores_gemma":[0.00004854253,0.00021535257,0.000058007583,0.00031828257,0.000030913197,0.00015485894,0.00004854638,0.00012628057,0.0000018481458],"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":[0.00011292171,0.00024443466,0.0014836129,0.0007807744,0.00022077293,0.0002525541,0.0022772606,0.8363548,0.080314,0.04574878,0.0065451222,0.025664996],"study_design_scores_gemma":[0.001918682,0.000113297145,0.0010737566,0.0011304606,0.00009311608,0.0005017445,0.00032399138,0.61756057,0.31512827,0.0023181874,0.058255117,0.0015828123],"about_ca_topic_score_codex":0.00004965221,"about_ca_topic_score_gemma":0.00007109748,"teacher_disagreement_score":0.27425414,"about_ca_system_score_codex":0.000047993788,"about_ca_system_score_gemma":0.000079456055,"threshold_uncertainty_score":0.8781823},"labels":[],"label_agreement":null},{"id":"W3199990629","doi":"10.1109/compsac51774.2021.00034","title":"Intelligent Probabilistic Forecasts of Day-Ahead Electricity Prices in a Highly Volatile Power Market","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Probabilistic logic; Electricity; Electricity market; Computer science; Environmental science; Electrical engineering; Engineering; Artificial intelligence","score_opus":0.010974827349959611,"score_gpt":0.21050733450266054,"score_spread":0.19953250715270093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199990629","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":[0.8238545,0.0007979142,0.009548293,0.000026768157,0.0003403528,0.00013499362,0.000007632875,0.0001517915,0.16513772],"genre_scores_gemma":[0.99662733,0.000044025768,0.0025199647,0.000018197663,0.000025994248,0.000012443386,0.000008893555,0.00002417781,0.0007189706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989661,0.000025927493,0.0003754467,0.00018702554,0.00014794702,0.00029754482],"domain_scores_gemma":[0.999455,0.00018672545,0.000036163117,0.00019723318,0.000065043714,0.000059874994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021276306,0.00015249655,0.00023798835,0.00011703891,0.000016703052,0.000018894358,0.00011210913,0.00007899275,0.00066209264],"category_scores_gemma":[0.00014634564,0.00014308841,0.00005270018,0.00056591374,0.000018374338,0.00009879055,0.00004162249,0.00015470198,0.000006927642],"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":[0.00033359334,0.0022899609,0.08799327,0.00445749,0.00091858173,0.00071086956,0.012278069,0.39208964,0.12717147,0.09488466,0.034454487,0.24241792],"study_design_scores_gemma":[0.0007702902,0.00026836022,0.020525558,0.0005382257,0.000039713603,0.00005063406,0.00019412061,0.7135288,0.23431039,0.0042836796,0.02453537,0.0009548314],"about_ca_topic_score_codex":0.00005023296,"about_ca_topic_score_gemma":0.0005296682,"teacher_disagreement_score":0.3214392,"about_ca_system_score_codex":0.0000721133,"about_ca_system_score_gemma":0.000044919754,"threshold_uncertainty_score":0.724945},"labels":[],"label_agreement":null},{"id":"W3200609747","doi":"10.17762/de.vi.4248","title":"Load Forecasting using Time Series Techniques","year":2021,"lang":"en","type":"article","venue":"Design Engineering","topic":"Energy Load and Power Forecasting","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":"Time series; Series (stratigraphy); Exponential smoothing; Computer science; Electric power system; Probabilistic forecasting; Moving average; Electrical load; Data mining; Power (physics); Econometrics; Machine learning; Artificial intelligence; Mathematics","score_opus":0.023602146183048216,"score_gpt":0.19488580725231472,"score_spread":0.1712836610692665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200609747","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040077638,0.0012794202,0.94866776,0.000011437537,0.00058235397,0.00011981509,0.000006611062,0.00264139,0.0066135474],"genre_scores_gemma":[0.42137727,0.00006717774,0.577128,0.000027006645,0.0005861529,0.000027291919,0.000015801736,0.0002234928,0.00054783473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989391,0.000016518938,0.00024972152,0.00019571542,0.00017153927,0.00042737884],"domain_scores_gemma":[0.9995247,0.00008215739,0.000022032384,0.00020686648,0.000074309246,0.00008989708],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019980127,0.00024871793,0.0002303858,0.00008873208,0.00007093902,0.00007803882,0.000111922345,0.00011084036,0.00007259798],"category_scores_gemma":[0.00010098354,0.0002920635,0.0000720344,0.00035458046,0.000011995888,0.00031413563,0.00004431452,0.00020013041,0.00001804231],"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":[0.0000020575144,0.0000039783063,0.000016720522,0.000072455405,0.00003570732,0.00012361688,0.00011595513,0.6758931,0.31976765,0.00012108517,0.00016643098,0.0036811964],"study_design_scores_gemma":[0.000057044905,0.0000106394145,0.0000061003498,0.00016425569,0.00001289598,0.0002541678,0.000008126752,0.6231535,0.37178513,0.000030891297,0.0042461203,0.0002711586],"about_ca_topic_score_codex":0.0000042283286,"about_ca_topic_score_gemma":0.0000011081174,"teacher_disagreement_score":0.38129964,"about_ca_system_score_codex":0.00015209515,"about_ca_system_score_gemma":0.000050277933,"threshold_uncertainty_score":0.99995315},"labels":[],"label_agreement":null},{"id":"W3205604801","doi":"10.3390/pr9101793","title":"Chaotic Analysis and Prediction of Wind Speed Based on Wavelet Decomposition","year":2021,"lang":"en","type":"article","venue":"Processes","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"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 Regina","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Wind speed; Chaotic; Kalman filter; Wavelet; Control theory (sociology); Fractal dimension; Lyapunov exponent; Computer science; Fractal; Algorithm; Mathematics; Meteorology; Artificial intelligence; Mathematical analysis; Physics","score_opus":0.009431963862088838,"score_gpt":0.21200017074254196,"score_spread":0.20256820688045313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205604801","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":[0.9878766,0.00060914655,0.008225082,0.000025904606,0.00008890257,0.00002287995,0.000023936514,0.00008125728,0.0030463166],"genre_scores_gemma":[0.99910176,0.000082375394,0.0006729083,0.000015309348,0.000032903175,5.0324525e-7,0.00006394572,0.0000073853266,0.000022928674],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967116,0.0000062465233,0.000097426244,0.00008470973,0.00007350772,0.00006692098],"domain_scores_gemma":[0.9997943,0.000048153004,0.000018408744,0.000059101138,0.00005670219,0.000023322027],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000033571334,0.00005734081,0.00009974162,0.00009580992,0.000022095755,0.000013193336,0.000019480776,0.000027536187,0.000032223405],"category_scores_gemma":[0.00003547542,0.000057442157,0.00002121287,0.0004916246,0.000008745708,0.00005080151,0.00000434759,0.000036299014,7.3376833e-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":[0.000017332653,0.0000735461,0.029101111,0.0024499828,0.00030128393,0.0000112523085,0.0004245645,0.9439917,0.018354997,0.000031998417,0.00005285402,0.0051893266],"study_design_scores_gemma":[0.0002706746,0.000052973297,0.016165681,0.00018091172,0.0002722794,0.000004660683,0.000043759315,0.70190895,0.28060165,0.000074589836,0.0003096557,0.000114233204],"about_ca_topic_score_codex":0.0000031678976,"about_ca_topic_score_gemma":0.000012230847,"teacher_disagreement_score":0.26224664,"about_ca_system_score_codex":0.000007872188,"about_ca_system_score_gemma":0.000014232087,"threshold_uncertainty_score":0.23424232},"labels":[],"label_agreement":null},{"id":"W3206145407","doi":"10.1016/j.compeleceng.2021.107480","title":"Prediction of wind farm reactive power fast variations by adaptive one-dimensional convolutional neural network","year":2021,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":23,"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 Saskatchewan","funders":"","keywords":"Flicker; Convolutional neural network; Controller (irrigation); Artificial neural network; Control theory (sociology); AC power; Computer science; Process (computing); Wind power; Model predictive control; Block (permutation group theory); Static VAR compensator; Electric power system; Power (physics); Artificial intelligence; Voltage; Engineering; Control (management); Mathematics","score_opus":0.009287852241412587,"score_gpt":0.1709670297235726,"score_spread":0.16167917748216,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206145407","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":[0.09814511,0.002087909,0.8958729,0.000046625773,0.0019457501,0.00013647968,0.00009384433,0.0005423564,0.0011290286],"genre_scores_gemma":[0.98864955,0.000010861408,0.0108115245,0.000034791425,0.00028913768,0.0000059033764,0.00012827225,0.00003917487,0.000030795665],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987863,0.000024009596,0.0003164642,0.00023502095,0.00024530248,0.00039291705],"domain_scores_gemma":[0.99934006,0.0002555091,0.000044279794,0.00012876811,0.000112625254,0.00011876067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007253618,0.00020201957,0.0002652122,0.0000854759,0.00006680553,0.000020533442,0.000098490054,0.00011719473,0.000020903968],"category_scores_gemma":[0.000033716038,0.00024391012,0.00009918024,0.00048771763,0.000018988663,0.000117337244,0.00004510284,0.0003693857,0.0000026891303],"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":[0.000009379245,0.000042450272,0.00008369721,0.0000095314035,0.00016290089,0.000007596932,0.000069357164,0.970834,0.022591112,0.003510681,0.0010981364,0.0015811468],"study_design_scores_gemma":[0.00032279076,0.000079297446,0.0027437664,0.000067146575,0.000027922984,0.000035468867,0.0000027087249,0.9919504,0.0033801757,0.00005852048,0.0011379838,0.00019381363],"about_ca_topic_score_codex":0.0000054015895,"about_ca_topic_score_gemma":8.788883e-7,"teacher_disagreement_score":0.8905044,"about_ca_system_score_codex":0.00015008026,"about_ca_system_score_gemma":0.00004320468,"threshold_uncertainty_score":0.9946366},"labels":[],"label_agreement":null},{"id":"W3206702056","doi":"10.32614/cran.package.cosmos","title":"CoSMoS: Complete Stochastic Modelling Solution","year":2019,"lang":"en","type":"dataset","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"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":"Global Institute for Water Security, University of Saskatchewan; Global Water Futures; University of Saskatchewan","keywords":"Cosmos (plant); Statistical physics; Mathematics; Computer science; Physics; Biology; Botany","score_opus":0.04432130590914146,"score_gpt":0.22567712609506693,"score_spread":0.18135582018592547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206702056","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000063246084,0.00026438065,0.26184535,0.0000029304476,0.0014001462,0.00008259034,0.73525816,0.00017166222,0.0009684719],"genre_scores_gemma":[0.00079642446,0.00007776983,0.00068277767,0.000037360194,0.00030677175,0.000009407117,0.9978025,0.000043869913,0.00024313352],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999001,0.0000104695755,0.00025614983,0.0002123621,0.00017704665,0.00034294216],"domain_scores_gemma":[0.9994278,0.0000678075,0.00004086194,0.00037386248,0.000023267969,0.000066400054],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007480399,0.00029341917,0.0003040908,0.00012017056,0.00005221216,0.00004461283,0.00022967823,0.0002481262,0.00030646127],"category_scores_gemma":[0.000005701084,0.00029320907,0.000082102866,0.00008863709,0.000015557134,0.00007110172,0.00005028506,0.00040154427,0.0009430981],"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":[8.556208e-7,0.0000022967245,2.0847471e-8,0.00008698783,0.000020250363,0.0000010821797,0.0000029503235,0.4969707,0.000005217211,0.00001321153,0.5028441,0.000052293133],"study_design_scores_gemma":[0.0000623628,0.0000084371095,4.4323425e-8,0.00010057713,0.000022316977,0.000004070901,0.0000016421905,0.5045649,0.000002193516,0.000021505859,0.49500936,0.00020258923],"about_ca_topic_score_codex":0.0001278871,"about_ca_topic_score_gemma":0.00004886037,"teacher_disagreement_score":0.26254433,"about_ca_system_score_codex":0.00006802865,"about_ca_system_score_gemma":0.000016164633,"threshold_uncertainty_score":0.999952},"labels":[],"label_agreement":null},{"id":"W3206913269","doi":"10.3390/en14206782","title":"A Comparative Analysis of the ARIMA and LSTM Predictive Models and Their Effectiveness for Predicting Wind Speed","year":2021,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":165,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive integrated moving average; Mean squared error; Computer science; Recurrent neural network; Artificial neural network; Wind speed; Autoregressive model; Time series; Artificial intelligence; Machine learning; Moving average; Grid; Deep learning; Statistics; Mathematics; Meteorology","score_opus":0.018426755935706522,"score_gpt":0.23088964951892343,"score_spread":0.2124628935832169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3206913269","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":[0.9908184,0.0015934657,0.005729518,0.000007533735,0.00012692042,0.00008430382,0.00008640684,0.00004716988,0.0015062994],"genre_scores_gemma":[0.99962395,0.00004891435,0.00022489465,0.000006122919,0.000033306584,0.000005788844,0.000013535512,0.000010413377,0.000033082048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999466,0.000052492145,0.00013741688,0.0001542519,0.000062925305,0.00012691706],"domain_scores_gemma":[0.9992471,0.00049833814,0.000039282873,0.00012303592,0.00006484148,0.00002741005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012059719,0.00012728386,0.0003281121,0.00006831013,0.00008881738,0.000025585512,0.000054894743,0.000046886937,0.0000015956952],"category_scores_gemma":[0.000028762903,0.00008991124,0.00008445317,0.0003304254,0.00007593072,0.00011391678,0.000060318755,0.00007016328,1.7542003e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017970175,0.0000072338257,0.0045769936,0.00007083153,0.0009910607,3.6989428e-7,0.0042909067,0.97675544,0.012119834,0.00085373706,0.0000058630408,0.00030973452],"study_design_scores_gemma":[0.00024761265,0.00002416955,0.019865654,0.00009124799,0.0002817352,0.0000024825413,0.0011596744,0.8647787,0.11272702,0.00067664764,0.000051650564,0.00009339471],"about_ca_topic_score_codex":0.000019574667,"about_ca_topic_score_gemma":0.00005532189,"teacher_disagreement_score":0.11197676,"about_ca_system_score_codex":0.000014579619,"about_ca_system_score_gemma":0.000014237067,"threshold_uncertainty_score":0.3666474},"labels":[],"label_agreement":null},{"id":"W3207379842","doi":"10.1049/gtd2.12265","title":"A decomposition‐based multi‐time dimension long short‐term memory model for short‐term electric load forecasting","year":2021,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Energy Load and Power Forecasting","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":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Term (time); Dimension (graph theory); Decomposition; Long short term memory; Computer science; Econometrics; Mathematics; Artificial intelligence; Artificial neural network; Physics","score_opus":0.03166775003189539,"score_gpt":0.25735563338497885,"score_spread":0.22568788335308346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207379842","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":[0.30688313,0.0006156195,0.69134367,0.000056874444,0.00024377981,0.00029481418,0.00017999754,0.00031018312,0.00007196361],"genre_scores_gemma":[0.9753876,0.00006732028,0.0134849,0.000053241027,0.00028806683,0.00012174169,0.010346917,0.00007690777,0.00017332009],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977898,0.00007239233,0.0006524776,0.0005293421,0.00040162852,0.0005543827],"domain_scores_gemma":[0.9989536,0.00007476431,0.000056451154,0.00028438878,0.00037901205,0.00025178125],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030838512,0.00040252594,0.00033714532,0.00009715816,0.00050687173,0.0001510278,0.00012623818,0.00028563308,0.00007195561],"category_scores_gemma":[0.000039732942,0.00042914075,0.00027725237,0.00039861293,0.000020609945,0.00035790013,0.000014856298,0.00023712464,0.000007173541],"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":[0.00003257834,0.00008188604,0.00006118578,0.00008224566,0.000024731522,0.000010007611,0.00009176435,0.45759597,0.50699466,0.00001426748,0.0005498604,0.034460846],"study_design_scores_gemma":[0.00055802113,0.00003704047,0.00015827401,0.000120698875,0.000069548514,0.00002063897,0.0000033186777,0.6204043,0.37820294,0.000012217471,0.00010473645,0.00030825243],"about_ca_topic_score_codex":0.000002414744,"about_ca_topic_score_gemma":0.00002059141,"teacher_disagreement_score":0.67785877,"about_ca_system_score_codex":0.00040465742,"about_ca_system_score_gemma":0.00019773183,"threshold_uncertainty_score":0.99981606},"labels":[],"label_agreement":null},{"id":"W3207621341","doi":"10.3390/en14206682","title":"Multiscale Decision-Making for Enterprise-Wide Operations Incorporating Clustering of High-Dimensional Attributes and Big Data Analytics: Applications to Energy Hub","year":2021,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","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":"University of Waterloo","funders":"","keywords":"Cluster analysis; Computer science; Big data; Flexibility (engineering); Scheduling (production processes); Data mining; Mathematical optimization; Machine learning; Mathematics","score_opus":0.03271652835149473,"score_gpt":0.26304259697389987,"score_spread":0.23032606862240512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207621341","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":[0.15068881,0.0008097027,0.8475089,0.00006728334,0.00030996682,0.00007451271,0.0003343798,0.00008956167,0.00011687886],"genre_scores_gemma":[0.81151605,0.00004200979,0.18775588,0.000061112914,0.00018682174,0.00005370761,0.00031074238,0.000027854485,0.00004583563],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900615,0.000014779355,0.00036908328,0.00029662522,0.00012872297,0.00018463838],"domain_scores_gemma":[0.99868995,0.0006428421,0.000040556923,0.00043565567,0.00012275747,0.00006820938],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000120199424,0.00014943558,0.00023079403,0.000120351404,0.00017269998,0.00007236888,0.00020485322,0.000056239478,0.000007758868],"category_scores_gemma":[0.00023212381,0.00015702857,0.000036365393,0.0002654982,0.000029539187,0.00014090116,0.0004038241,0.000055881417,6.7011393e-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":[0.000005207257,0.000012720594,0.00036587374,0.000037636826,0.00004621556,0.0000022743004,0.00007693945,0.93104684,0.0059098867,0.0020389273,0.00033036334,0.060127135],"study_design_scores_gemma":[0.00029465847,0.000023067989,0.00047375212,0.000369644,0.000043006115,0.000012162588,0.00015489578,0.97049516,0.020300824,0.00081284274,0.0067401496,0.0002798226],"about_ca_topic_score_codex":0.00006979642,"about_ca_topic_score_gemma":0.0017001644,"teacher_disagreement_score":0.6608272,"about_ca_system_score_codex":0.000023554967,"about_ca_system_score_gemma":0.000037941507,"threshold_uncertainty_score":0.6403439},"labels":[],"label_agreement":null},{"id":"W3207946173","doi":"10.1016/j.apenergy.2021.118011","title":"A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms","year":2021,"lang":"en","type":"article","venue":"Applied Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":118,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; Lakes Environmental (Canada)","funders":"Fundamental Research Funds for the Central Universities; National University's Basic Research Foundation of China","keywords":"Coal; Computer science; Stability (learning theory); Mode (computer interface); Support vector machine; Decomposition; Artificial neural network; Deep learning; Artificial intelligence; Machine learning; Algorithm; Data mining; Engineering","score_opus":0.027407092337067916,"score_gpt":0.2455491956576503,"score_spread":0.21814210332058237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207946173","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":[0.032833975,0.00036850688,0.96432424,0.0000024816813,0.000093008246,0.0000975861,0.000006390287,0.0003059746,0.001967839],"genre_scores_gemma":[0.6217621,0.000042348503,0.3777868,0.000027180697,0.00009547795,0.00010605278,0.00006516523,0.000062730054,0.00005217649],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989669,0.000013072406,0.00024989533,0.00029471482,0.00009571412,0.00037971488],"domain_scores_gemma":[0.99960333,0.00008307535,0.000054121327,0.000116879484,0.000049315997,0.00009330443],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000119106066,0.00022066121,0.00022272451,0.000077936915,0.00022080989,0.000060079376,0.00006929176,0.00010396757,0.0000040555533],"category_scores_gemma":[0.000014797899,0.00026018522,0.00005682971,0.000107553795,0.000021766467,0.000096210104,0.00006423531,0.00016353303,3.5812005e-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":[0.000008033633,0.000016517364,0.000008906228,0.00007272596,0.000034378772,0.000011358855,0.00012294616,0.8799086,0.0667449,0.0029038354,0.000007546254,0.050160233],"study_design_scores_gemma":[0.0004089283,0.000012807773,0.0000023980676,0.000052638854,0.0000241462,0.00012078432,0.00004809574,0.9076891,0.08987057,0.0003137482,0.0011792568,0.00027751699],"about_ca_topic_score_codex":0.000025263527,"about_ca_topic_score_gemma":0.00003541727,"teacher_disagreement_score":0.5889281,"about_ca_system_score_codex":0.000069341375,"about_ca_system_score_gemma":0.00002080995,"threshold_uncertainty_score":0.99998504},"labels":[],"label_agreement":null},{"id":"W3208783625","doi":"10.32920/ryerson.14663082.v1","title":"Estimating power consumption in City of Toronto: a case study","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Electricity; Electricity demand; Electricity generation; Renewable energy; Environmental economics; Consumption (sociology); Electricity retailing; Peak demand; Energy demand; Business; Economics; Electricity market; Power (physics); Engineering; Electrical engineering","score_opus":0.03022307324850706,"score_gpt":0.28226930144755247,"score_spread":0.25204622819904543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208783625","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":[0.985913,0.00083236495,0.0038258103,8.4843924e-7,0.0009860966,0.00018406699,0.000005306189,0.00014563726,0.008106888],"genre_scores_gemma":[0.98894125,0.000015176094,0.010917567,0.0000028788843,0.000034892335,0.00002399445,0.000011235795,0.000024985784,0.00002803944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990223,0.000039640472,0.00043250486,0.00022394484,0.00011918876,0.00016239076],"domain_scores_gemma":[0.9994997,0.000065939785,0.000060024053,0.00030054056,0.000033801305,0.00003999793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023590862,0.00019437924,0.0003407837,0.000036016725,0.000016831402,0.000035300123,0.00008936764,0.00014929575,0.0006469409],"category_scores_gemma":[0.000039852566,0.00020726032,0.0000638309,0.000037076505,0.0000112285015,0.00008247566,0.0002176642,0.00031997907,9.049514e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005420569,0.00030428823,0.16182806,0.0009806929,0.00020846374,0.003855144,0.019426757,0.805849,0.00038325734,0.000042511678,0.000031951124,0.007084437],"study_design_scores_gemma":[0.0010802869,0.0000955078,0.021159988,0.0016310494,0.00008971164,0.0008674811,0.011809391,0.96105075,0.001173912,0.000038006325,0.000018815133,0.0009851091],"about_ca_topic_score_codex":0.015406033,"about_ca_topic_score_gemma":0.040064797,"teacher_disagreement_score":0.15520173,"about_ca_system_score_codex":0.00012964806,"about_ca_system_score_gemma":0.000022173803,"threshold_uncertainty_score":0.99115044},"labels":[],"label_agreement":null},{"id":"W3208784669","doi":"10.32920/ryerson.14654613.v1","title":"Application of least squares support vector machines in medium-term load forecasting","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University","funders":"","keywords":"Support vector machine; Simulated annealing; Computer science; Artificial neural network; Least squares support vector machine; Cross-validation; Artificial intelligence; Genetic algorithm; Principal component analysis; Machine learning; Data mining; Algorithm","score_opus":0.017269809853002756,"score_gpt":0.2338139623001967,"score_spread":0.21654415244719394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208784669","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":[0.91815704,0.0012836977,0.0356229,0.00005518936,0.0016125971,0.00037850413,0.00005821909,0.0004749676,0.042356905],"genre_scores_gemma":[0.99627745,0.000060056715,0.0028185993,0.000010878529,0.00025462615,0.000080426886,0.00031861954,0.00006514953,0.00011417357],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984284,0.000023504279,0.0006253197,0.0003542267,0.0002742356,0.00029430134],"domain_scores_gemma":[0.99922985,0.000082291524,0.00012569701,0.00039512865,0.0000976199,0.00006938821],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023628924,0.00031904434,0.00047775553,0.00016340827,0.00002108686,0.000044242082,0.00027644148,0.00028398106,0.00021095182],"category_scores_gemma":[0.000060790346,0.00032660266,0.00013530198,0.00019501921,0.000028342969,0.00008622503,0.0002707634,0.0004951125,0.0000049692703],"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":[0.00002540811,0.0001287435,0.07486852,0.005704885,0.0002504123,0.000084921616,0.0040095523,0.7702255,0.018500045,0.0006013954,0.00033122694,0.12526935],"study_design_scores_gemma":[0.00060150924,0.0000432776,0.0266497,0.0014866389,0.00007599852,0.000049731934,0.00023868568,0.9396606,0.02909935,0.00023080423,0.0007982361,0.0010654483],"about_ca_topic_score_codex":0.000616437,"about_ca_topic_score_gemma":0.0023337747,"teacher_disagreement_score":0.1694351,"about_ca_system_score_codex":0.00012581279,"about_ca_system_score_gemma":0.0001586431,"threshold_uncertainty_score":0.9999186},"labels":[],"label_agreement":null},{"id":"W3209169461","doi":"10.1109/ccece53047.2021.9569134","title":"Very Short-Term Wind Speed Prediction Techniques Using Machine Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Saskatchewan","funders":"","keywords":"Mean absolute percentage error; Wind speed; Mean squared error; Random forest; Standard deviation; Computer science; Statistics; Wind power; Prediction interval; Mean absolute error; Moving average; Term (time); Decision tree; Interval (graph theory); Mathematics; Artificial intelligence; Meteorology; Engineering","score_opus":0.01831927143837693,"score_gpt":0.22758760913738563,"score_spread":0.2092683376990087,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209169461","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":[0.9485748,0.00047402916,0.0093882615,0.0000068696595,0.00056791387,0.000038167527,0.000005003573,0.0012263411,0.03971865],"genre_scores_gemma":[0.9936698,0.000092757946,0.005014755,0.000014362283,0.00025767996,3.6129813e-7,0.000050288098,0.000037022462,0.00086297287],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943066,0.000015782418,0.00015395308,0.00012680262,0.000095316034,0.0001774798],"domain_scores_gemma":[0.999798,0.000019049994,0.000009749881,0.000101891885,0.000025822417,0.000045483906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007584387,0.000112992464,0.00011000833,0.000057029625,0.00007272719,0.000043693868,0.00004304249,0.00007486138,0.0002322073],"category_scores_gemma":[0.000014782565,0.0001165408,0.000045600587,0.00014337116,0.000009860427,0.00014948608,0.000032282256,0.00020435183,0.0000044095864],"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":[0.0000045294078,0.00001896172,0.062485997,0.000081050064,0.00007251595,0.00009064817,0.00020593697,0.20533355,0.7150394,0.00011985944,0.00015874038,0.016388834],"study_design_scores_gemma":[0.00008059899,0.000020589512,0.0009928184,0.00011670075,0.000021854426,0.00010741187,0.000038549548,0.55876154,0.43221825,0.000020875472,0.007419535,0.00020126186],"about_ca_topic_score_codex":0.00001989067,"about_ca_topic_score_gemma":0.00001605977,"teacher_disagreement_score":0.353428,"about_ca_system_score_codex":0.000045889574,"about_ca_system_score_gemma":0.000011067732,"threshold_uncertainty_score":0.4752396},"labels":[],"label_agreement":null},{"id":"W3209466551","doi":"10.32920/ryerson.14656170.v1","title":"ANN-Based Day-Ahead Short Term Load Forecasting","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Electric power system; Reliability engineering; Term (time); Reliability (semiconductor); Artificial intelligence; Artificial neural network; Industrial engineering; Operations research; Machine learning; Engineering; Power (physics)","score_opus":0.032747771381765864,"score_gpt":0.2350733568164505,"score_spread":0.20232558543468465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209466551","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":[0.50636184,0.0028796277,0.122214116,0.00006875445,0.006780396,0.00040263816,0.00007115733,0.0029538986,0.3582676],"genre_scores_gemma":[0.988476,0.000040040857,0.009527057,0.000088701,0.0006474095,0.000059071797,0.0003738376,0.00015202096,0.00063586084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782646,0.00003742276,0.0005442205,0.0005819726,0.00039383097,0.00061610603],"domain_scores_gemma":[0.9987769,0.00013996773,0.0000489737,0.00071343407,0.0001329393,0.00018776755],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033094265,0.00057113334,0.0005526873,0.00013669209,0.00008857815,0.00025979176,0.0003966506,0.00049963954,0.00045073457],"category_scores_gemma":[0.00008106375,0.00059601816,0.0003074073,0.00018117555,0.0000352503,0.000097935685,0.0003408469,0.0009985795,0.000016611772],"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":[0.0000051487077,0.00004370247,0.0046184724,0.0011618999,0.00021827183,0.00029182987,0.0005304935,0.92106557,0.0022207047,0.000048351278,0.0018400473,0.06795549],"study_design_scores_gemma":[0.00021312837,0.000019861121,0.0006192359,0.0014179166,0.00007299382,0.000024137373,0.000070519,0.97710955,0.015173578,0.00005171646,0.0041492456,0.0010780904],"about_ca_topic_score_codex":0.000091020695,"about_ca_topic_score_gemma":0.0003234344,"teacher_disagreement_score":0.4821142,"about_ca_system_score_codex":0.00025206798,"about_ca_system_score_gemma":0.00022687326,"threshold_uncertainty_score":0.9996491},"labels":[],"label_agreement":null},{"id":"W3210205991","doi":"10.1109/ccece53047.2021.9569081","title":"Optimal Bidding Strategy in Day-Ahead Electricity Market for Large Consumers","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Electricity price forecasting; Volatility (finance); Econometrics; Electricity market; Electricity; Bidding; Exponential smoothing; Probabilistic forecasting; Economics; Computer science; Probabilistic logic; Microeconomics","score_opus":0.015077021965782181,"score_gpt":0.23586222268714688,"score_spread":0.2207852007213647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210205991","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":[0.6591939,0.0010820787,0.06613396,0.000038152397,0.00039453563,0.0001411399,0.000040628707,0.00033974394,0.27263588],"genre_scores_gemma":[0.9949387,0.000052947194,0.0031557605,0.000035848607,0.000042517408,0.000014265492,0.000025790605,0.00002475152,0.0017094195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921846,0.000018693698,0.0001719868,0.00014570072,0.0000607708,0.00038438383],"domain_scores_gemma":[0.99967235,0.00014949191,0.00001119539,0.00009081934,0.000024734662,0.000051418097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021301013,0.00011025577,0.00014282986,0.00006984774,0.00003746336,0.000034307297,0.00005904638,0.00007034887,0.00053286564],"category_scores_gemma":[0.000053818374,0.00011725181,0.000048478465,0.00025430872,0.0000050180865,0.00008991336,0.000015496835,0.00012345273,0.0000047639483],"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":[0.00010597377,0.00033534807,0.027589973,0.00078726705,0.00045981177,0.000397516,0.0009980743,0.69577676,0.06275164,0.055767976,0.063688606,0.09134105],"study_design_scores_gemma":[0.00093487604,0.000029837585,0.0019260421,0.000052853586,0.000011933325,0.000015164889,0.00019991767,0.9171224,0.052368805,0.00010514401,0.02689303,0.00033995844],"about_ca_topic_score_codex":0.000017103246,"about_ca_topic_score_gemma":0.00042903988,"teacher_disagreement_score":0.33574483,"about_ca_system_score_codex":0.000041703828,"about_ca_system_score_gemma":0.000031480962,"threshold_uncertainty_score":0.5834505},"labels":[],"label_agreement":null},{"id":"W3210332873","doi":"10.1109/ccece53047.2021.9569032","title":"Data-Driven Wind Speed Forecasting Techniques Using Hybrid Neural Network Methods","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Saskatchewan","funders":"","keywords":"Wind speed; Wind power; Particle swarm optimization; Artificial neural network; Computer science; Renewable energy; Wind power forecasting; Selection (genetic algorithm); Electricity generation; Power (physics); Meteorology; Electric power system; Artificial intelligence; Engineering; Machine learning; Electrical engineering; Geography","score_opus":0.09644099117483279,"score_gpt":0.32198988167733905,"score_spread":0.22554889050250626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210332873","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30407724,0.0020793537,0.58799344,0.000072478164,0.004139495,0.0002668644,0.000103594706,0.0031259477,0.09814157],"genre_scores_gemma":[0.21524401,0.000044782297,0.78288877,0.00012196651,0.0011925923,6.6183134e-7,0.00019192511,0.00008839539,0.00022688047],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986489,0.000086071195,0.00033167674,0.00032443256,0.00012422181,0.00048473163],"domain_scores_gemma":[0.9990694,0.00017949208,0.000043128024,0.00055910915,0.00005115104,0.00009775345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003902141,0.00022080363,0.00027156388,0.000081208535,0.00012954522,0.00010634721,0.00030200585,0.00006770181,0.0001725212],"category_scores_gemma":[0.00008805369,0.00022246857,0.000065001885,0.0004822603,0.000024747353,0.0003522311,0.00032841845,0.00025820557,0.0000034014756],"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":[0.000002632861,0.000008041362,0.0004757969,0.00005132523,0.000075574564,0.00015945193,0.000033549226,0.8999602,0.017896073,0.00012047797,0.0055966335,0.07562021],"study_design_scores_gemma":[0.00007223838,0.000008047434,0.000015937876,0.00007947655,0.000031833726,0.00028236496,0.000023449784,0.93915856,0.03719025,0.00015897065,0.022715183,0.00026370038],"about_ca_topic_score_codex":0.000025224934,"about_ca_topic_score_gemma":0.000024312614,"teacher_disagreement_score":0.19489534,"about_ca_system_score_codex":0.000030061718,"about_ca_system_score_gemma":0.000023058696,"threshold_uncertainty_score":0.9072005},"labels":[],"label_agreement":null},{"id":"W3210451168","doi":"10.1002/er.7374","title":"Optimal dispatching of renewable energy‐based urban microgrids using a deep learning approach for electrical load and wind power forecasting","year":2021,"lang":"en","type":"article","venue":"International Journal of Energy Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":56,"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":"Microgrid; Renewable energy; Reliability engineering; Wind power; Computer science; Grid; Electric power system; Load profile; Turbine; Automotive engineering; Power (physics); Electricity; Engineering; Electrical engineering","score_opus":0.03954852194790953,"score_gpt":0.29808657867339805,"score_spread":0.2585380567254885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210451168","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":[0.57750577,0.0043757004,0.41515464,0.000036075362,0.00035312612,0.000029775842,0.000007193776,0.000020053125,0.0025176655],"genre_scores_gemma":[0.95725477,0.000115912764,0.041902475,0.00001498643,0.00047086814,0.0000031013833,0.00001908411,0.000047340767,0.0001714767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99778587,0.00013620534,0.0005621881,0.00020191957,0.0008814211,0.00043241214],"domain_scores_gemma":[0.9975103,0.0005621449,0.00016559237,0.000094790696,0.0015192692,0.00014791847],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010164459,0.00016319126,0.00029869445,0.00044165007,0.00013260447,0.00013550208,0.0003226491,0.00011820526,0.000021694445],"category_scores_gemma":[0.0005190528,0.000161163,0.00016859102,0.00036139818,0.00007402242,0.00022995863,0.00010775615,0.0004439008,5.4193606e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013787656,0.00007976841,0.0017945253,0.00003181856,0.0002414121,0.000089152505,0.00019689536,0.9413141,0.048034716,0.0004992667,0.00011867092,0.0074617784],"study_design_scores_gemma":[0.00088289735,0.00016620253,0.000057425037,0.0002162146,0.000019263309,0.0003997637,0.00012383147,0.9351136,0.057739403,0.00012223524,0.0049987426,0.00016039878],"about_ca_topic_score_codex":0.00018055183,"about_ca_topic_score_gemma":0.000028726909,"teacher_disagreement_score":0.379749,"about_ca_system_score_codex":0.0002449504,"about_ca_system_score_gemma":0.00027736346,"threshold_uncertainty_score":0.6572036},"labels":[],"label_agreement":null},{"id":"W3210564383","doi":"10.1007/s42521-023-00075-z","title":"Deep learning algorithms for hedging with frictions","year":2023,"lang":"en","type":"article","venue":"Digital Finance","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Deep learning; Scalability; Computer science; Solver; Artificial intelligence; Algorithm; Focus (optics); Domain (mathematical analysis); Machine learning; Mathematics","score_opus":0.012403282833685527,"score_gpt":0.20676099869239156,"score_spread":0.19435771585870604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210564383","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":[0.5120037,0.0005338728,0.41155514,0.00004729366,0.0008000262,0.00019651509,0.00004094659,0.0029093926,0.0719131],"genre_scores_gemma":[0.99597085,0.000038044003,0.0014630203,0.000005271127,0.00010103655,0.000042273678,0.00005394291,0.000036678346,0.0022888768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952877,0.0000011029244,0.00007903414,0.00010668595,0.000053571344,0.0002308694],"domain_scores_gemma":[0.99982464,0.000056399676,0.000012732674,0.000066473294,0.000017517003,0.000022254231],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000259298,0.00008589943,0.000077173856,0.000049391623,0.000107747495,0.00006908402,0.00005441189,0.000021094962,0.0000016695059],"category_scores_gemma":[0.000022311404,0.00008227524,0.0000314018,0.0003133092,0.000011900118,0.00024077651,0.000011730698,0.00007947128,0.000042403397],"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":[0.000002767501,0.000003829662,0.00091816933,0.00003060207,0.000014695272,0.000010475771,0.00029470172,0.82393223,0.00005786807,0.0010932079,0.00029067174,0.1733508],"study_design_scores_gemma":[0.00020222196,0.000058803133,0.0012311836,0.00008108586,0.0000040100567,0.000012797584,0.00012434323,0.86539,0.0005229788,0.00026547542,0.13188408,0.00022300542],"about_ca_topic_score_codex":9.949066e-7,"about_ca_topic_score_gemma":0.000002230888,"teacher_disagreement_score":0.48396713,"about_ca_system_score_codex":0.000014447313,"about_ca_system_score_gemma":0.0000041540034,"threshold_uncertainty_score":0.3355087},"labels":[],"label_agreement":null},{"id":"W3210987464","doi":"10.1016/j.energy.2021.122367","title":"A Hybrid Optimized Model of Adaptive Neuro-Fuzzy Inference System, Recurrent Kalman Filter and Neuro-Wavelet for Wind Power Forecasting Driven by DFIG","year":2021,"lang":"en","type":"article","venue":"Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":78,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Adaptive neuro fuzzy inference system; Kalman filter; Computer science; Control theory (sociology); Engineering; Fuzzy logic; Artificial intelligence; Fuzzy control system","score_opus":0.026710264917081845,"score_gpt":0.20985001114739524,"score_spread":0.1831397462303134,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210987464","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":[0.61399233,0.0017440779,0.36487827,0.00009024007,0.0014910577,0.00031215185,0.00077613123,0.00043226234,0.01628348],"genre_scores_gemma":[0.98830074,0.000053938053,0.011085141,0.000070941576,0.00007722145,0.00003536791,0.00009672839,0.00007509668,0.00020481914],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859387,0.000049356015,0.00042340768,0.00037247245,0.00016774356,0.000393175],"domain_scores_gemma":[0.9990027,0.00038082525,0.0001109059,0.00024919573,0.00012437749,0.00013200917],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008883984,0.00029071848,0.0004173126,0.00008270574,0.000082481296,0.00004210881,0.00014160009,0.00007865624,0.000011404444],"category_scores_gemma":[0.00014197612,0.0003023161,0.00011565649,0.00011844454,0.0000381988,0.00015114096,0.00010447172,0.00015595727,5.3607175e-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":[0.00006941921,0.00003344779,0.000022253937,0.00018126036,0.00009422267,0.000032007138,0.00030028794,0.9751192,0.01197457,0.0048317066,0.0025663744,0.0047752913],"study_design_scores_gemma":[0.00076356024,0.0001145751,0.000005510142,0.00025010115,0.000033781205,0.000040311636,0.000042091942,0.97339976,0.022930441,0.00018208909,0.0019484755,0.0002892819],"about_ca_topic_score_codex":0.000022690294,"about_ca_topic_score_gemma":0.0000106865355,"teacher_disagreement_score":0.3743084,"about_ca_system_score_codex":0.00003757683,"about_ca_system_score_gemma":0.000041147865,"threshold_uncertainty_score":0.9999429},"labels":[],"label_agreement":null},{"id":"W3211933770","doi":"10.32920/ryerson.14644221.v1","title":"Analysis of the impacts of extreme weather events on Ontario’s electricity grid using agent-based modeling","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Extreme weather; Environmental science; Electricity; Meteorology; Storm; Grid; Winter storm; Climatology; Mains electricity; Engineering; Geography; Climate change; Geology; Electrical engineering","score_opus":0.04664533742974797,"score_gpt":0.23974919712898743,"score_spread":0.19310385969923946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211933770","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":[0.9356696,0.00017935199,0.061752807,0.0000035627745,0.00049644144,0.000095554286,0.000022326642,0.000046743266,0.0017336403],"genre_scores_gemma":[0.99814224,0.00001389541,0.0016246503,0.000019469753,0.000041069405,0.0000030619633,0.000049819,0.00003800877,0.000067786095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985029,0.00006899463,0.0005282236,0.00027024632,0.0003638986,0.00026571794],"domain_scores_gemma":[0.99900186,0.00004826072,0.00017898247,0.0006165976,0.00009859309,0.000055712873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025174057,0.0002912617,0.0006281474,0.00035727883,0.000037503873,0.000018600378,0.00029883033,0.00021200282,0.00023570629],"category_scores_gemma":[0.000030355877,0.00022661561,0.0006900906,0.0006662208,0.000010553179,0.00003401515,0.00013909637,0.00048199488,2.5595915e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006185379,0.000042069256,0.014787055,0.00010550596,0.0012350107,0.0000012583994,0.00030202238,0.978044,0.005236646,0.000008324773,0.000002719305,0.0002292175],"study_design_scores_gemma":[0.00011707981,0.0000090719295,0.002580124,0.00044487102,0.0009082995,3.090731e-7,0.000019956791,0.97378427,0.02191521,0.000015700909,0.0000041272547,0.00020099676],"about_ca_topic_score_codex":0.0342933,"about_ca_topic_score_gemma":0.035420645,"teacher_disagreement_score":0.062472668,"about_ca_system_score_codex":0.0003726769,"about_ca_system_score_gemma":0.00021877492,"threshold_uncertainty_score":0.9821804},"labels":[],"label_agreement":null},{"id":"W3212952282","doi":"10.1109/access.2021.3126747","title":"A Novel Hybrid Neural Network-Based Day-Ahead Wind Speed Forecasting Technique","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial neural network; Wind speed; Real-time computing; Artificial intelligence; Meteorology","score_opus":0.041774168833667365,"score_gpt":0.2583673391690957,"score_spread":0.21659317033542835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212952282","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":[0.7549366,0.00047885906,0.22490376,0.00009615225,0.0046525034,0.00031376528,0.000043666507,0.0010964196,0.013478319],"genre_scores_gemma":[0.9945467,0.0000049726555,0.003893644,0.00025463983,0.0010306815,0.000011588193,0.00004740147,0.000089410474,0.00012095928],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853826,0.00003082088,0.00033625786,0.0003076057,0.0001964736,0.0005906094],"domain_scores_gemma":[0.9992186,0.0001744437,0.00006101331,0.00033853133,0.00008193887,0.00012546973],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022493064,0.00027053145,0.00027418684,0.00007922448,0.0001331871,0.00021482866,0.00037111787,0.000096582866,0.00008283106],"category_scores_gemma":[0.00005814112,0.00029208622,0.000114948634,0.0004896023,0.000030672007,0.0003664282,0.0000726517,0.00035538813,0.0000072634803],"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":[0.0000059864283,0.000022184588,0.001432327,0.00008061188,0.000028464927,0.00018697926,0.00002371642,0.9595807,0.030469276,0.000021193882,0.0018474084,0.0063011455],"study_design_scores_gemma":[0.0004704168,0.000019099394,0.0003489376,0.0002548701,0.000025767362,0.00018489551,0.000004542803,0.755,0.23897092,0.00012543704,0.0041437573,0.0004513719],"about_ca_topic_score_codex":0.000038760405,"about_ca_topic_score_gemma":0.00005839109,"teacher_disagreement_score":0.23961015,"about_ca_system_score_codex":0.000054355194,"about_ca_system_score_gemma":0.000052203428,"threshold_uncertainty_score":0.99995315},"labels":[],"label_agreement":null},{"id":"W3212991002","doi":"10.2139/ssrn.3957452","title":"Bombardier Aftermarket Demand Forecast with Machine Learning","year":2021,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Group for Research in Decision Analysis; HEC Montréal; Bombardier (Canada)","funders":"","keywords":"Business; Computer science; Economics","score_opus":0.003626395448850131,"score_gpt":0.17584112800826093,"score_spread":0.1722147325594108,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3212991002","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":[0.87711614,0.030233335,0.06159956,0.00016375471,0.00048276738,0.00006058471,0.000004243957,0.00027620498,0.03006342],"genre_scores_gemma":[0.99254817,0.003855022,0.00034670607,0.000028853066,0.00029526898,0.0000030642275,0.000010685634,0.00005967874,0.0028525623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.997842,0.0000546864,0.00018698933,0.0001380389,0.00017681292,0.0016014539],"domain_scores_gemma":[0.9996709,0.000032583142,0.000041805037,0.00010732931,0.00004997438,0.000097398544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000518098,0.00018186486,0.00018288223,0.00006501881,0.00018181826,0.000078869816,0.00010718744,0.00005970956,0.000097768745],"category_scores_gemma":[0.000020438922,0.0001527158,0.00008207696,0.00016708334,0.000018131055,0.00017537009,0.00002320585,0.0018833013,0.000011441299],"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":[0.00043648953,0.00013221691,0.1345632,0.00022971621,0.003996692,0.0014264521,0.0017019119,0.45471463,0.008972003,0.032944012,0.00091055373,0.35997212],"study_design_scores_gemma":[0.01314579,0.0026964522,0.008191391,0.0015156271,0.00092299946,0.091453195,0.0047343173,0.3207149,0.024736503,0.0651861,0.46142232,0.0052803974],"about_ca_topic_score_codex":0.000006212325,"about_ca_topic_score_gemma":0.00075799297,"teacher_disagreement_score":0.46051177,"about_ca_system_score_codex":0.00026029116,"about_ca_system_score_gemma":0.0003294371,"threshold_uncertainty_score":0.81821114},"labels":[],"label_agreement":null},{"id":"W3215081746","doi":"10.1088/1742-6596/2042/1/012035","title":"Short-Term Load Forecasting in a microgrid environment: Investigating the series-specific and cross-learning forecasting methods","year":2021,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"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":"Benchmarking; Computer science; Benchmark (surveying); Probabilistic forecasting; Metric (unit); Recurrent neural network; Microgrid; Time series; Artificial neural network; Key (lock); Smart grid; Machine learning; Process (computing); Artificial intelligence; Data mining; Probabilistic logic; Engineering; Control (management)","score_opus":0.052737273906101,"score_gpt":0.2674441138982989,"score_spread":0.21470683999219792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215081746","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":[0.9785832,0.0030938722,0.016375648,0.000076818564,0.00039977167,0.000066867506,0.0000043055197,0.000039468894,0.0013600583],"genre_scores_gemma":[0.9650243,0.0010751779,0.033354297,0.00001209799,0.0004132964,0.0000051988336,0.0000047895214,0.000046978406,0.00006389494],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99823904,0.00017179453,0.00070765306,0.00020445709,0.0002701444,0.00040691043],"domain_scores_gemma":[0.99898404,0.0003131257,0.00025131032,0.0001605183,0.00018081481,0.0001101741],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000790191,0.0002828995,0.00042306966,0.000053537096,0.0002656648,0.00038565235,0.00020462225,0.00008786362,0.000024670244],"category_scores_gemma":[0.00021394581,0.00024018032,0.00010981062,0.00026291516,0.00024220932,0.0009881845,0.00014553785,0.0009094774,7.687164e-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":[0.000030667998,0.000023257171,0.031075211,0.00021944623,0.00011674638,0.0002529147,0.0099479295,0.0784271,0.13299125,0.0022147028,0.0000140908705,0.74468666],"study_design_scores_gemma":[0.001365334,0.00040080998,0.015268895,0.0030030103,0.00012034825,0.005786186,0.009832651,0.07053038,0.86465627,0.013403314,0.014151709,0.001481101],"about_ca_topic_score_codex":0.0000032405187,"about_ca_topic_score_gemma":0.0000145174445,"teacher_disagreement_score":0.7432056,"about_ca_system_score_codex":0.00009352053,"about_ca_system_score_gemma":0.000099459154,"threshold_uncertainty_score":0.979427},"labels":[],"label_agreement":null},{"id":"W3215399585","doi":"10.1016/j.seta.2021.101698","title":"A multi-layer extreme learning machine refined by sparrow search algorithm and weighted mean filter for short-term multi-step wind speed forecasting","year":2021,"lang":"en","type":"article","venue":"Sustainable Energy Technologies and Assessments","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Wind speed; Computer science; Algorithm; Wind power; Particle swarm optimization; Artificial intelligence; Engineering; Meteorology","score_opus":0.03872143989215312,"score_gpt":0.27350176222414485,"score_spread":0.23478032233199173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215399585","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":[0.39125314,0.0061830464,0.5989877,0.000177585,0.000320594,0.0003386201,0.00007723314,0.0017594757,0.0009026078],"genre_scores_gemma":[0.85102737,0.00067066634,0.1401262,0.000020082509,0.000042929503,0.000040129602,0.00025113646,0.000106063766,0.0077154357],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99802244,0.000035712546,0.0003299852,0.0005384417,0.00018511993,0.0008883073],"domain_scores_gemma":[0.9993111,0.000118239586,0.00005589454,0.00025494056,0.00016502316,0.00009480696],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023467671,0.00037332717,0.00036140208,0.00019793591,0.00042539125,0.00017626269,0.00018684244,0.0002929396,0.000018337345],"category_scores_gemma":[0.00008100843,0.0003696882,0.00006827543,0.00034150685,0.00009453556,0.00028537455,0.0004060754,0.00039537347,2.160165e-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":[0.000038125178,0.00016099891,0.006289064,0.00073086243,0.00043021762,0.00054041634,0.00022272777,0.008452809,0.021441532,0.0013722387,0.000557536,0.95976347],"study_design_scores_gemma":[0.0013702839,0.00014142395,0.00012637042,0.00011502333,0.000041853997,0.000048546444,0.0062527168,0.9523451,0.02622274,0.000117698335,0.012722671,0.00049562095],"about_ca_topic_score_codex":0.00014940703,"about_ca_topic_score_gemma":0.00007298363,"teacher_disagreement_score":0.95926785,"about_ca_system_score_codex":0.00009936451,"about_ca_system_score_gemma":0.000039484665,"threshold_uncertainty_score":0.9998755},"labels":[],"label_agreement":null},{"id":"W3215404817","doi":"10.1109/access.2021.3129449","title":"An Efficient Framework for Short-Term Electricity Price Forecasting in Deregulated Power Market","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"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":"Academy of Finland","keywords":"Electricity market; Term (time); Electricity; Electricity price forecasting; Computer science; Industrial organization; Econometrics; Business; Economics; Electrical engineering; Engineering","score_opus":0.027157702898239374,"score_gpt":0.28224237227910975,"score_spread":0.2550846693808704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215404817","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":[0.8494875,0.00024316963,0.14566664,0.00000958325,0.0007897919,0.0001586642,0.000010408129,0.00021334774,0.0034208964],"genre_scores_gemma":[0.99501425,0.000010662149,0.0045981184,0.00005464805,0.00016533157,0.00004285258,0.000022643351,0.00006242761,0.000029073188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986083,0.000036754005,0.00032489977,0.0003232558,0.00015435758,0.0005524326],"domain_scores_gemma":[0.9991892,0.00028668338,0.000035709665,0.00029317933,0.00008729345,0.000107910324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002392311,0.00021102959,0.00023953816,0.00013160499,0.00008669713,0.00017425268,0.00032101097,0.00018213174,0.00010157013],"category_scores_gemma":[0.00012223069,0.00022780824,0.00006167127,0.00071738334,0.000013690616,0.00023766002,0.000034110282,0.00027729495,0.000001292071],"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":[0.00007834534,0.0002243419,0.024607984,0.00032016577,0.00009293811,0.0001763307,0.00078137877,0.9378817,0.014489587,0.0007870984,0.0005449469,0.020015175],"study_design_scores_gemma":[0.00035304338,0.00005067522,0.016214203,0.0003242805,0.00001871554,0.00003554826,0.00003174008,0.8988517,0.08246405,0.0007952773,0.00036074666,0.0005000434],"about_ca_topic_score_codex":0.000008494965,"about_ca_topic_score_gemma":0.0000590084,"teacher_disagreement_score":0.14552675,"about_ca_system_score_codex":0.00009297435,"about_ca_system_score_gemma":0.000036114983,"threshold_uncertainty_score":0.92897505},"labels":[],"label_agreement":null},{"id":"W3215677444","doi":"10.1109/epec52095.2021.9621686","title":"Wind Speed Forecasting by Conventional Statistical Methods and Machine Learning Techniques","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Saskatchewan","funders":"","keywords":"Wind speed; Support vector machine; Autoregressive integrated moving average; Mean absolute percentage error; Wind power; Mean squared error; Time series; Artificial neural network; Intermittency; Computer science; Moving average; Wind power forecasting; Autoregressive–moving-average model; Autoregressive model; Artificial intelligence; Machine learning; Control theory (sociology); Electric power system; Power (physics); Engineering; Statistics; Mathematics; Meteorology","score_opus":0.027187787628386927,"score_gpt":0.28721748241990475,"score_spread":0.26002969479151783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215677444","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03380103,0.001884422,0.9003729,0.000060422368,0.0002855495,0.000063301595,0.00004033208,0.00069581834,0.0627962],"genre_scores_gemma":[0.35601553,0.000087826724,0.64014643,0.000061774146,0.00010437487,0.0000015766758,0.00021816583,0.00004830766,0.003316032],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993334,0.00006413924,0.00017737289,0.00015201622,0.00008441944,0.00018868218],"domain_scores_gemma":[0.9995464,0.0002762187,0.00001678486,0.000055655863,0.00002940558,0.000075535914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002592736,0.00011572229,0.00014266264,0.000031702642,0.00007584904,0.000050051814,0.00003345438,0.00005869551,0.0006965785],"category_scores_gemma":[0.00014054899,0.00011490231,0.000025136897,0.00008890211,0.000029719413,0.000073618205,0.00004208081,0.0002159181,0.00000259507],"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":[0.000017272614,0.000059609723,0.009981281,0.00038140937,0.0002585807,0.00017729987,0.0003202939,0.0077107395,0.33320227,0.02441416,0.006988327,0.61648875],"study_design_scores_gemma":[0.00032590653,0.00004997446,0.00020605561,0.000080916245,0.000029180901,0.0002491279,0.00008292184,0.56574845,0.31234008,0.0013750715,0.11912349,0.00038885963],"about_ca_topic_score_codex":0.000015659256,"about_ca_topic_score_gemma":0.000005746797,"teacher_disagreement_score":0.6160999,"about_ca_system_score_codex":0.000015344865,"about_ca_system_score_gemma":0.000008925209,"threshold_uncertainty_score":0.7627047},"labels":[],"label_agreement":null},{"id":"W3215684797","doi":"10.1109/epec52095.2021.9621457","title":"Direct Net Load Forecasting Using Adaptive Neuro Fuzzy Inference System","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Adaptive neuro fuzzy inference system; Renewable energy; Electric power system; Solar power; Computer science; Fuzzy logic; Environmental science; Power (physics); Fuzzy control system; Engineering; Artificial intelligence; Electrical engineering","score_opus":0.04050791694450452,"score_gpt":0.22435445958085629,"score_spread":0.18384654263635175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215684797","genre_codex":"other","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":[0.34539124,0.0007000969,0.035896562,0.000009185643,0.0014714762,0.00008175487,0.00001282655,0.0011722868,0.6152646],"genre_scores_gemma":[0.9912987,0.000010707171,0.008068005,0.00003813576,0.0002178712,0.0000047676863,0.0000056785957,0.00004773919,0.00030840182],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998868,0.000038455644,0.00026753146,0.00024589774,0.00019397034,0.0003861563],"domain_scores_gemma":[0.9992791,0.0002516499,0.00003655999,0.00021385247,0.00011494444,0.00010388529],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011992617,0.00021089832,0.00024140504,0.000048905873,0.00010776207,0.000080805454,0.00010688325,0.00007880334,0.00004849096],"category_scores_gemma":[0.00012771455,0.00021137482,0.00007418499,0.00034270482,0.000016675342,0.00018850785,0.0000754055,0.0001728709,0.000019663676],"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":[0.000008226108,0.000014200322,0.000883048,0.00025516094,0.00009705089,0.00041761211,0.00048667833,0.967576,0.012740538,0.006404289,0.00041151262,0.010705648],"study_design_scores_gemma":[0.00016967753,0.000018353363,0.000060514532,0.00033196088,0.000024848632,0.00014845139,0.00031591777,0.97058547,0.026304761,0.000030013576,0.0016993097,0.00031072236],"about_ca_topic_score_codex":0.00014105548,"about_ca_topic_score_gemma":0.00012607465,"teacher_disagreement_score":0.64590746,"about_ca_system_score_codex":0.00015249866,"about_ca_system_score_gemma":0.000076302706,"threshold_uncertainty_score":0.86196154},"labels":[],"label_agreement":null},{"id":"W3215708800","doi":"10.1109/epec52095.2021.9621650","title":"Wind Speed Forecasting Using ARMA and Neural Network Models","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"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 Saskatchewan","funders":"","keywords":"Wind speed; Wind power; Autoregressive–moving-average model; Artificial neural network; Recurrent neural network; Computer science; Autoregressive model; Time series; Feedforward neural network; Wind power forecasting; Chaotic; Electric power system; Data modeling; Feed forward; Power (physics); Moving average; Artificial intelligence; Machine learning; Engineering; Meteorology; Control engineering; Statistics; Mathematics; Geography","score_opus":0.05061649448786988,"score_gpt":0.2214827563895116,"score_spread":0.17086626190164173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215708800","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":[0.96147937,0.0010164883,0.008700513,0.000016496915,0.0006291549,0.000030458767,0.0000013648327,0.00019753142,0.027928611],"genre_scores_gemma":[0.98708576,0.000019950749,0.012131011,0.00008642232,0.0003902724,1.5705238e-7,0.000005180215,0.000033703822,0.00024754793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929553,0.00001262367,0.00015900929,0.00014222969,0.00007691519,0.000313663],"domain_scores_gemma":[0.999732,0.000050224633,0.000014889516,0.00009835361,0.000024034793,0.00008049761],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000713569,0.00012874369,0.00013706839,0.000023109917,0.00009022791,0.00006630018,0.00004028123,0.00007555071,0.000053950553],"category_scores_gemma":[0.000009233706,0.00013064785,0.000034512974,0.00016578754,0.000014090302,0.00020551699,0.000052290252,0.00017554782,9.769291e-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":[0.0000012227803,0.000001876418,0.00063280156,0.00002116241,0.000016832624,0.000039623916,0.000093320734,0.9935049,0.001324106,0.0004404863,0.00014046794,0.0037831918],"study_design_scores_gemma":[0.00013971905,0.0000046278196,0.000035160065,0.000046441066,0.000012119245,0.0001691036,0.000049734772,0.9970947,0.0012070258,0.000705263,0.00037569253,0.00016042076],"about_ca_topic_score_codex":0.000015051681,"about_ca_topic_score_gemma":0.0000179583,"teacher_disagreement_score":0.027681064,"about_ca_system_score_codex":0.00001518742,"about_ca_system_score_gemma":0.00000922551,"threshold_uncertainty_score":0.53276646},"labels":[],"label_agreement":null},{"id":"W3215742664","doi":"10.1109/epec52095.2021.9621652","title":"A Comparative Study of Hourly Wind Speed and Power Forecasting Using Deep Learning Networks, Weka Time Series, and ARIMA Algorithms for Smart Grid Integration","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Wind power; Autoregressive integrated moving average; Renewable energy; Wind power forecasting; Wind speed; Computer science; Smart grid; Time series; Data pre-processing; Grid; Electric power system; Engineering; Power (physics); Data mining; Machine learning; Meteorology; Electrical engineering","score_opus":0.03161232362483922,"score_gpt":0.24369100005001906,"score_spread":0.21207867642517986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215742664","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":[0.9586575,0.00060136145,0.038637996,0.000004797114,0.00027897235,0.00022681551,0.000005589628,0.000086550965,0.0015004214],"genre_scores_gemma":[0.9845923,0.000015461332,0.014935395,0.0000059376653,0.00011439501,0.00000215204,0.000041483447,0.000032791482,0.00026003722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990876,0.000047020094,0.00030892045,0.00022471866,0.00009727329,0.00023447908],"domain_scores_gemma":[0.9994862,0.00016645787,0.00007500933,0.00008433816,0.0001220451,0.00006596454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019533509,0.00019863278,0.00036033476,0.00006730681,0.00013432006,0.00008550878,0.00003882427,0.00007020375,0.00002354129],"category_scores_gemma":[0.000044242992,0.0001921505,0.00003257605,0.00019644607,0.000032975437,0.00027588123,0.000050588966,0.00018207467,2.7025482e-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":[0.000086520224,0.00008894574,0.0066437945,0.00007500525,0.00029708314,0.000019782761,0.010219264,0.9576012,0.008536442,0.00008785776,0.00006221192,0.016281892],"study_design_scores_gemma":[0.00071160856,0.00036270393,0.0010447645,0.00010242424,0.000053810993,0.00006139552,0.005012637,0.99043167,0.0017731233,0.000018288829,0.00021096335,0.00021660505],"about_ca_topic_score_codex":0.00005081067,"about_ca_topic_score_gemma":0.00020156508,"teacher_disagreement_score":0.032830473,"about_ca_system_score_codex":0.0000262292,"about_ca_system_score_gemma":0.000012785071,"threshold_uncertainty_score":0.783567},"labels":[],"label_agreement":null},{"id":"W3215784189","doi":"10.1109/epec52095.2021.9621596","title":"Energy System Observatory of Honduras","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Ontario Tech University","funders":"","keywords":"Firewood; Observatory; Fossil fuel; Electricity; Energy consumption; Environmental economics; Consumption (sociology); Energy planning; Renewable energy; Business; Computer science; Environmental science; Engineering; Economics; Electrical engineering; Waste management","score_opus":0.013471958863531313,"score_gpt":0.1776870224877926,"score_spread":0.1642150636242613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215784189","genre_codex":"other","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":[0.45975125,0.0025107083,0.014261502,0.000012790512,0.0012013831,0.000011902036,0.0000055569167,0.00058986,0.521655],"genre_scores_gemma":[0.9974909,0.000022115455,0.0010107609,0.000020998446,0.00005940864,0.0000016987616,0.0000052620235,0.000013951798,0.0013749277],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996468,0.000007897915,0.00012670583,0.000059507915,0.000062618936,0.0000965044],"domain_scores_gemma":[0.99978244,0.000022495647,0.000010195268,0.00012383395,0.000029193738,0.000031826585],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000026111788,0.000057514226,0.00009905679,0.000016998185,0.000011229615,0.0000052417977,0.000045944376,0.00003763094,0.00007876115],"category_scores_gemma":[0.0000036548736,0.000055145578,0.0000377963,0.0001096632,0.000006110707,0.000038976363,0.000014628632,0.000030209934,0.0000053210524],"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":[0.000004326624,0.000039409435,0.0056146504,0.0013626732,0.00026992717,0.00021157162,0.0004158513,0.08463748,0.32994738,0.53632176,0.013108233,0.02806675],"study_design_scores_gemma":[0.00020117933,0.000010268007,0.0006994183,0.00019569647,0.000014206926,0.000041885247,0.00042175173,0.06735868,0.85723656,0.00005533447,0.07353855,0.00022645951],"about_ca_topic_score_codex":0.000027294309,"about_ca_topic_score_gemma":0.000036803853,"teacher_disagreement_score":0.53773963,"about_ca_system_score_codex":0.000016837324,"about_ca_system_score_gemma":0.000012479656,"threshold_uncertainty_score":0.22487713},"labels":[],"label_agreement":null},{"id":"W3215993373","doi":"10.1007/s00202-021-01389-0","title":"A decomposition-based approximate entropy cooperation long short-term memory ensemble model for short-term load forecasting","year":2021,"lang":"en","type":"article","venue":"Electrical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Jiangxi Provincial Department of Science and Technology; National Natural Science Foundation of China","keywords":"Computer science; Entropy (arrow of time); Hilbert–Huang transform; Predictive power; Term (time); Robustness (evolution); Approximate entropy; Algorithm; Electric power system; Long short term memory; Sample entropy; Volatility (finance); Series (stratigraphy); Time series; Artificial intelligence; Machine learning; Power (physics); Mathematics; White noise; Econometrics","score_opus":0.018057986934244232,"score_gpt":0.2332291053916825,"score_spread":0.21517111845743828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215993373","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":[0.22877286,0.00070154323,0.76906765,0.000012418037,0.000289497,0.00022511504,0.000011667318,0.0005825878,0.00033668592],"genre_scores_gemma":[0.9659981,0.000027203085,0.033059403,0.000035422323,0.00028448598,0.00016996996,0.0002091141,0.00013758347,0.00007871928],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997988,0.000015607638,0.0004937103,0.00041972206,0.00027782013,0.0008051328],"domain_scores_gemma":[0.99910754,0.00022699246,0.00002528208,0.000259893,0.00017237234,0.00020792677],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018252029,0.0003952191,0.00039103706,0.00015201147,0.00016313267,0.00015016967,0.00016196362,0.00018812506,0.000018239041],"category_scores_gemma":[0.0001243071,0.0004539246,0.00019710773,0.0004475177,0.000011442603,0.00023008804,0.000030440176,0.00035966554,0.0000030957522],"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":[0.000015608553,0.000033076256,0.00010845365,0.0001792279,0.00005038073,0.000043420827,0.00007994952,0.791059,0.19953983,0.0003030613,0.00005675437,0.008531202],"study_design_scores_gemma":[0.00036217202,0.000038875165,0.000032762164,0.00012591963,0.000044855562,0.0000512288,0.0000020461084,0.81598485,0.18289287,0.000014876126,0.000028129301,0.00042143013],"about_ca_topic_score_codex":0.0000014323012,"about_ca_topic_score_gemma":0.00001191902,"teacher_disagreement_score":0.73722523,"about_ca_system_score_codex":0.0003166389,"about_ca_system_score_gemma":0.000122367,"threshold_uncertainty_score":0.99979126},"labels":[],"label_agreement":null},{"id":"W3216057549","doi":"10.1109/epec52095.2021.9621733","title":"Locational Marginal Price Forecasting Based on Deep Neural Networks and Prophet Techniques","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Electricity market; Electricity; Computer science; Electricity price forecasting; Profit (economics); Python (programming language); Artificial neural network; Econometrics; Electric power system; MATLAB; Economic forecasting; Time series; Demand forecasting; Operations research; Economics; Microeconomics; Artificial intelligence; Power (physics); Machine learning; Engineering","score_opus":0.011798665104938369,"score_gpt":0.19597500486960118,"score_spread":0.1841763397646628,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216057549","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":[0.06674183,0.00061623263,0.7957629,0.00026233323,0.00041880037,0.00017059798,0.0000035592568,0.0010488197,0.13497493],"genre_scores_gemma":[0.9788689,0.0000071589516,0.020509968,0.0002301568,0.00019356543,0.00001680639,0.000026720958,0.000024983136,0.0001217329],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993904,0.000014908288,0.00013176863,0.00015283226,0.00010299451,0.00020709158],"domain_scores_gemma":[0.999684,0.00009318926,0.000016168835,0.00010145976,0.0000449231,0.000060288017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008224094,0.00012300535,0.000093990966,0.00004185186,0.000069310314,0.000050718387,0.00004732841,0.00005926472,0.000101996004],"category_scores_gemma":[0.000027271984,0.00011515225,0.000025542788,0.00016062461,0.000017610844,0.000076255696,0.000021301481,0.00015272638,0.0000011152731],"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":[0.00000850963,0.000015853206,0.0013974581,0.000058638558,0.000010102588,0.000037967373,0.000021302505,0.92471,0.00033009084,0.0012805584,0.00020139097,0.071928166],"study_design_scores_gemma":[0.00009814584,0.000029393166,0.00040410878,0.000045117667,0.000004478639,0.00004542098,0.000010215636,0.99424607,0.0036926651,0.000044114542,0.0012399952,0.00014029765],"about_ca_topic_score_codex":0.0000032432022,"about_ca_topic_score_gemma":0.00001461854,"teacher_disagreement_score":0.9121271,"about_ca_system_score_codex":0.0000239771,"about_ca_system_score_gemma":0.0000113820215,"threshold_uncertainty_score":0.46957725},"labels":[],"label_agreement":null},{"id":"W3216409598","doi":"10.1109/epec52095.2021.9621614","title":"Probabilistic Imputation for High Resolution Univariate Electric Load Data with Large Gaps","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Yukon University","funders":"","keywords":"Imputation (statistics); Missing data; Univariate; Computer science; Probabilistic logic; Data mining; Multivariate statistics; Artificial intelligence; Machine learning","score_opus":0.014756263548120834,"score_gpt":0.22267989935920549,"score_spread":0.20792363581108464,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216409598","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":[0.09178322,0.0006252519,0.8940072,0.00016641701,0.00040809967,0.00029630715,0.00012014943,0.00066734734,0.011926003],"genre_scores_gemma":[0.98035777,0.000022590111,0.018046856,0.00003448023,0.00011318984,0.000014981412,0.000792809,0.000029892606,0.0005874458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993046,0.000014025803,0.00013091904,0.00020215273,0.000103502534,0.0002448135],"domain_scores_gemma":[0.99948305,0.00007356921,0.000019637951,0.00028892234,0.00009817321,0.00003662248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016340625,0.00009613384,0.000095384465,0.000032867654,0.000063356616,0.000036935133,0.000101992075,0.00004665646,0.00003445537],"category_scores_gemma":[0.00008270205,0.00008456779,0.000012801329,0.0002989851,0.0000043053046,0.00017701197,0.000032767755,0.00006841745,0.0000057926754],"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":[0.00011687685,0.0001431026,0.00017044155,0.0006996815,0.0002457713,0.000044149267,0.00031133424,0.6275915,0.014943128,0.3299258,0.011096977,0.014711221],"study_design_scores_gemma":[0.0007686763,0.00006582752,0.00028858194,0.00004891813,0.000047838883,0.000020192607,0.000021275842,0.9752247,0.0047447854,0.0021137162,0.016443308,0.00021222298],"about_ca_topic_score_codex":0.000032210763,"about_ca_topic_score_gemma":0.0003513029,"teacher_disagreement_score":0.88857454,"about_ca_system_score_codex":0.000076241915,"about_ca_system_score_gemma":0.000085549786,"threshold_uncertainty_score":0.34485745},"labels":[],"label_agreement":null},{"id":"W3216423019","doi":"10.3389/fenrg.2021.720406","title":"Forecasting Electricity Load With Hybrid Scalable Model Based on Stacked Non Linear Residual Approach","year":2021,"lang":"en","type":"article","venue":"Frontiers in Energy Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":21,"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":"Norges Forskningsråd; Department of Science and Technology, Ministry of Science and Technology, India","keywords":"Computer science; Residual; Convolutional neural network; Scalability; Time series; Mean squared error; Multilayer perceptron; Artificial intelligence; Perceptron; Deep learning; Artificial neural network; Pattern recognition (psychology); Algorithm; Machine learning; Statistics; Mathematics","score_opus":0.03351865259392544,"score_gpt":0.2506167934675062,"score_spread":0.21709814087358076,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3216423019","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":[0.19212511,0.00095143117,0.68816197,0.00006728641,0.00033097764,0.00018124767,0.000026720823,0.0002459374,0.11790934],"genre_scores_gemma":[0.9092927,0.00010234041,0.08791005,0.00005502833,0.0002050545,0.00008429783,0.00010967171,0.00011676307,0.0021240734],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99663186,0.00018651078,0.000319441,0.0005785408,0.0011007868,0.001182888],"domain_scores_gemma":[0.9988055,0.00015686818,0.000030043648,0.00050362496,0.00030701517,0.00019693993],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010766004,0.0002872804,0.00037996325,0.00053544674,0.00021657936,0.00009007685,0.00033903113,0.00015505747,0.000013035222],"category_scores_gemma":[0.0001852459,0.00027661744,0.00006153948,0.0015513981,0.00010582936,0.00016804379,0.00008036432,0.0009837227,0.000002223821],"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":[0.00018073016,0.000110487454,0.0010200933,0.000086929744,0.0000345902,0.00016865041,0.00009095217,0.9818768,0.00039676492,0.00014035654,0.008214376,0.0076792617],"study_design_scores_gemma":[0.00088887603,0.00012707255,0.000017831555,0.00015412124,0.0000058947217,0.000011645566,0.0000959015,0.9592714,0.03710424,0.00025450203,0.0017634415,0.00030508198],"about_ca_topic_score_codex":0.00014667393,"about_ca_topic_score_gemma":0.00010414715,"teacher_disagreement_score":0.7171676,"about_ca_system_score_codex":0.0005615502,"about_ca_system_score_gemma":0.0005459495,"threshold_uncertainty_score":0.9999686},"labels":[],"label_agreement":null},{"id":"W3217023249","doi":"10.4095/329257","title":"Comparing machine learning and linear regression methods for estimating marginal greenhouse gas emission factors of electricity generation with renewables","year":2021,"lang":"en","type":"report","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Greenhouse gas; Renewable energy; Electricity; Econometrics; Electricity generation; Linear regression; Regression; Environmental science; Environmental economics; Computer science; Machine learning; Economics; Statistics; Engineering; Mathematics; Power (physics); Ecology","score_opus":0.06024242736707241,"score_gpt":0.32860907563577935,"score_spread":0.26836664826870693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217023249","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1350874,0.004850729,0.85366714,0.0000046442856,0.00052013656,0.0002479443,0.000011380893,0.0003528374,0.0052578095],"genre_scores_gemma":[0.089869164,0.0014341507,0.9054264,0.0000013545123,0.00047442288,0.000017181199,0.0006966092,0.00015742284,0.001923312],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833244,0.00010011276,0.0005632212,0.0003779731,0.00031504978,0.00031119995],"domain_scores_gemma":[0.9987533,0.00037615755,0.0003288584,0.00016411109,0.00027110724,0.00010643137],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00077465613,0.00042415256,0.0007881762,0.00020922096,0.00020626908,0.00005284298,0.000083637344,0.00028455866,0.000021413683],"category_scores_gemma":[0.00056174706,0.00031598416,0.00009810582,0.00022898862,0.000023584113,0.00010420162,0.00006286877,0.00042020445,3.4139774e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007371948,0.000053850614,0.03221912,0.0052001905,0.0004445725,0.00001536427,0.00035303252,0.7125849,0.09320729,0.000017407672,0.0009509225,0.15487961],"study_design_scores_gemma":[0.00025668374,0.00012050911,0.000021888285,0.0013768005,0.00010993845,0.00006110474,0.000024896905,0.86561036,0.12774259,0.00000796703,0.0043358444,0.00033144487],"about_ca_topic_score_codex":0.0005015736,"about_ca_topic_score_gemma":0.00024208741,"teacher_disagreement_score":0.15454817,"about_ca_system_score_codex":0.00011754345,"about_ca_system_score_gemma":0.00014597674,"threshold_uncertainty_score":0.99992925},"labels":[],"label_agreement":null},{"id":"W3217663633","doi":"10.1109/epec52095.2021.9621395","title":"The Augmented Unscented H-infinity Transform with H-infinity Filtering for Effective Wind Speed Estimation in Wind Turbines","year":2021,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Calgary","funders":"","keywords":"Wind speed; Wind power; Control theory (sociology); Turbine; Robustness (evolution); Anemometer; Computer science; Unscented transform; Kalman filter; Engineering; Extended Kalman filter; Meteorology; Control (management); Artificial intelligence","score_opus":0.009105825444986658,"score_gpt":0.22006534130016328,"score_spread":0.2109595158551766,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217663633","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":[0.94301474,0.00014205913,0.030018713,0.00018159913,0.00050460594,0.00065312017,0.000024628005,0.00028603745,0.025174482],"genre_scores_gemma":[0.9975,0.00002315607,0.0018617458,0.000027266533,0.000051501043,0.000013516434,0.00007064193,0.000035642734,0.0004165173],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990627,0.000030383568,0.00025663857,0.00018423324,0.000130709,0.00033528928],"domain_scores_gemma":[0.9992689,0.0004050692,0.00002967535,0.0001672111,0.000071485025,0.000057690053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021767919,0.00020528841,0.0002018359,0.000057629466,0.00015772723,0.00007832007,0.00008940726,0.000072463925,0.000028696013],"category_scores_gemma":[0.00008611042,0.0001470271,0.00006260986,0.00033094038,0.00003308492,0.00018536174,0.000014055762,0.00018458221,0.0000032864784],"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":[0.00033478142,0.00009270893,0.0031611975,0.00042134165,0.00026157033,0.000030912644,0.0015314955,0.9364136,0.012863206,0.0014344407,0.00022267057,0.04323207],"study_design_scores_gemma":[0.0032865056,0.0001876654,0.013926335,0.00048585978,0.000052487474,0.00003306487,0.0004284348,0.8379712,0.13603601,0.00044190817,0.006642683,0.0005078175],"about_ca_topic_score_codex":0.0000574249,"about_ca_topic_score_gemma":0.0009131198,"teacher_disagreement_score":0.12317281,"about_ca_system_score_codex":0.00008595181,"about_ca_system_score_gemma":0.000029397024,"threshold_uncertainty_score":0.59955907},"labels":[],"label_agreement":null},{"id":"W4200256026","doi":"10.18280/ejee.230602","title":"Forecasting of Electricity Demand by Hybrid ANN-PSO under Shadow of the COVID-19 Pandemic","year":2021,"lang":"en","type":"article","venue":"European Journal of Electrical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":3,"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":"Electricity; Coronavirus disease 2019 (COVID-19); Term (time); Computer science; Pandemic; Shadow (psychology); Variable (mathematics); Consumption (sociology); Econometrics; Engineering; Economics; Mathematics; Medicine; Electrical engineering","score_opus":0.02219410416452954,"score_gpt":0.20909024587679373,"score_spread":0.18689614171226418,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200256026","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":[0.68549836,0.008441397,0.30360562,0.000091523536,0.00046500156,0.00007035466,0.000013205888,0.00008754882,0.0017269669],"genre_scores_gemma":[0.9984069,0.00023912682,0.0009928127,0.00009122677,0.00016455298,4.0068892e-7,0.0000023425698,0.00006221265,0.00004045821],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981647,0.00015912917,0.00082564563,0.00013654481,0.00033290486,0.0003810333],"domain_scores_gemma":[0.99876034,0.00039484046,0.00027365613,0.00018353379,0.00013257733,0.00025505992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085033034,0.00021407264,0.00040476967,0.00015878213,0.0000582412,0.000021464899,0.0003606608,0.000039438866,0.000023522203],"category_scores_gemma":[0.001274482,0.00017544132,0.00024251739,0.0007918485,0.000028502847,0.00010837604,0.00005857005,0.00065931806,6.439419e-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":[0.000017233826,0.00003117265,0.0011500296,0.00013226745,0.00014175948,0.000109398665,0.000097161326,0.8911441,0.10265419,0.000132677,0.001166011,0.0032239864],"study_design_scores_gemma":[0.0022044273,0.0004091925,0.0012880452,0.00043629005,0.00020584068,0.004823808,0.000034834284,0.76297647,0.21438125,0.00020524087,0.012328733,0.0007058879],"about_ca_topic_score_codex":0.0000038348658,"about_ca_topic_score_gemma":0.000001292755,"teacher_disagreement_score":0.31290847,"about_ca_system_score_codex":0.0001454871,"about_ca_system_score_gemma":0.00011797841,"threshold_uncertainty_score":0.71542895},"labels":[],"label_agreement":null},{"id":"W4200361452","doi":"10.1002/cpe.6772","title":"Multi‐step wind speed and wind power forecasting using variational momentum factor and deep learning based intelligent neural network models","year":2021,"lang":"en","type":"article","venue":"Concurrency and Computation Practice and Experience","topic":"Energy Load and Power Forecasting","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":"Trinity College","funders":"","keywords":"Artificial neural network; Wind speed; Wind power; Wind power forecasting; Heuristic; Computer science; Renewable energy; Process (computing); Power (physics); Artificial intelligence; Deep learning; Machine learning; Simulation; Electric power system; Engineering; Meteorology","score_opus":0.05216643408450233,"score_gpt":0.2863138410012601,"score_spread":0.23414740691675778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200361452","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":[0.77062416,0.0048512146,0.22385399,0.00003176396,0.00039420606,0.00006889929,0.0000026146872,0.00004323252,0.00012995131],"genre_scores_gemma":[0.9859936,0.00024695392,0.013571297,0.00008201282,0.000065830165,0.00000130265,0.000015384945,0.000014863072,0.000008716414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990291,0.0000767024,0.000251988,0.00028948183,0.00012918845,0.0002235609],"domain_scores_gemma":[0.99919355,0.00044417568,0.00009871611,0.00004888675,0.000099285775,0.00011541284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011832251,0.00017598404,0.00016050073,0.000042521773,0.0003226278,0.00017840351,0.000030105937,0.00006324547,0.00001620325],"category_scores_gemma":[0.00014006093,0.00018920071,0.000016850317,0.00014275787,0.0000649928,0.00090400456,0.000057257093,0.00021219505,2.4905862e-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":[0.000014128798,0.00001452052,0.0016520112,0.000042967764,0.00002100265,0.00001561934,0.009512956,0.962973,0.0004205842,0.00016501866,0.0000017389074,0.025166458],"study_design_scores_gemma":[0.00038076533,0.00004207411,0.00073810865,0.0000852646,0.000022218486,0.00011672916,0.0031010818,0.9947672,0.00008686753,0.00007302439,0.00037019255,0.00021645818],"about_ca_topic_score_codex":0.000013355868,"about_ca_topic_score_gemma":0.0000021002093,"teacher_disagreement_score":0.21536951,"about_ca_system_score_codex":0.000015732614,"about_ca_system_score_gemma":0.000023479224,"threshold_uncertainty_score":0.77153814},"labels":[],"label_agreement":null},{"id":"W4200442181","doi":"10.18280/jesa.540611","title":"Performance Improvement in Steam Turbine in Thermal Power Plants Using Artificial Neural Network","year":2021,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"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":"Artificial neural network; Computer science; Artificial intelligence; Steam turbine; Transformer; Turbine; MATLAB; Control engineering; Voltage; Engineering; Electrical engineering; Mechanical engineering","score_opus":0.018727150054815334,"score_gpt":0.22612276501260328,"score_spread":0.20739561495778794,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200442181","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":[0.9945181,0.0015481961,0.00013579417,0.000013248485,0.0014289445,0.00007781072,0.0000040735226,0.00010931037,0.0021645073],"genre_scores_gemma":[0.9980886,0.00010817132,0.0012107758,0.000042495685,0.00043331305,0.0000032090275,0.0000039871315,0.000059632002,0.000049816037],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980219,0.00011567746,0.00075983116,0.00017840594,0.00027305997,0.00065110537],"domain_scores_gemma":[0.99951303,0.000052120933,0.00012116555,0.00015902091,0.00004300094,0.0001116537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041884065,0.00024906607,0.0003455186,0.00016632762,0.00013089052,0.00014654709,0.00016873225,0.00008641959,0.0001085466],"category_scores_gemma":[0.000028563782,0.00023603506,0.000078415724,0.00041100426,0.000028124747,0.00037972868,0.00006778636,0.0005496408,0.0000113006545],"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":[0.000017012126,0.000035627196,0.012115171,0.00007955936,0.00002991608,0.0006930046,0.00048282067,0.8406807,0.025278276,0.000025003834,0.00004625864,0.12051661],"study_design_scores_gemma":[0.00044206314,0.00007904089,0.2680568,0.00079910026,0.000008759937,0.0007716217,0.00011967841,0.7258474,0.003403234,0.00009910862,0.00009282661,0.00028032993],"about_ca_topic_score_codex":0.000019478257,"about_ca_topic_score_gemma":0.00011166871,"teacher_disagreement_score":0.25594163,"about_ca_system_score_codex":0.0002789784,"about_ca_system_score_gemma":0.00006592537,"threshold_uncertainty_score":0.96252304},"labels":[],"label_agreement":null},{"id":"W4205105140","doi":"10.32920/ryerson.14668095","title":"Wind energy forecasts in calculation of expected energy not served","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Markov chain; Wind power; Energy (signal processing); Autoregressive–moving-average model; Computer science; Moving average; Benchmark (surveying); Power (physics); Mathematical optimization; Autoregressive model; Econometrics; Engineering; Mathematics; Statistics","score_opus":0.01933944317152492,"score_gpt":0.21376111925798336,"score_spread":0.19442167608645844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205105140","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":[0.9445293,0.00096201006,0.029157298,0.000018365896,0.0012620031,0.00005653027,0.000015516667,0.00029398096,0.023704985],"genre_scores_gemma":[0.99658006,0.00014385181,0.001840102,0.000031464504,0.0001577857,0.000020052385,0.00048664023,0.000071660215,0.0006683577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984151,0.000054365042,0.00063721236,0.00035547477,0.00023136394,0.00030644704],"domain_scores_gemma":[0.99924713,0.00006692646,0.00010421892,0.00041458374,0.00009303916,0.000074116215],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008889334,0.00033839233,0.0005221747,0.0002919472,0.000016586348,0.000038483962,0.0002004144,0.00047321586,0.00022722599],"category_scores_gemma":[0.000024307812,0.0003689157,0.0001735863,0.00030390176,0.000018683886,0.00009228305,0.00023770625,0.00027101912,5.453029e-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":[0.000011846124,0.00003329938,0.0005958596,0.0002173366,0.00010374544,0.00003208154,0.0006612196,0.97904485,0.0063338475,0.0017745952,0.00013438288,0.011056938],"study_design_scores_gemma":[0.0005761946,0.000021039137,0.003822772,0.0008647034,0.00003124586,0.00000965453,0.00009381122,0.8226336,0.16938123,0.00024728198,0.0015583676,0.0007600932],"about_ca_topic_score_codex":0.00396357,"about_ca_topic_score_gemma":0.0071590203,"teacher_disagreement_score":0.16304737,"about_ca_system_score_codex":0.00011170568,"about_ca_system_score_gemma":0.000062396364,"threshold_uncertainty_score":0.99987626},"labels":[],"label_agreement":null},{"id":"W4205179843","doi":"10.32802/asmscj.2021.609","title":"Time Series Long-Term Forecasting of per Capita Electricity Consumption for Bangladesh","year":2021,"lang":"en","type":"article","venue":"ASM Science Journal","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Per capita; Autoregressive integrated moving average; Electricity; Consumption (sociology); Economics; Time series; Estimation; Work (physics); Sustainable development; Agricultural economics; Business; Statistics; Population; Engineering; Mathematics; Demography","score_opus":0.021353553490463834,"score_gpt":0.24078440335143222,"score_spread":0.2194308498609684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205179843","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":[0.9926884,0.0005044775,0.005219855,0.000032222833,0.0004852363,0.0000397597,0.000004660459,0.00003381989,0.0009915226],"genre_scores_gemma":[0.99347275,0.000066057015,0.006024245,0.000011622322,0.00018139672,0.0000020929438,0.0000029414593,0.0000130325725,0.00022588504],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99900985,0.000010923548,0.00025142613,0.00012596895,0.00024900457,0.00035283732],"domain_scores_gemma":[0.99942255,0.00006528605,0.00008147511,0.00009496852,0.00023193286,0.00010376838],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054274447,0.0000956201,0.00014283748,0.0001227809,0.00028055854,0.00011321151,0.00018222057,0.00004023839,0.00022380015],"category_scores_gemma":[0.000120503704,0.000090557,0.00007124932,0.00022684234,0.00015143778,0.0006142074,0.000028824832,0.00016608197,0.000010628165],"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":[0.000028891083,0.000053276868,0.044183746,0.0002472049,0.00007075292,0.00007809515,0.0018324034,0.037511192,0.88286215,0.00077250006,0.0006666131,0.031693194],"study_design_scores_gemma":[0.0008296723,0.00023095553,0.035984237,0.00038628402,0.00006377888,0.0041441415,0.00014230498,0.14950214,0.80694133,0.0006798358,0.00055627583,0.00053903216],"about_ca_topic_score_codex":8.040221e-7,"about_ca_topic_score_gemma":0.000007271456,"teacher_disagreement_score":0.11199095,"about_ca_system_score_codex":0.000072044335,"about_ca_system_score_gemma":0.00013598456,"threshold_uncertainty_score":0.36928073},"labels":[],"label_agreement":null},{"id":"W4205226443","doi":"10.32920/ryerson.14668095.v1","title":"Wind energy forecasts in calculation of expected energy not served","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Markov chain; Wind power; Energy (signal processing); Autoregressive–moving-average model; Moving average; Computer science; Benchmark (surveying); Power (physics); Mathematical optimization; Autoregressive model; Econometrics; Engineering; Statistics; Mathematics","score_opus":0.01933944317152492,"score_gpt":0.21376111925798336,"score_spread":0.19442167608645844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205226443","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":[0.9445293,0.00096201006,0.029157298,0.000018365896,0.0012620031,0.00005653027,0.000015516667,0.00029398096,0.023704985],"genre_scores_gemma":[0.99658006,0.00014385181,0.001840102,0.000031464504,0.0001577857,0.000020052385,0.00048664023,0.000071660215,0.0006683577],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984151,0.000054365042,0.00063721236,0.00035547477,0.00023136394,0.00030644704],"domain_scores_gemma":[0.99924713,0.00006692646,0.00010421892,0.00041458374,0.00009303916,0.000074116215],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00008889334,0.00033839233,0.0005221747,0.0002919472,0.000016586348,0.000038483962,0.0002004144,0.00047321586,0.00022722599],"category_scores_gemma":[0.000024307812,0.0003689157,0.0001735863,0.00030390176,0.000018683886,0.00009228305,0.00023770625,0.00027101912,5.453029e-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":[0.000011846124,0.00003329938,0.0005958596,0.0002173366,0.00010374544,0.00003208154,0.0006612196,0.97904485,0.0063338475,0.0017745952,0.00013438288,0.011056938],"study_design_scores_gemma":[0.0005761946,0.000021039137,0.003822772,0.0008647034,0.00003124586,0.00000965453,0.00009381122,0.8226336,0.16938123,0.00024728198,0.0015583676,0.0007600932],"about_ca_topic_score_codex":0.00396357,"about_ca_topic_score_gemma":0.0071590203,"teacher_disagreement_score":0.16304737,"about_ca_system_score_codex":0.00011170568,"about_ca_system_score_gemma":0.000062396364,"threshold_uncertainty_score":0.99987626},"labels":[],"label_agreement":null},{"id":"W4205273169","doi":"10.1007/s10489-021-02864-8","title":"A novel decomposition-based ensemble model for short-term load forecasting using hybrid artificial neural networks","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Jiangxi Provincial Department of Science and Technology; National Natural Science Foundation of China","keywords":"Computer science; Residual; Artificial intelligence; Robustness (evolution); Machine learning; Artificial neural network; Algorithm","score_opus":0.06347100573431713,"score_gpt":0.2682691372499174,"score_spread":0.20479813151560028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205273169","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":[0.16368935,0.000077829354,0.83464223,0.0000053526837,0.0004417048,0.00028105197,0.000045149787,0.0002238116,0.0005935188],"genre_scores_gemma":[0.9728802,0.0000015410094,0.026373854,0.00009972357,0.00022886576,0.00023424675,0.00009068421,0.00008309629,0.000007807001],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998398,0.000011099896,0.00044002244,0.0003444593,0.00024281969,0.00056360645],"domain_scores_gemma":[0.9993492,0.00022009521,0.00005877969,0.00022293709,0.00004980774,0.00009919247],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030235024,0.00026522353,0.00023001281,0.00009073122,0.0005944246,0.00006924092,0.00028207822,0.000050907074,0.000029393253],"category_scores_gemma":[0.000013415721,0.0003233636,0.00012023048,0.00023094563,0.00003554268,0.0000774476,0.000083819694,0.00038598364,0.0000013118025],"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":[0.00005305136,0.0000337829,0.000015869304,0.000028921217,0.000017530583,0.0000040035316,0.0001656945,0.92776364,0.037786737,0.0010683546,0.00003589259,0.033026516],"study_design_scores_gemma":[0.00007471056,0.000030461228,8.86294e-7,0.000018420282,0.000026294916,0.000033150092,0.00006362904,0.960545,0.038246773,0.00057137007,0.000041997217,0.00034729924],"about_ca_topic_score_codex":0.000014241175,"about_ca_topic_score_gemma":0.000027241054,"teacher_disagreement_score":0.80919087,"about_ca_system_score_codex":0.00024086698,"about_ca_system_score_gemma":0.000054258377,"threshold_uncertainty_score":0.99992186},"labels":[],"label_agreement":null},{"id":"W4205588374","doi":"10.36227/techrxiv.14398304","title":"Predicting Rainfall using Machine Learning Techniques","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Ottawa","funders":"","keywords":"Computer science; Reliability (semiconductor); Machine learning; Set (abstract data type); Artificial intelligence","score_opus":0.018528462465508905,"score_gpt":0.23259972069757745,"score_spread":0.21407125823206855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205588374","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":[0.79573214,0.0044498146,0.09017236,0.000017890563,0.0018904814,0.00023613911,0.000016062988,0.0065945154,0.100890614],"genre_scores_gemma":[0.89855367,0.00035536126,0.099447496,0.000039222272,0.00064277806,0.000020470638,0.00019563969,0.00016792212,0.0005774668],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867857,0.00004567544,0.00038557706,0.0003406449,0.0001849118,0.0003646139],"domain_scores_gemma":[0.9994306,0.000059360413,0.00007569183,0.00029143374,0.00005637535,0.00008656467],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025021096,0.00036848857,0.00037832552,0.00015101625,0.00010684531,0.00019232304,0.0002113301,0.00038711866,0.0002087932],"category_scores_gemma":[0.00006876414,0.00039242412,0.00016387452,0.00013086174,0.00001866583,0.00009921855,0.0005410857,0.0014733347,0.0000021780893],"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":[0.000002296742,0.000013617605,0.017520988,0.0007921204,0.00022289858,0.00007695157,0.0009155043,0.94750917,0.022347266,0.00008296805,0.00003618913,0.01048004],"study_design_scores_gemma":[0.000061456114,0.000009325893,0.000034023105,0.0008784642,0.00004102656,0.000038781818,0.00009517669,0.9407292,0.054120254,0.0000459574,0.0034527383,0.00049360894],"about_ca_topic_score_codex":0.00072183006,"about_ca_topic_score_gemma":0.00019854915,"teacher_disagreement_score":0.102821514,"about_ca_system_score_codex":0.00011357802,"about_ca_system_score_gemma":0.000044553526,"threshold_uncertainty_score":0.9998528},"labels":[],"label_agreement":null},{"id":"W4206064875","doi":"10.4095/329423","title":"Forecasting of GIC indices for Canadian power utilities","year":2022,"lang":"en","type":"report","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Power (physics); Statistics; Econometrics; Environmental science; Economics; Mathematics; Physics; Thermodynamics","score_opus":0.05080548628465557,"score_gpt":0.24288362877927622,"score_spread":0.19207814249462066,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206064875","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015756821,0.0030586645,0.00009409594,0.000008243379,0.0036846413,0.00026520662,0.00087687623,0.00017529416,0.9760802],"genre_scores_gemma":[0.97609735,0.00024959116,0.001110862,0.000025396877,0.00036953366,0.00014196496,0.0006081981,0.00017760074,0.021219514],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984278,0.000009735474,0.00049253413,0.00020992136,0.00036189728,0.00049807713],"domain_scores_gemma":[0.99919623,0.00017785312,0.00012255566,0.00022384604,0.00013133194,0.00014817168],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00044148444,0.00028339308,0.00045299486,0.00046035758,0.00013913818,0.000025988067,0.00024091196,0.00022772339,0.0030535986],"category_scores_gemma":[0.00014860838,0.0002918138,0.00020797353,0.0001696293,0.000029105671,0.00006645075,0.00004294697,0.0003138891,0.0000012943624],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000045776127,0.000078906145,0.011036717,0.021156562,0.0036201063,0.0002603657,0.009690596,0.21612948,0.000076855715,0.0067618517,0.6261376,0.105005145],"study_design_scores_gemma":[0.00009912554,0.00006153518,0.000045376666,0.00018890521,0.000048532882,0.000045113116,0.00048886257,0.009117981,0.00018537484,0.0001081195,0.98922515,0.00038589764],"about_ca_topic_score_codex":0.06764325,"about_ca_topic_score_gemma":0.14054668,"teacher_disagreement_score":0.9603405,"about_ca_system_score_codex":0.0003928466,"about_ca_system_score_gemma":0.00089803187,"threshold_uncertainty_score":0.9999534},"labels":[],"label_agreement":null},{"id":"W4206269627","doi":"10.11121/ijocta.2022.1084","title":"Multi-objective regression modeling for natural gas prediction with ridge regression and CMARS","year":2022,"lang":"en","type":"article","venue":"An International Journal of Optimization and Control Theories & Applications (IJOCTA)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"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 Calgary","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Orta Doğu Teknik Üniversitesi; Çankaya Üniversitesi; University of Calgary","keywords":"Multivariate adaptive regression splines; Collinearity; Statistics; Econometrics; Regression; Mars Exploration Program; Time horizon; Regression analysis; Harvest season; Computer science; Mathematics; Polynomial regression; Mathematical optimization","score_opus":0.006787255679413508,"score_gpt":0.2363806886170678,"score_spread":0.22959343293765427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206269627","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":[0.042224213,0.0008090195,0.9558046,0.00026265896,0.0003542032,0.0002669976,0.00009014969,0.00005626217,0.0001319105],"genre_scores_gemma":[0.97848827,0.00040138653,0.020517394,0.000072310446,0.00021307485,0.00014595635,0.000103001825,0.000025392206,0.000033197495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991577,0.00004498267,0.000305316,0.00014637341,0.00023812472,0.00010747313],"domain_scores_gemma":[0.999195,0.000077228506,0.0001903302,0.0000872441,0.00038325175,0.0000669576],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025340822,0.00013293713,0.0001548762,0.00018300537,0.00032385817,0.000091955204,0.0001567586,0.00003706327,0.000012663925],"category_scores_gemma":[0.000024391635,0.000106089356,0.00004091075,0.000107496795,0.000037820322,0.0005116781,0.000029131159,0.00019227422,7.309607e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004027974,0.000043110376,0.000432315,0.000009613744,0.00011720601,0.0000014517633,0.0005850373,0.982889,0.0015426287,0.005301023,0.000017194716,0.008658597],"study_design_scores_gemma":[0.0019651523,0.00015293929,0.00007726168,0.000056584733,0.000049587477,0.00013939009,0.0007657442,0.99458593,0.00019710883,0.00077475474,0.0011195241,0.00011604578],"about_ca_topic_score_codex":0.0000029781374,"about_ca_topic_score_gemma":0.0000027533063,"teacher_disagreement_score":0.9362641,"about_ca_system_score_codex":0.000071806775,"about_ca_system_score_gemma":0.000027375485,"threshold_uncertainty_score":0.43261984},"labels":[],"label_agreement":null},{"id":"W4206520829","doi":"10.1109/access.2022.3142351","title":"Pandemic-Aware Day-Ahead Demand Forecasting Using Ensemble Learning","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":30,"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 Calgary","funders":"","keywords":"Computer science; Probabilistic forecasting; Boosting (machine learning); Probabilistic logic; Machine learning; Artificial intelligence; Quantile regression; Gradient boosting; Ensemble forecasting; Ensemble learning; Demand forecasting; Random forest; Data modeling; Scheduling (production processes); Operations research; Engineering","score_opus":0.06538795440001266,"score_gpt":0.27841064546116734,"score_spread":0.21302269106115468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206520829","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":[0.94332147,0.00043924645,0.04850894,0.0000055123737,0.0015980571,0.000090112975,0.0000080382115,0.00057577033,0.005452883],"genre_scores_gemma":[0.9989509,0.000018488186,0.0003015037,0.00004968293,0.0003684687,0.000027398677,0.000017720362,0.000082952785,0.0001828982],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99863005,0.00008196668,0.00028920977,0.00025151393,0.00025455738,0.0004927203],"domain_scores_gemma":[0.9994781,0.00015218196,0.00007530762,0.00017569946,0.000028521315,0.000090211644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040558763,0.00020923615,0.00022367801,0.00014384625,0.00063656695,0.0001353482,0.00038942732,0.000060644477,0.00017724966],"category_scores_gemma":[0.00003247675,0.00023938026,0.00007665805,0.00040513315,0.000018364257,0.00040802147,0.00019044867,0.0005747843,0.000005075355],"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":[0.0000049945784,0.000007340929,0.018874442,0.00004947075,0.000029778921,0.000038096237,0.00046703417,0.9616981,0.0069962246,0.000013714001,0.000243366,0.011577449],"study_design_scores_gemma":[0.00032964197,0.000031177806,0.00020127058,0.000071452545,0.000029010254,0.0001487522,0.00020734924,0.98149407,0.007498214,0.00018544374,0.00938594,0.00041765472],"about_ca_topic_score_codex":0.000091336486,"about_ca_topic_score_gemma":0.00003958182,"teacher_disagreement_score":0.055629447,"about_ca_system_score_codex":0.0001481727,"about_ca_system_score_gemma":0.000027897066,"threshold_uncertainty_score":0.97616434},"labels":[],"label_agreement":null},{"id":"W4206769499","doi":"10.1109/icjece.2021.3123091","title":"Stochastic Optimal Power Flow in Hybrid Power System Using Reduced-Discrete Point Estimation Method and Latin Hypercube Sampling","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":25,"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":"Latin hypercube sampling; Monte Carlo method; Mathematical optimization; Probabilistic logic; Random variable; Intermittency; Computer science; Probability distribution; Point estimation; Sampling (signal processing); Cumulative distribution function; Wind speed; Mathematics; Algorithm; Probability density function; Statistics","score_opus":0.010096349494229037,"score_gpt":0.20450749450826447,"score_spread":0.19441114501403545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206769499","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":[0.3808617,0.0012643592,0.617519,0.000013309558,0.00027737083,0.00002359105,0.0000023991436,0.000018105586,0.000020164243],"genre_scores_gemma":[0.84544545,0.0000047739572,0.15440509,0.000013187292,0.0001019415,6.282709e-7,0.0000016520505,0.000025966277,0.0000013198412],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900645,0.00002447098,0.00037161107,0.00014407969,0.000094202485,0.00035919275],"domain_scores_gemma":[0.9993207,0.00013294008,0.000041098723,0.000069857604,0.00005836099,0.00037704947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020108516,0.0001755115,0.0003041784,0.0003312688,0.000055461613,0.00011050534,0.00006609227,0.00006314034,0.0000052807522],"category_scores_gemma":[0.000058712492,0.00018341684,0.000050730236,0.00026720332,0.000009264689,0.00016553665,0.000015298161,0.0003606859,2.4532915e-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":[0.0000024148897,0.0000017866523,0.000048159196,0.000039434828,0.000032082484,0.00024268616,0.00021822043,0.99261886,0.0013506807,0.00025317,0.0000073864826,0.0051851417],"study_design_scores_gemma":[0.00022957244,0.000047918948,0.0006999365,0.00041278225,0.000019074108,0.002606648,0.000018987337,0.99525714,0.00044331394,0.000021651551,0.00004838917,0.0001945682],"about_ca_topic_score_codex":0.00006708946,"about_ca_topic_score_gemma":0.000025989639,"teacher_disagreement_score":0.46458372,"about_ca_system_score_codex":0.0001870449,"about_ca_system_score_gemma":0.00012352699,"threshold_uncertainty_score":0.74795216},"labels":[],"label_agreement":null},{"id":"W4206910610","doi":"10.3390/en15030810","title":"CBLSTM-AE: A Hybrid Deep Learning Framework for Predicting Energy Consumption","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":58,"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":"Engineering and Physical Sciences Research Council","keywords":"Overfitting; Computer science; Autoencoder; Robustness (evolution); Artificial intelligence; Deep learning; Mean squared error; Convolutional neural network; Machine learning; Energy consumption; Artificial neural network; Statistics","score_opus":0.011055219690164195,"score_gpt":0.2148248886772208,"score_spread":0.20376966898705662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206910610","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":[0.80505174,0.005381237,0.17941517,0.000038743266,0.0026875348,0.000094239644,0.000032452037,0.0018132201,0.0054856534],"genre_scores_gemma":[0.991723,0.00016943748,0.006822024,0.0000580939,0.0003911393,0.0002400332,0.000090012094,0.000072514966,0.00043375793],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990026,0.000044660133,0.0002193537,0.0001992718,0.00017784619,0.0003562873],"domain_scores_gemma":[0.9993927,0.00033598486,0.000054608707,0.00014241622,0.000019441197,0.000054835105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016828757,0.00016893301,0.00016783216,0.00010838913,0.0005042477,0.000049272137,0.00015753943,0.00004580577,0.00024835995],"category_scores_gemma":[0.000093006485,0.00019767035,0.000091763664,0.00012567815,0.000023785682,0.00010988041,0.000094483854,0.00030806492,0.0000031157294],"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":[0.000012687712,0.0000077138,0.0011134378,0.00004219902,0.00004399671,0.0000063563994,0.00043915908,0.95499074,0.0008827515,0.019985262,0.00036761095,0.022108104],"study_design_scores_gemma":[0.0004576943,0.00014399666,0.00020709404,0.00008155764,0.000041793013,0.000062774234,0.00067357847,0.7691526,0.014755517,0.005468981,0.20840155,0.00055291736],"about_ca_topic_score_codex":0.000023141612,"about_ca_topic_score_gemma":0.000009730197,"teacher_disagreement_score":0.20803393,"about_ca_system_score_codex":0.00007935643,"about_ca_system_score_gemma":0.00001019737,"threshold_uncertainty_score":0.8060763},"labels":[],"label_agreement":null},{"id":"W4210349096","doi":"10.1109/access.2022.3147602","title":"Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":212,"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":"Engineering and Physical Sciences Research Council; Thapar Institute of Engineering and Technology; Singapore University of Technology and Design; Queen's University; Soongsil University; Soonchunhyang University; Queen's University Belfast","keywords":"Wind power; Computer science; Renewable energy; Wind speed; Cloud computing; Time series; Real-time computing; Simulation; Artificial intelligence; Machine learning; Meteorology; Engineering","score_opus":0.025952135421046192,"score_gpt":0.24345322325600144,"score_spread":0.21750108783495525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210349096","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":[0.9265165,0.0003327061,0.06993217,0.000028090071,0.00074649905,0.00018362241,0.00012870715,0.00029378448,0.0018379423],"genre_scores_gemma":[0.99947554,0.000002801986,0.00006467036,0.0000340338,0.00013078966,0.00007578414,0.000091477385,0.00004456259,0.00008035999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928874,0.000013732586,0.00017687566,0.0001601601,0.00012706469,0.00023345083],"domain_scores_gemma":[0.9997472,0.00008501234,0.000023632774,0.00008865952,0.000018403018,0.000037086604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103926686,0.00012553358,0.00013477384,0.00013471703,0.000109165565,0.000088461005,0.00023663446,0.000030799874,0.000030797095],"category_scores_gemma":[0.000029810513,0.00013568763,0.000052383963,0.00021948048,0.0000081303915,0.000323306,0.000046568144,0.0002579294,0.0000010163169],"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":[0.000036887668,0.000017776718,0.00556183,0.000032152173,0.000012151825,0.0000055803357,0.0001699657,0.9924743,0.00007545471,0.0000062088225,0.000062031504,0.0015456356],"study_design_scores_gemma":[0.0005276907,0.000055248584,0.000058635876,0.000021216538,0.000005283187,0.000002032524,0.000033045573,0.99631506,0.00053499645,0.00017710529,0.0021140403,0.00015561737],"about_ca_topic_score_codex":0.00004968365,"about_ca_topic_score_gemma":0.000023098024,"teacher_disagreement_score":0.072959036,"about_ca_system_score_codex":0.00007021115,"about_ca_system_score_gemma":0.000018056035,"threshold_uncertainty_score":0.5533181},"labels":[],"label_agreement":null},{"id":"W4213036298","doi":"10.1049/tje2.12132","title":"Performance comparison of single and ensemble CNN, LSTM and traditional ANN models for short‐term electricity load forecasting","year":2022,"lang":"en","type":"article","venue":"The Journal of Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Boosting (machine learning); Artificial intelligence; Convolutional neural network; Machine learning; Ensemble forecasting; Artificial neural network; Term (time); Deep learning; Consistency (knowledge bases); Ensemble learning","score_opus":0.05183930200391334,"score_gpt":0.21468216976904286,"score_spread":0.16284286776512952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213036298","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":[0.9574438,0.0021851806,0.03984317,0.000009711921,0.00018329434,0.000059353744,0.000008413971,0.00002026259,0.0002468124],"genre_scores_gemma":[0.9978227,0.00007263032,0.001955709,0.000004189082,0.00011158497,0.0000035540736,0.0000017474827,0.000023014532,0.00000487535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922204,0.000011369615,0.0003367024,0.00005339806,0.00019563483,0.00018082795],"domain_scores_gemma":[0.99952865,0.00023587732,0.00008155264,0.0000557394,0.00004876251,0.00004940244],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047045748,0.0001118005,0.00022157416,0.000093378636,0.00012186158,0.000013178763,0.00010571849,0.000024134064,0.0000030986812],"category_scores_gemma":[0.000023823892,0.00009786615,0.000043404332,0.00010578874,0.00001534811,0.00018112766,0.000029525092,0.00024996203,1.816425e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000035517303,0.000015912072,0.00034721172,0.0001095788,0.000042466192,9.3340105e-7,0.0008139461,0.9480431,0.04448537,0.00007480505,0.00005888812,0.0059722485],"study_design_scores_gemma":[0.00024674027,0.00030520454,0.00040282714,0.00007440558,0.000039963867,0.00024150446,0.00008057421,0.9861173,0.012180439,0.00007844162,0.00012874938,0.00010384342],"about_ca_topic_score_codex":0.0000013878712,"about_ca_topic_score_gemma":9.760016e-7,"teacher_disagreement_score":0.040378895,"about_ca_system_score_codex":0.00006846844,"about_ca_system_score_gemma":0.00001539312,"threshold_uncertainty_score":0.39908656},"labels":[],"label_agreement":null},{"id":"W4213080213","doi":"10.1155/2022/9326856","title":"Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting","year":2022,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Energy Load and Power Forecasting","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":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Electricity; Energy storage; Electricity market; Scheduling (production processes); Artificial neural network; Artificial intelligence; Economics; Power (physics); Operations management; Engineering; Electrical engineering","score_opus":0.04041031459178597,"score_gpt":0.25716051277400115,"score_spread":0.2167501981822152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213080213","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":[0.21838981,0.0003408852,0.7802717,0.000077174016,0.00058095064,0.0002120653,0.00006328766,0.000029046516,0.00003507197],"genre_scores_gemma":[0.99047565,0.0000052784426,0.009012938,0.00028081323,0.00007416524,0.00007231143,0.00006267853,0.000010487037,0.000005669453],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990592,0.000050780443,0.00024933196,0.00021008086,0.00026508773,0.00016549703],"domain_scores_gemma":[0.99830455,0.0014483866,0.000070537855,0.00005332123,0.00007784382,0.000045350927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027532462,0.00010491821,0.00009580698,0.00007660828,0.00070867455,0.000059045273,0.00011728813,0.000017413335,0.0000051065495],"category_scores_gemma":[0.00009038771,0.000099297904,0.00002931465,0.00017668697,0.00011975762,0.00015253248,0.000045842207,0.00012698284,1.6024458e-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":[0.00002288332,0.000015873398,0.001121616,0.000026660231,0.0000025846966,0.0000013535563,0.00011471402,0.9944441,0.00041986463,0.0030185804,0.00001744678,0.00079436373],"study_design_scores_gemma":[0.00009953155,0.00013693205,0.0020516969,0.000024681483,0.000004810278,0.00004607823,0.00006024507,0.9967919,0.0001419571,0.000424543,0.00011672837,0.00010086795],"about_ca_topic_score_codex":0.00000387542,"about_ca_topic_score_gemma":0.0000013200814,"teacher_disagreement_score":0.77208585,"about_ca_system_score_codex":0.000028375514,"about_ca_system_score_gemma":0.000030068803,"threshold_uncertainty_score":0.54506236},"labels":[],"label_agreement":null},{"id":"W4220716594","doi":"10.3389/fenvs.2022.836050","title":"Capturing Spatial Influence in Wind Prediction With a Graph Convolutional Neural Network","year":2022,"lang":"en","type":"article","venue":"Frontiers in Environmental Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":22,"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 Calgary","funders":"","keywords":"Computer science; Wind power; Recurrent neural network; Wind speed; Convolutional neural network; Artificial neural network; Graph; Turbine; Artificial intelligence; Predictive power; Machine learning; Meteorology; Engineering","score_opus":0.002612730836339484,"score_gpt":0.14414867335734097,"score_spread":0.1415359425210015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220716594","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":[0.99680406,0.00024470105,0.0016462721,0.000008500815,0.0007695094,0.00009166025,0.000015251877,0.000034967536,0.00038508547],"genre_scores_gemma":[0.99849725,0.000012412828,0.001371613,0.000026153164,0.000040419392,0.000022013932,0.0000088141815,0.000009715332,0.0000115838975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99887383,0.000020250678,0.00014638974,0.00022718227,0.00036873153,0.00036358996],"domain_scores_gemma":[0.9998197,0.000007807962,0.000023473292,0.00009178171,0.0000010316359,0.000056189503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023136652,0.00009999462,0.00008631899,0.00014200565,0.00020219194,0.000018953444,0.00020929358,0.000018543422,0.00002936903],"category_scores_gemma":[0.0000030519031,0.00010675544,0.000014293414,0.00046293245,0.00029639027,0.00036756904,0.00009859319,0.000247664,6.9725564e-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":[0.00000794511,0.000008132747,0.41550753,0.0000012130658,0.0000011412197,0.000008210812,0.00018644614,0.5831552,0.00050433003,0.000004761097,0.000026096777,0.00058897637],"study_design_scores_gemma":[0.00028921032,0.000047215755,0.65415794,0.000014011345,0.0000017109289,0.000019122881,0.00025950468,0.3445788,0.000116170224,0.00008884199,0.00030217748,0.00012527833],"about_ca_topic_score_codex":0.00010664628,"about_ca_topic_score_gemma":0.000046220903,"teacher_disagreement_score":0.23865043,"about_ca_system_score_codex":0.0005156941,"about_ca_system_score_gemma":0.000016100172,"threshold_uncertainty_score":0.43533605},"labels":[],"label_agreement":null},{"id":"W4220817608","doi":"10.1109/oajpe.2022.3161101","title":"Day-Ahead Electricity Demand Forecasting Competition: Post-COVID Paradigm","year":2022,"lang":"en","type":"article","venue":"IEEE Open Access Journal of Power and Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":43,"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 Calgary; Simon Fraser University","funders":"Engineering and Physical Sciences Research Council; IEEE Foundation","keywords":"Coronavirus disease 2019 (COVID-19); Electricity demand; Competition (biology); Demand forecasting; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Electricity; Economics; Engineering; Electricity generation; Medicine; Virology; Operations management; Power (physics); Infectious disease (medical specialty); Biology","score_opus":0.03162101866033894,"score_gpt":0.28131117273396306,"score_spread":0.2496901540736241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220817608","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":[0.8679452,0.006449513,0.04264698,0.000785955,0.0050267167,0.00012325514,0.0000601577,0.000111042034,0.076851204],"genre_scores_gemma":[0.99843824,0.000269811,0.00019080112,0.0006151724,0.0002474486,0.000010244667,0.000009708207,0.000035227604,0.00018334998],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99856764,0.00011699157,0.0005152373,0.00016605311,0.00029793696,0.0003361303],"domain_scores_gemma":[0.99916744,0.0001510518,0.00021643707,0.00013797257,0.00006949728,0.0002575758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065949594,0.00020028125,0.00036291862,0.00024349439,0.00044855694,0.00054726045,0.0009949289,0.000046595145,0.00050486776],"category_scores_gemma":[0.000037389906,0.00019056628,0.00009696679,0.00037679536,0.00003291476,0.0012363268,0.0003163154,0.00036711185,6.5073334e-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":[0.0003766431,0.00020236228,0.002633863,0.00010775456,0.00048510358,0.0009940087,0.0015370188,0.92462206,0.0052433745,0.016858388,0.016686857,0.030252552],"study_design_scores_gemma":[0.006308251,0.0021145176,0.0028227263,0.00037278194,0.0002650913,0.011702298,0.00077697396,0.0855042,0.017394695,0.013539548,0.85690254,0.0022964051],"about_ca_topic_score_codex":0.00018373354,"about_ca_topic_score_gemma":0.000100059384,"teacher_disagreement_score":0.8402157,"about_ca_system_score_codex":0.00011260957,"about_ca_system_score_gemma":0.00009731793,"threshold_uncertainty_score":0.7771067},"labels":[],"label_agreement":null},{"id":"W4221025273","doi":"10.3390/su14053063","title":"The Investigation of Monthly/Seasonal Data Clustering Impact on Short-Term Electricity Price Forecasting Accuracy: Ontario Province Case Study","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Energy Load and Power Forecasting","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":"University of Waterloo","funders":"","keywords":"Cluster analysis; Electricity price forecasting; Electricity; Econometrics; Electricity market; Term (time); Correlation coefficient; Economics; Computer science; Statistics; Engineering; Mathematics; Artificial intelligence","score_opus":0.0327976381988421,"score_gpt":0.27501659235956755,"score_spread":0.24221895416072545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221025273","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":[0.99805844,0.000079348836,0.0002451906,0.000029121895,0.00017735965,0.000967507,0.000058792088,0.00013115237,0.00025307704],"genre_scores_gemma":[0.99965346,6.129367e-7,0.00006671553,0.000006096753,0.000058243037,0.000093173694,0.000052574695,0.00003177411,0.000037361526],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99789065,0.00025500605,0.00048954843,0.00042571558,0.0004220744,0.00051703036],"domain_scores_gemma":[0.99777746,0.00078025885,0.00012286134,0.0010225317,0.00018337689,0.00011351154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00191475,0.00025338616,0.00024546572,0.00007684271,0.0007837524,0.000078421865,0.0006237595,0.000039671813,0.000020360518],"category_scores_gemma":[0.00078517024,0.00020435419,0.00007673002,0.00041655693,0.00006863725,0.00034569565,0.0006964919,0.00066211744,1.4269816e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016288766,0.00015241087,0.49891987,0.00017985838,0.00010087407,0.00053920917,0.0077921674,0.47271666,0.00004448823,0.000024400426,0.000081775426,0.019285414],"study_design_scores_gemma":[0.00051192165,0.0010173189,0.13441482,0.000025661377,0.00007086958,0.00034310503,0.008838434,0.85327613,0.00024032545,0.0004259615,0.00035011818,0.00048531883],"about_ca_topic_score_codex":0.01650218,"about_ca_topic_score_gemma":0.04856162,"teacher_disagreement_score":0.3805595,"about_ca_system_score_codex":0.003112649,"about_ca_system_score_gemma":0.00093357323,"threshold_uncertainty_score":0.99004704},"labels":[],"label_agreement":null},{"id":"W4224055553","doi":"10.1108/imds-12-2021-0769","title":"Making the hospital smart: using a deep long short-term memory model to predict hospital performance metrics","year":2022,"lang":"en","type":"article","venue":"Industrial Management & Data Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"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 British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Recurrent neural network; Computer science; Autoregressive integrated moving average; Mean squared error; Mean absolute percentage error; Medical prescription; Autoregressive model; Time series; Artificial intelligence; Term (time); Artificial neural network; Machine learning; Data mining; Statistics; Medicine; Mathematics","score_opus":0.10195099930216678,"score_gpt":0.26220609533287564,"score_spread":0.16025509603070887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224055553","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":[0.9388218,0.0009361179,0.04022421,0.000054322947,0.012071952,0.0020705466,0.00047520382,0.00049803936,0.004847803],"genre_scores_gemma":[0.9978412,0.000023966366,0.00034028015,0.000025292655,0.0010542101,0.00018737321,0.00026651175,0.000087280736,0.00017392474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99732536,0.00008751882,0.00060392777,0.00053773564,0.00081973244,0.00062574074],"domain_scores_gemma":[0.99838066,0.000059515925,0.00010803378,0.0013173489,0.000026630312,0.000107837],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011308412,0.00033588544,0.0003166025,0.00034269734,0.0005446815,0.0003525928,0.0018337497,0.00009242507,0.00002154458],"category_scores_gemma":[0.00003617288,0.00031107516,0.00006684356,0.0010242667,0.000027391843,0.0006104686,0.002207518,0.0005664805,0.000009617921],"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":[0.000014107773,0.000041822765,0.0049018096,0.0001162587,0.00023743344,0.00006838239,0.00042373937,0.9722228,0.000009917015,0.000089193294,0.0067765876,0.015097916],"study_design_scores_gemma":[0.0004170657,0.00010612954,0.00025275897,0.0001845341,0.00015175235,0.000015818763,0.0009520394,0.9899572,0.000008478407,0.0000016202649,0.00752811,0.00042448562],"about_ca_topic_score_codex":0.000079168356,"about_ca_topic_score_gemma":0.0000070964165,"teacher_disagreement_score":0.059019353,"about_ca_system_score_codex":0.00037875457,"about_ca_system_score_gemma":0.000027487273,"threshold_uncertainty_score":0.99993414},"labels":[],"label_agreement":null},{"id":"W4224236046","doi":"10.1002/asmb.2681","title":"Prediction of electricity prices for non‐regulated markets based on a power transformed mean reverting process","year":2022,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Energy Load and Power Forecasting","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":"Stylized fact; Mean reversion; Econometrics; Electricity; Spot contract; Electricity market; Economics; Estimator; Volatility (finance); Electricity price forecasting; Jump process; Marginal likelihood; Jump; Statistics; Mathematics; Maximum likelihood; Financial economics; Futures contract","score_opus":0.016329370401966633,"score_gpt":0.20347762179296217,"score_spread":0.18714825139099553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224236046","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":[0.70339346,0.00002888108,0.29385427,0.00002019392,0.000104469684,0.0004318286,0.000053236934,0.00007003488,0.0020436477],"genre_scores_gemma":[0.9991958,0.0000018491129,0.00029265092,0.000031886306,0.000025273437,0.0003726766,0.000035565365,0.00003807928,0.0000062280283],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989071,0.000008029111,0.00034951876,0.00023654873,0.00021737281,0.0002814607],"domain_scores_gemma":[0.9996148,0.00009983765,0.000075297285,0.000113135866,0.00005199222,0.000044946984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027332906,0.00018541455,0.00025211836,0.00021096403,0.00013094477,0.000012477715,0.00010868886,0.00017794239,0.00001661341],"category_scores_gemma":[0.000016787008,0.00019236103,0.00002599497,0.000640075,0.00002499853,0.00009453929,0.00001556385,0.00041118157,3.8868844e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023438018,0.00006163058,0.000030279725,0.00023470093,0.000014468479,6.455008e-7,0.00052920094,0.9940722,0.0014557802,0.0005656775,0.000021624044,0.002779389],"study_design_scores_gemma":[0.0013297084,0.000061551276,0.0010298978,0.00015545095,0.000019661504,0.0000027809854,0.00016034719,0.9952719,0.0009646511,0.0008122993,0.000014307606,0.0001774255],"about_ca_topic_score_codex":0.000011468418,"about_ca_topic_score_gemma":0.0000030532526,"teacher_disagreement_score":0.29580233,"about_ca_system_score_codex":0.00006303603,"about_ca_system_score_gemma":0.00005459014,"threshold_uncertainty_score":0.7844255},"labels":[],"label_agreement":null},{"id":"W4224301794","doi":"10.21203/rs.3.rs-1552614/v1","title":"A Sustainable Climate Forecast System for Post-processing of Precipitation With Application of Machine Learning Computations","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Energy Load and Power Forecasting","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":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Flood myth; Random forest; Decision support system; Task (project management); Sustainable development; Precipitation; Computation; Machine learning; Data processing; Data mining; Meteorology; Systems engineering; Engineering; Algorithm","score_opus":0.022814317869594425,"score_gpt":0.3146150954653549,"score_spread":0.29180077759576045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224301794","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":[0.55535513,0.0037741577,0.42614433,0.00007723884,0.00019426862,0.005009446,0.0012245434,0.0007192413,0.0075016404],"genre_scores_gemma":[0.9920183,0.000039277173,0.005153411,5.5830685e-7,0.000051304345,0.000908672,0.0016586183,0.00008252031,0.000087359214],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981031,0.0001411515,0.00042920216,0.00028236533,0.0005445019,0.0004996834],"domain_scores_gemma":[0.99772966,0.00039492975,0.00021807967,0.00024105111,0.0013523574,0.00006391633],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013416836,0.00017717884,0.00033849097,0.0005319533,0.00033870543,0.00005455372,0.00024022989,0.00011477865,0.000008524163],"category_scores_gemma":[0.00012694826,0.00017729838,0.00008761048,0.0005521395,0.000059689566,0.000106658605,0.0002827337,0.0006899249,5.1147526e-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":[0.00010909725,0.00003033285,0.0012796731,0.03730097,0.00005082221,0.0000021464552,0.0023323353,0.9472684,0.0004717208,0.0033107898,0.000007831715,0.007835879],"study_design_scores_gemma":[0.00037172303,0.0003638854,0.0004319045,0.0014315749,0.00003102266,0.000003885866,0.010254385,0.98484755,0.0012304672,0.00016488363,0.00069024554,0.00017848484],"about_ca_topic_score_codex":0.00043718165,"about_ca_topic_score_gemma":0.000078334815,"teacher_disagreement_score":0.43666315,"about_ca_system_score_codex":0.00037296346,"about_ca_system_score_gemma":0.00020092711,"threshold_uncertainty_score":0.72300184},"labels":[],"label_agreement":null},{"id":"W4224868011","doi":"10.1049/rpg2.12479","title":"Applications of artificial intelligence in renewable energy systems","year":2022,"lang":"en","type":"article","venue":"IET Renewable Power Generation","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":19,"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":"Renewable energy; Computer science; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.016873982501590577,"score_gpt":0.22080628703639404,"score_spread":0.20393230453480346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224868011","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":[0.020142015,0.003418364,0.9506845,0.000029403798,0.0019764656,0.00029449287,0.000063042404,0.00023750936,0.023154194],"genre_scores_gemma":[0.99736965,0.000073048235,0.0009300313,0.000019161382,0.00019304287,0.0004156156,0.00014756415,0.00003290538,0.00081897527],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99873585,0.00006305593,0.0005005564,0.00021743434,0.00024165794,0.00024147227],"domain_scores_gemma":[0.999522,0.00003175887,0.0000781477,0.00027699152,0.000043664433,0.00004744074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028294223,0.00013961582,0.00018782233,0.0002448482,0.00013531794,0.000037575726,0.0001839575,0.00005962003,0.00017345046],"category_scores_gemma":[0.000008181321,0.00016755794,0.000045642108,0.00069455267,0.00001970972,0.00011829006,0.00005219003,0.00009935639,0.0000029985808],"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":[0.0000046675286,0.00003943196,0.00005304576,0.00001565535,0.000010516364,0.000001632905,0.00019977121,0.9538676,0.037690066,0.0037423002,0.0014611933,0.002914113],"study_design_scores_gemma":[0.000060100687,0.00005678848,0.0000039905567,0.000014004453,0.000007126943,0.000009042476,0.0003586355,0.8597482,0.06975586,0.00089523767,0.068872,0.00021902993],"about_ca_topic_score_codex":0.004161243,"about_ca_topic_score_gemma":0.002603201,"teacher_disagreement_score":0.9772276,"about_ca_system_score_codex":0.00011164768,"about_ca_system_score_gemma":0.00004730476,"threshold_uncertainty_score":0.6832814},"labels":[],"label_agreement":null},{"id":"W4225590053","doi":"10.23919/cjee.2022.000007","title":"Short-term Wind Power Prediction Based on Soft Margin Multiple Kernel Learning Method","year":2022,"lang":"en","type":"article","venue":"Chinese Journal of Electrical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"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":"National Natural Science Foundation of China","keywords":"Hinge loss; Multiple kernel learning; Margin (machine learning); Extreme learning machine; Kernel (algebra); Support vector machine; Computer science; Wind power; Radial basis function kernel; Artificial intelligence; Machine learning; Mathematical optimization; Control theory (sociology); Mathematics; Engineering; Kernel method; Artificial neural network","score_opus":0.005793528330879776,"score_gpt":0.212856702301847,"score_spread":0.2070631739709672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225590053","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":[0.71816283,0.0007566495,0.2785912,0.000030068146,0.0013969769,0.00009366112,0.000008028496,0.00030169153,0.0006589118],"genre_scores_gemma":[0.9961944,0.000013272288,0.0032685916,0.000023819219,0.00035919162,0.0000061176997,0.0000099929175,0.00008734151,0.000037269434],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983685,0.000065464585,0.00051602867,0.0001594924,0.0004914366,0.00039907562],"domain_scores_gemma":[0.9990445,0.00051247946,0.00008274069,0.00012926657,0.00005082708,0.00018021963],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006009382,0.00028509588,0.0003725234,0.0005144187,0.0001427655,0.000038992766,0.000239416,0.00007771106,0.0000962587],"category_scores_gemma":[0.00039713355,0.0002513985,0.00023312162,0.0007202984,0.0000058784976,0.00015517867,0.00003720202,0.0015847828,0.0000016810761],"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":[0.00005526595,0.000047539674,0.0093395505,0.000022901222,0.00006315566,0.000075858334,0.00010364124,0.9658303,0.01979485,0.000014815924,0.000089169116,0.0045629474],"study_design_scores_gemma":[0.0005791162,0.00037911654,0.013769568,0.000048912785,0.000023474082,0.0002098673,0.000007165432,0.98157316,0.000752886,0.000010023564,0.0024165295,0.0002301715],"about_ca_topic_score_codex":0.000001291272,"about_ca_topic_score_gemma":2.0573422e-7,"teacher_disagreement_score":0.27803156,"about_ca_system_score_codex":0.00028356677,"about_ca_system_score_gemma":0.000027419093,"threshold_uncertainty_score":0.9999938},"labels":[],"label_agreement":null},{"id":"W4225983762","doi":"10.46660/ijeeg.vol12.iss4.2021.654","title":"Using XGBoost Model with Feature Selection Techniques for Wind Speed Forecasting","year":2022,"lang":"en","type":"article","venue":"International Journal of Economic and Environmental Geology","topic":"Energy Load and Power Forecasting","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":"Simon Fraser University","funders":"","keywords":"Renewable energy; Wind power; Fossil fuel; Weir; Government (linguistics); Global warming; Environmental economics; Environmental science; Natural resource economics; Computer science; Climate change; Business; Engineering; Economics; Geography; Waste management; Cartography; Geology; Oceanography; Electrical engineering","score_opus":0.017109172782618566,"score_gpt":0.21347165239385363,"score_spread":0.19636247961123507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225983762","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":[0.98717684,0.00010652467,0.011686931,0.00006873428,0.00039191745,0.000047026184,0.000027050639,0.000010080326,0.00048487913],"genre_scores_gemma":[0.99054164,0.000026480517,0.009021372,0.000049827388,0.0002022438,0.0000011648083,0.000013873363,0.000014806783,0.00012858932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963176,0.0000068312706,0.00015180452,0.000068138324,0.000048846996,0.000092587725],"domain_scores_gemma":[0.99982435,0.000023289303,0.000100945734,0.000020322259,0.0000046511395,0.000026468288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008805969,0.00007345439,0.000101128644,0.000086481676,0.00006704739,0.000010908111,0.00008348852,0.00002871031,0.000055977882],"category_scores_gemma":[0.0000015102995,0.000072881405,0.000034595167,0.000007826214,0.000029748144,0.00011704051,0.00003816645,0.00013111155,1.7266385e-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":[0.00008562617,0.00000841296,0.0056820377,0.0000023316843,0.00009226038,0.0000059129006,0.00008849978,0.9867238,0.0044750595,0.00011323548,0.00012166888,0.0026011781],"study_design_scores_gemma":[0.0004246863,0.00016313467,0.00013263401,0.000008065761,0.000016514718,0.002042243,0.000144775,0.99157345,0.001587067,0.00046372757,0.0033498902,0.0000937819],"about_ca_topic_score_codex":0.000004734918,"about_ca_topic_score_gemma":0.0000043737127,"teacher_disagreement_score":0.0055494034,"about_ca_system_score_codex":0.00022403522,"about_ca_system_score_gemma":0.00001166672,"threshold_uncertainty_score":0.29720175},"labels":[],"label_agreement":null},{"id":"W4226154885","doi":"10.1109/tsg.2022.3171499","title":"A Novel Hybrid Method for Short-Term Probabilistic Load Forecasting in Distribution Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; SaskPower","keywords":"Benchmark (surveying); Probabilistic logic; Smart meter; Computer science; Markov chain Monte Carlo; Probabilistic forecasting; Context (archaeology); Monte Carlo method; Term (time); Distributed generation; Range (aeronautics); Grid; Smart grid; Uncertainty quantification; Mathematical optimization; Data mining; Artificial intelligence; Machine learning; Renewable energy; Bayesian probability; Engineering; Mathematics; Statistics","score_opus":0.027884799361893038,"score_gpt":0.24376797335634917,"score_spread":0.21588317399445614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226154885","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":[0.036375135,0.000053899836,0.95906466,0.000023842573,0.0029416045,0.00041815345,0.0006451931,0.000253393,0.00022413101],"genre_scores_gemma":[0.9933166,0.000008067978,0.0053144004,0.000029042701,0.00020484794,0.00085021014,0.0001517134,0.000060307986,0.00006480293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99860364,0.000042495998,0.00038228574,0.00030490238,0.00021101625,0.00045566927],"domain_scores_gemma":[0.99934185,0.00030368334,0.000032184907,0.00020259077,0.000039470062,0.00008022612],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004911519,0.00022729089,0.00023991363,0.00010657119,0.00034193127,0.00003392792,0.00015198732,0.00005409998,0.00004892949],"category_scores_gemma":[0.000009814373,0.0002635507,0.00015625918,0.0003621248,0.000019401554,0.00011366718,0.0000030634028,0.0005044505,0.0000014050185],"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":[0.000058751146,0.00010121405,0.000033064025,0.000050027367,0.00003785971,0.000006933186,0.00009774195,0.97269446,0.0008855527,0.00003545672,0.00019292922,0.025806006],"study_design_scores_gemma":[0.000559319,0.00012779477,0.00006975902,0.0000531069,0.000048953865,0.0001025682,0.000031375344,0.9924008,0.0026714671,0.00004197092,0.0035992833,0.00029355852],"about_ca_topic_score_codex":0.00005611481,"about_ca_topic_score_gemma":0.00025873247,"teacher_disagreement_score":0.9569415,"about_ca_system_score_codex":0.000522594,"about_ca_system_score_gemma":0.000040767274,"threshold_uncertainty_score":0.99998164},"labels":[],"label_agreement":null},{"id":"W4226427243","doi":"10.1016/j.ijepes.2022.108092","title":"QCAE: A quadruple branch CNN autoencoder for real-time electricity price forecasting","year":2022,"lang":"en","type":"article","venue":"International Journal of Electrical Power & Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Autoencoder; Electricity price forecasting; Electricity; Computer science; Artificial intelligence; Electricity market; Artificial neural network; Engineering; Electrical engineering","score_opus":0.01167342213992981,"score_gpt":0.22406855255277228,"score_spread":0.21239513041284247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226427243","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":[0.270948,0.009888022,0.64343643,0.0005584963,0.022190852,0.0006194745,0.00018834374,0.0007701182,0.05140027],"genre_scores_gemma":[0.9964967,0.00008823869,0.00082798925,0.00009414515,0.0011334108,0.000056936213,0.000029578034,0.00008135537,0.0011916676],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99699867,0.0001271965,0.0010669866,0.00023507359,0.0010134008,0.0005586952],"domain_scores_gemma":[0.9980977,0.0005653291,0.0004824238,0.00014823471,0.0005255788,0.00018069008],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007913587,0.00027468216,0.00047705258,0.0005552854,0.00019540431,0.00012682826,0.00079260155,0.00009886902,0.00012149168],"category_scores_gemma":[0.00020812746,0.00027058605,0.00033257532,0.0005813144,0.00001838393,0.0002697723,0.00007427409,0.0004636474,0.000003527675],"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":[0.00040138522,0.00023744677,0.00030951897,0.000027276314,0.0009803097,0.00018398768,0.00035885512,0.9207888,0.029944114,0.016383393,0.023435509,0.006949396],"study_design_scores_gemma":[0.0013011489,0.00064958644,0.000057854122,0.00006738567,0.000044155368,0.0016836644,0.000033880748,0.8260256,0.0023749918,0.0007519184,0.16657034,0.0004394465],"about_ca_topic_score_codex":0.00014067898,"about_ca_topic_score_gemma":0.0000035903538,"teacher_disagreement_score":0.7255487,"about_ca_system_score_codex":0.000756744,"about_ca_system_score_gemma":0.00016187291,"threshold_uncertainty_score":0.9999746},"labels":[],"label_agreement":null},{"id":"W4226449636","doi":"10.1109/access.2022.3160484","title":"Solar Power Forecasting Using Deep Learning Techniques","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":220,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Saint Mary's University","funders":"","keywords":"Mean absolute percentage error; Photovoltaic system; Computer science; Artificial intelligence; Mean squared error; Perceptron; Deep learning; Artificial neural network; Machine learning; Multilayer perceptron; Electricity; Statistics; Engineering; Mathematics; Electrical engineering","score_opus":0.03364399308681013,"score_gpt":0.26042314439119346,"score_spread":0.22677915130438334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226449636","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":[0.93010277,0.00045004004,0.04739884,0.000008112935,0.0014332145,0.00011878799,0.0000048095826,0.001262853,0.019220557],"genre_scores_gemma":[0.99702793,0.000010095457,0.0025110682,0.000059014066,0.00019325715,0.000034604695,0.0000068406925,0.000080731756,0.00007645614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989261,0.00004678554,0.00022992733,0.0001925311,0.0002168061,0.0003878463],"domain_scores_gemma":[0.99963963,0.000059632428,0.0000588499,0.00015507129,0.00002618016,0.00006064601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002600818,0.00017298851,0.00016494124,0.00016123278,0.00049501366,0.00012637522,0.0003882405,0.000049378777,0.0003040559],"category_scores_gemma":[0.000024357085,0.00019935855,0.00006985937,0.00037117157,0.000017205368,0.0004110404,0.00018121734,0.0005245317,0.000003216578],"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":[0.000006142779,0.000012746921,0.003347628,0.000039014933,0.00003488356,0.000060705803,0.000628959,0.9526774,0.014777754,0.000046559202,0.00017190268,0.028196286],"study_design_scores_gemma":[0.00021182466,0.00007961492,0.0001230838,0.00007387941,0.000028034416,0.00019936034,0.00024103894,0.85441303,0.09500277,0.00027352432,0.0486675,0.0006863492],"about_ca_topic_score_codex":0.000047362602,"about_ca_topic_score_gemma":0.000008047342,"teacher_disagreement_score":0.0982644,"about_ca_system_score_codex":0.00012259776,"about_ca_system_score_gemma":0.000012653133,"threshold_uncertainty_score":0.81296057},"labels":[],"label_agreement":null},{"id":"W4226453959","doi":"10.2139/ssrn.4018993","title":"A Deep Learning Method Based on Bidirectional Wavenet for Voltage Sag State Estimation Via Limited Monitors in Power System","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Voltage sag; Estimation; State (computer science); Computer science; Power (physics); Artificial intelligence; Voltage; Engineering; Power quality; Electrical engineering; Algorithm; Physics; Systems engineering","score_opus":0.005475468538810303,"score_gpt":0.21907216903567678,"score_spread":0.21359670049686647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226453959","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":[0.1268702,0.0005563094,0.87113273,0.000025056144,0.0006761617,0.00014380974,0.000005230502,0.00018399468,0.0004065227],"genre_scores_gemma":[0.99675804,0.00003430562,0.0027805138,0.000014805108,0.00008509949,0.000062696716,0.000032718715,0.00006251057,0.00016929522],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979448,0.00016095392,0.00032141642,0.00016474065,0.0002731974,0.0011349443],"domain_scores_gemma":[0.9995081,0.00022016646,0.00010196775,0.00007890473,0.00003362725,0.000057279194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018769321,0.00017227493,0.00018373378,0.00039328303,0.0003342567,0.0000383621,0.00012342377,0.000046176712,0.000023256647],"category_scores_gemma":[0.000049897248,0.00018500575,0.0001051315,0.00035606456,0.000005708,0.00009902375,0.000015001449,0.0016870497,0.0000026099588],"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":[0.00008601656,0.000021101538,0.00033752096,0.000020526857,0.00005188075,0.0000052707564,0.00015515997,0.96344906,0.00069872115,0.0011370523,0.0000074521736,0.03403023],"study_design_scores_gemma":[0.0007463561,0.00041619543,0.00019897445,0.000044947294,0.000014520488,0.0001575747,0.0004789049,0.9943796,0.00029187038,0.0019872263,0.0010930836,0.00019076523],"about_ca_topic_score_codex":0.00003815365,"about_ca_topic_score_gemma":0.000102804835,"teacher_disagreement_score":0.8698879,"about_ca_system_score_codex":0.002301029,"about_ca_system_score_gemma":0.00020709314,"threshold_uncertainty_score":0.75443155},"labels":[],"label_agreement":null},{"id":"W4229335382","doi":"10.3390/en15093425","title":"Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":48,"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":"Imam Abdulrahman Bin Faisal University; University of Bahrain","keywords":"Nonlinear autoregressive exogenous model; Autoregressive model; Computer science; Bayesian probability; Hyperparameter; Term (time); Algorithm; Statistics; Artificial intelligence; Mathematics","score_opus":0.027489002542927292,"score_gpt":0.20924003054195484,"score_spread":0.18175102799902754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229335382","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":[0.021412224,0.0007217233,0.9768105,0.000014292461,0.00012309116,0.000106474734,0.000057959893,0.000274512,0.0004791897],"genre_scores_gemma":[0.86906844,0.000019493224,0.13024525,0.000013740879,0.000082509236,0.00011096694,0.00036329316,0.000047763704,0.000048544247],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991542,0.000051401687,0.00018669326,0.00020071561,0.00012355356,0.00028343458],"domain_scores_gemma":[0.9995626,0.00025634168,0.000027937365,0.000070410664,0.000012750379,0.000069927715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023461787,0.00016021787,0.0001680743,0.000086278495,0.0004467686,0.00005595042,0.00007565837,0.00003870342,0.0000430968],"category_scores_gemma":[0.00006109078,0.00017222919,0.00003453168,0.00014999817,0.000026859338,0.000078330806,0.000043882083,0.00020128477,6.979811e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011748767,0.000010197548,0.0008381435,0.000054598495,0.000020748521,0.0000049573214,0.00013662671,0.93798554,0.00005287567,0.0003951923,0.000035086756,0.0604543],"study_design_scores_gemma":[0.00026966608,0.00010157538,0.000041998617,0.000007815427,0.000022944505,0.000017093571,0.000068880036,0.9976238,0.0007836514,0.00009266979,0.00076656113,0.00020336731],"about_ca_topic_score_codex":0.000013231596,"about_ca_topic_score_gemma":0.000010177891,"teacher_disagreement_score":0.8476562,"about_ca_system_score_codex":0.000056589546,"about_ca_system_score_gemma":0.000017565799,"threshold_uncertainty_score":0.70233023},"labels":[],"label_agreement":null},{"id":"W4229443704","doi":"10.1016/j.energy.2022.124212","title":"Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization","year":2022,"lang":"en","type":"article","venue":"Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":134,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Renewable energy; Computer science; Wind power; Electricity; Electricity market; Electricity price forecasting; Volatility (finance); Econometrics; Economics; Engineering; Electrical engineering","score_opus":0.009770477575652355,"score_gpt":0.18088239226468264,"score_spread":0.17111191468903028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4229443704","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":[0.100400984,0.0004637202,0.89650303,0.0000125553215,0.00030026323,0.00004846431,0.000022788643,0.0002028225,0.0020453657],"genre_scores_gemma":[0.9833276,0.00001577305,0.015550245,0.00006707172,0.00017071042,0.000040450042,0.00044487044,0.000074232805,0.00030902997],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99853987,0.00008525401,0.00037606418,0.00025525794,0.0003192697,0.0004242755],"domain_scores_gemma":[0.9993944,0.00009289917,0.00018401627,0.00018159384,0.00007757633,0.000069491034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019978119,0.000219717,0.00024134424,0.00012403366,0.0003943907,0.000039519517,0.00015476467,0.000068902504,0.0001203159],"category_scores_gemma":[0.0000141560695,0.00023961462,0.00006238131,0.00097388297,0.000024522804,0.000162024,0.000029294812,0.00011584162,7.017095e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000046534402,0.000033873574,0.00025612873,0.000021022775,0.000041253028,0.0000040090345,0.000027938822,0.9875533,0.010091505,0.00038186248,0.00096044835,0.0005821382],"study_design_scores_gemma":[0.00051673985,0.00015462702,0.000023720073,0.000035632467,0.000029390743,0.000011769007,0.000021791488,0.97432,0.021889083,0.00006164763,0.0026645693,0.00027100978],"about_ca_topic_score_codex":0.0028789735,"about_ca_topic_score_gemma":0.00033095552,"teacher_disagreement_score":0.88292664,"about_ca_system_score_codex":0.00017275359,"about_ca_system_score_gemma":0.0000735257,"threshold_uncertainty_score":0.97712004},"labels":[],"label_agreement":null},{"id":"W4230662344","doi":"10.4018/978-1-4666-2065-0.ch007","title":"Performance Analysis of Sequential and Parallel Neural Network Algorithm for Stock Price Forecasting","year":2013,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Artificial neural network; Computer science; Backpropagation; Stock market; Process (computing); Artificial intelligence; Stock market prediction; Machine learning; Algorithm","score_opus":0.02276194310435193,"score_gpt":0.21840563017330522,"score_spread":0.1956436870689533,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230662344","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013301261,0.001305103,0.023722652,0.0000026564635,0.00088778103,0.0005799024,0.00034551692,0.00025367524,0.95960146],"genre_scores_gemma":[0.8872555,0.00008782675,0.076942965,0.00010513538,0.0022317707,0.00015022872,0.00018777401,0.0003000023,0.032738823],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847144,0.000006747905,0.00052303576,0.0003231331,0.00020223658,0.00047343364],"domain_scores_gemma":[0.9992443,0.00006835758,0.0002128849,0.00024488493,0.0000997141,0.00012990645],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001078977,0.00043982157,0.0007239287,0.00011318032,0.00010115515,0.000050771832,0.00018462879,0.0003174112,0.000027230637],"category_scores_gemma":[0.000005277089,0.00044953692,0.00030624893,0.000063055326,0.00006426534,0.00007469563,0.00008538821,0.0002132739,0.0000024921776],"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":[0.000032000964,0.000005004447,0.00025574674,0.00044689205,0.00373788,0.000012396555,0.00012407436,0.5357294,0.000014556105,0.08360027,0.0011446488,0.37489715],"study_design_scores_gemma":[0.00027714783,0.000089234265,0.00007824214,0.00019519943,0.0010302416,0.000019254085,0.0000029279452,0.99175596,0.0000096431995,0.0022958608,0.0037897765,0.00045653942],"about_ca_topic_score_codex":0.000033978806,"about_ca_topic_score_gemma":0.000043079493,"teacher_disagreement_score":0.92686266,"about_ca_system_score_codex":0.0000815341,"about_ca_system_score_gemma":0.00002618961,"threshold_uncertainty_score":0.9997956},"labels":[],"label_agreement":null},{"id":"W4231743284","doi":"10.32920/ryerson.14649180","title":"A probabilistic approach for optimal capacitor planning in distribution systems with wind generators","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cumulant; Probabilistic logic; Mathematical optimization; Logarithm; Monte Carlo method; Wind power; Interior point method; Probability density function; Computer science; Point (geometry); Wind speed; Electric power system; Probability distribution; Control theory (sociology); Engineering; Mathematics; Power (physics); Electrical engineering; Statistics","score_opus":0.020639041308049517,"score_gpt":0.21246030569916727,"score_spread":0.19182126439111774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231743284","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":[0.51483667,0.0007477487,0.4815838,0.0000017658404,0.00072741974,0.00055026927,0.0001302825,0.00021408101,0.0012079715],"genre_scores_gemma":[0.97576064,0.0000046302175,0.020905184,0.000002117464,0.0004471099,0.00038652282,0.0023718788,0.000064717446,0.00005720231],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869436,0.00002806754,0.00034805812,0.0004103536,0.00015203668,0.00036712928],"domain_scores_gemma":[0.99949646,0.0000493974,0.00005802327,0.00024972862,0.000067940455,0.00007846705],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018722001,0.00033314843,0.0004173504,0.00007275294,0.000047323512,0.00019828766,0.00013864523,0.00030668298,0.0000030913347],"category_scores_gemma":[0.000029355035,0.00029360285,0.00006953592,0.0001418498,0.000024005867,0.00007048282,0.000066511566,0.00044090505,2.5101457e-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":[0.0000119419365,0.000021263842,0.00051113084,0.0015764072,0.00006873032,0.000011707294,0.0004136437,0.9967792,0.00006351638,0.00031705076,0.00012775193,0.00009766373],"study_design_scores_gemma":[0.00029067544,0.000029429291,0.00008724937,0.0007216206,0.00003370702,0.000020931879,0.0005187325,0.9972767,0.00037203866,0.00000502187,0.00022258665,0.00042128374],"about_ca_topic_score_codex":0.00009181481,"about_ca_topic_score_gemma":0.000016540773,"teacher_disagreement_score":0.46092397,"about_ca_system_score_codex":0.00025099883,"about_ca_system_score_gemma":0.000084395935,"threshold_uncertainty_score":0.9999516},"labels":[],"label_agreement":null},{"id":"W4232444411","doi":"10.32920/ryerson.14644221","title":"Analysis of the impacts of extreme weather events on Ontario’s electricity grid using agent-based modeling","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Extreme weather; Environmental science; Electricity; Meteorology; Storm; Winter storm; Grid; Climatology; Engineering; Climate change; Geography; Geology; Electrical engineering","score_opus":0.04664533742974797,"score_gpt":0.23974919712898743,"score_spread":0.19310385969923946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4232444411","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":[0.9356696,0.00017935199,0.061752807,0.0000035627745,0.00049644144,0.000095554286,0.000022326642,0.000046743266,0.0017336403],"genre_scores_gemma":[0.99814224,0.00001389541,0.0016246503,0.000019469753,0.000041069405,0.0000030619633,0.000049819,0.00003800877,0.000067786095],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985029,0.00006899463,0.0005282236,0.00027024632,0.0003638986,0.00026571794],"domain_scores_gemma":[0.99900186,0.00004826072,0.00017898247,0.0006165976,0.00009859309,0.000055712873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025174057,0.0002912617,0.0006281474,0.00035727883,0.000037503873,0.000018600378,0.00029883033,0.00021200282,0.00023570629],"category_scores_gemma":[0.000030355877,0.00022661561,0.0006900906,0.0006662208,0.000010553179,0.00003401515,0.00013909637,0.00048199488,2.5595915e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000006185379,0.000042069256,0.014787055,0.00010550596,0.0012350107,0.0000012583994,0.00030202238,0.978044,0.005236646,0.000008324773,0.000002719305,0.0002292175],"study_design_scores_gemma":[0.00011707981,0.0000090719295,0.002580124,0.00044487102,0.0009082995,3.090731e-7,0.000019956791,0.97378427,0.02191521,0.000015700909,0.0000041272547,0.00020099676],"about_ca_topic_score_codex":0.0342933,"about_ca_topic_score_gemma":0.035420645,"teacher_disagreement_score":0.062472668,"about_ca_system_score_codex":0.0003726769,"about_ca_system_score_gemma":0.00021877492,"threshold_uncertainty_score":0.9821804},"labels":[],"label_agreement":null},{"id":"W4235167712","doi":"10.35940/ijitee.l1117.10812s219","title":"Electricity Price Forecasting using a Hybrid of Neural Network and Genetic Algorithm","year":2019,"lang":"en","type":"article","venue":"International Journal of Innovative Technology and Exploring Engineering","topic":"Energy Load and Power Forecasting","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":"Electricity price forecasting; Artificial neural network; Benchmark (surveying); Genetic algorithm; Electricity market; Computer science; Electricity; Energy (signal processing); Process (computing); Artificial intelligence; Machine learning; Engineering; Statistics; Mathematics","score_opus":0.020263387741544407,"score_gpt":0.21495361528230467,"score_spread":0.19469022754076026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235167712","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":[0.91620606,0.00088847545,0.081951745,0.00001721288,0.0007994674,0.000034745128,0.0000023310083,0.00005559529,0.00004435683],"genre_scores_gemma":[0.9544763,0.00012566581,0.045187987,0.0000060436487,0.00017893972,0.0000019319712,6.7625564e-7,0.000020736696,0.0000017009467],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991834,0.0000050743733,0.00039586128,0.000091907765,0.00013424759,0.00018948846],"domain_scores_gemma":[0.9994235,0.00007191806,0.00015593988,0.000054487075,0.0002671817,0.000026998161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001402459,0.0001323918,0.00022779626,0.0006608757,0.000020608419,0.000015719463,0.00015280659,0.000055152344,0.0000026976843],"category_scores_gemma":[0.00007135621,0.00013294845,0.000025171064,0.00056649745,0.00003052659,0.00025827173,0.00007027697,0.00034245028,1.5818037e-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":[0.000023919332,0.000013338627,0.026410671,0.000086609456,0.00042264845,0.00012668558,0.00021383293,0.8094835,0.05389776,0.0019940594,0.000009415703,0.107317545],"study_design_scores_gemma":[0.00052001094,0.00013566963,0.0035150782,0.00050187577,0.00001224776,0.0017816261,0.000071774775,0.9550649,0.037449144,0.0004578443,0.00027404985,0.00021581068],"about_ca_topic_score_codex":0.0000018835941,"about_ca_topic_score_gemma":9.895137e-8,"teacher_disagreement_score":0.14558135,"about_ca_system_score_codex":0.000045687273,"about_ca_system_score_gemma":0.000011902733,"threshold_uncertainty_score":0.54214805},"labels":[],"label_agreement":null},{"id":"W4235879985","doi":"10.1109/icjece.2021.3099708","title":"Table of contents","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Table (database); Information retrieval; Computer science; Mathematics; Database","score_opus":0.006226560083767036,"score_gpt":0.15074782456904434,"score_spread":0.1445212644852773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4235879985","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":[0.83349586,0.014655729,0.14966474,0.00004605087,0.001207092,0.00002427869,0.000005136876,0.000027213426,0.0008738876],"genre_scores_gemma":[0.99730235,0.00005703406,0.0024358283,0.000020332021,0.0001537375,1.6874559e-7,6.9896953e-7,0.00001026423,0.000019612526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995327,0.0000042696647,0.00018948037,0.000042094693,0.000051390165,0.00018008304],"domain_scores_gemma":[0.99957544,0.000040926123,0.00002132465,0.000039824994,0.00007193621,0.00025055947],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003816445,0.00006811453,0.00016061733,0.00011299931,0.000015692916,0.00002067713,0.000059529124,0.0000349307,0.000011300051],"category_scores_gemma":[0.00002185842,0.00006765384,0.000037664457,0.00020363287,0.0000068359454,0.000058498408,0.000004729644,0.00014326863,2.664075e-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":[0.0000059220806,0.000020231199,0.009758494,0.00023473348,0.00039779872,0.0012047199,0.00041332035,0.8787768,0.019587096,0.007799035,0.0035830296,0.078218825],"study_design_scores_gemma":[0.00061275624,0.00016330213,0.007290601,0.00032717295,0.000034807563,0.0013229899,0.000007915896,0.92029643,0.02651784,0.00010697186,0.043012492,0.00030674992],"about_ca_topic_score_codex":0.000053410713,"about_ca_topic_score_gemma":0.00008598588,"teacher_disagreement_score":0.16380645,"about_ca_system_score_codex":0.000020189198,"about_ca_system_score_gemma":0.00007056592,"threshold_uncertainty_score":0.27588436},"labels":[],"label_agreement":null},{"id":"W4236370022","doi":"10.26868/25222708.2019.211250","title":"Building Energy Use Surrogate Model Feature Selection – A Methodology Using Forward Stepwise Selection and LASSO Regression Methods","year":2020,"lang":"en","type":"article","venue":"Building Simulation Conference proceedings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Ontario Centres of Excellence","keywords":"Lasso (programming language); Feature selection; Stepwise regression; Selection (genetic algorithm); Computer science; Artificial intelligence; Regression; Elastic net regularization; Machine learning; Regression analysis; Energy (signal processing); Surrogate model; Feature (linguistics); Pattern recognition (psychology); Statistics; Mathematics","score_opus":0.11710669995547174,"score_gpt":0.34995094134320637,"score_spread":0.23284424138773463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236370022","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":[0.391477,0.00007901884,0.6075965,0.000102444916,0.00011313501,0.00009285679,0.0000027343542,0.0004808029,0.00005546432],"genre_scores_gemma":[0.5814468,0.00003154002,0.4182096,0.00008615614,0.00014084546,0.00000861464,0.0000035754767,0.000051871084,0.000020987243],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982276,0.000087654174,0.00039996192,0.00057820405,0.00023347684,0.00047308562],"domain_scores_gemma":[0.9987307,0.00036895773,0.00020889628,0.000062975996,0.00039886285,0.00022959696],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054695643,0.0004061743,0.00044757838,0.0002749838,0.00031498366,0.00032919788,0.00013556457,0.00037217324,0.000009600027],"category_scores_gemma":[0.00066227856,0.00041602235,0.000083325955,0.0007258571,0.000035632125,0.0012228803,0.000089633366,0.00043565655,3.8516956e-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":[0.000047544323,0.000004295585,0.0009519612,0.00009391105,0.000035879213,4.3716435e-7,0.00069704704,0.6653428,0.31522152,0.006419522,0.000057909077,0.0111271925],"study_design_scores_gemma":[0.0003746008,0.000054194905,0.00006393015,0.00017004943,0.000076947574,0.000019749605,0.00005322162,0.93105507,0.06365022,0.0020467213,0.0020352064,0.0004000574],"about_ca_topic_score_codex":0.000043277778,"about_ca_topic_score_gemma":0.000009074466,"teacher_disagreement_score":0.26571232,"about_ca_system_score_codex":0.00014479559,"about_ca_system_score_gemma":0.000048516733,"threshold_uncertainty_score":0.9998292},"labels":[],"label_agreement":null},{"id":"W4238102430","doi":"10.1002/9781119547273.app5","title":"Appendix E: Asymmetrical Current‐Calculating Areas Under Curves","year":2019,"lang":"en","type":"other","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Hydro (Canada)","funders":"","keywords":"Citation; Current (fluid); Library science; Computer science; Operations research; Information retrieval; Mathematics; Electrical engineering; Engineering","score_opus":0.01795038636872953,"score_gpt":0.24491425164121877,"score_spread":0.22696386527248924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238102430","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000006357018,0.028664088,0.009803816,0.000013229595,0.0022212483,0.00013773981,0.000021090784,0.0013119923,0.9578204],"genre_scores_gemma":[0.002710514,0.0027768135,0.0012113344,0.00012671783,0.0013289026,0.000014443576,0.0004184621,0.001340436,0.99007237],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988723,0.00001703607,0.00022893537,0.00026359648,0.00024926066,0.0003689001],"domain_scores_gemma":[0.9994596,0.000069797214,0.000056135563,0.0003052287,0.000010266883,0.000098964425],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00006522575,0.0003509311,0.0003754127,0.00031794116,0.000019420831,0.000033572804,0.00020367913,0.00025380048,0.004814438],"category_scores_gemma":[0.000023175902,0.00031449515,0.00013242284,0.00031396787,0.000017426142,0.000040145012,0.00005876521,0.00039902734,0.002896535],"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":[3.5199363e-7,0.0000107006945,0.00017416544,0.00083468936,0.00008294641,0.0000021747471,0.0000060439875,0.0018165353,0.0000034737384,0.0030293658,0.97847104,0.015568522],"study_design_scores_gemma":[0.00014842022,0.0000066872276,0.00002334668,0.0019833448,0.000031577576,0.0000054466796,0.0000066409502,0.008124002,0.000021908803,0.000030342691,0.9891457,0.00047258573],"about_ca_topic_score_codex":0.000055295146,"about_ca_topic_score_gemma":0.000030565938,"teacher_disagreement_score":0.032251935,"about_ca_system_score_codex":0.000041682677,"about_ca_system_score_gemma":0.000016332973,"threshold_uncertainty_score":0.99993074},"labels":[],"label_agreement":null},{"id":"W4239495869","doi":"10.1007/978-1-4939-7131-2_100308","title":"Edge Prediction","year":2018,"lang":"en","type":"book-chapter","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Enhanced Data Rates for GSM Evolution; Computer science; Artificial intelligence","score_opus":0.013834108753460044,"score_gpt":0.1818829619925178,"score_spread":0.16804885323905774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239495869","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000027521426,0.00027125093,0.0014617648,0.0000023554255,0.0015564057,0.000031299416,0.000021212021,0.0007003347,0.9959279],"genre_scores_gemma":[0.0015581638,0.00012718966,0.00033156833,0.000020337697,0.001516386,0.0000018595035,0.00006179387,0.00007528986,0.99630743],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995858,6.743989e-7,0.00012701991,0.00010368904,0.00007594006,0.00010684312],"domain_scores_gemma":[0.99977523,0.000009169535,0.000013891774,0.00014204839,0.00001827332,0.00004137914],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000027604769,0.00015459213,0.000110523535,0.00005783461,0.00002203288,0.000013330142,0.000061026494,0.0002174197,0.007100833],"category_scores_gemma":[0.0000015053259,0.00014751028,0.000054178407,0.000007038791,0.000018966844,0.000036565147,0.000015201549,0.00014150466,0.0008642007],"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":[0.000003904866,0.0000034728143,0.000010515061,0.00019709114,0.000261635,0.000016166721,0.00023487283,0.002034833,0.00016839933,0.19591974,0.76299554,0.038153853],"study_design_scores_gemma":[0.00003907108,0.000018088482,0.0000021717622,0.000104116385,0.00001498598,0.000006521455,8.722541e-7,0.0032089162,0.00019893679,0.0028719876,0.9933815,0.00015285079],"about_ca_topic_score_codex":0.0000010639769,"about_ca_topic_score_gemma":0.000009590827,"teacher_disagreement_score":0.23038596,"about_ca_system_score_codex":0.000028664366,"about_ca_system_score_gemma":0.0000051600623,"threshold_uncertainty_score":0.99991375},"labels":[],"label_agreement":null},{"id":"W4239949396","doi":"10.1002/9781119283362.ch5","title":"Short‐Term Load Forecasting and Post‐Strategy Design for <scp>CCHP</scp> Systems","year":2017,"lang":"en","type":"other","venue":"","topic":"Energy Load and Power Forecasting","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 Victoria","funders":"","keywords":"Autoregressive model; Ordinary least squares; Term (time); Autoregressive–moving-average model; Identification (biology); Moving average; Quadratic equation; Computer science; Mathematical optimization; Control theory (sociology); Mathematics; Econometrics; Statistics; Control (management); Artificial intelligence","score_opus":0.04317418905315792,"score_gpt":0.23716355501283226,"score_spread":0.19398936595967434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4239949396","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0005417059,0.00881383,0.06618413,0.0000016118146,0.0017281823,0.0007617688,0.00012508675,0.0010655092,0.92077816],"genre_scores_gemma":[0.06374642,0.0005626821,0.006074323,0.000014614403,0.0021237445,0.0002201405,0.0001280509,0.0015173993,0.9256126],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984609,0.000021455082,0.00029760547,0.00040822115,0.00019054544,0.0006213032],"domain_scores_gemma":[0.9989078,0.0002903094,0.00012345439,0.0004332844,0.000064224216,0.00018092281],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025259153,0.0005570243,0.000603619,0.00017698671,0.00014368819,0.0003212038,0.00031041313,0.00055472896,0.000030758387],"category_scores_gemma":[0.00012347059,0.00050960045,0.00011552468,0.00004649298,0.000052687952,0.00011347858,0.000051180155,0.00023283127,0.000014365124],"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":[0.000007847106,0.000024823657,0.0002991783,0.003966411,0.0008885195,0.00009702167,0.00033576725,0.02894272,0.0013344386,0.0012550796,0.8797524,0.083095774],"study_design_scores_gemma":[0.0008059977,0.00024450643,0.00005826805,0.0035474014,0.00023035363,0.00023813389,0.0001476155,0.21613301,0.0007134711,0.000060317765,0.77704495,0.00077597913],"about_ca_topic_score_codex":0.00017798398,"about_ca_topic_score_gemma":0.00016614354,"teacher_disagreement_score":0.1871903,"about_ca_system_score_codex":0.000061198596,"about_ca_system_score_gemma":0.000051767747,"threshold_uncertainty_score":0.99973553},"labels":[],"label_agreement":null},{"id":"W4242259452","doi":"10.32920/ryerson.14649129","title":"Day-Ahead Electricity Price And Spike Forecasting Using Machine Learning Techniques","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Windsor; Toronto Metropolitan University","funders":"","keywords":"Electricity price forecasting; Electricity; Spike (software development); Electricity market; Artificial neural network; Econometrics; Computer science; Economics; Artificial intelligence; Engineering","score_opus":0.026516066469268452,"score_gpt":0.23392156610097983,"score_spread":0.20740549963171137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242259452","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":[0.7548341,0.0063829687,0.20560019,0.00001586478,0.00061430625,0.00028308845,0.00000983543,0.002245532,0.030014075],"genre_scores_gemma":[0.9040414,0.0006800464,0.0943455,0.000034008026,0.00037376376,0.000019334295,0.00009568516,0.00014465039,0.00026559873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817705,0.00007617612,0.0004633065,0.00052196754,0.00021374511,0.0005477247],"domain_scores_gemma":[0.9992357,0.00014918849,0.00012330395,0.00027775104,0.00008350092,0.00013059338],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004808514,0.00049877784,0.0005394471,0.00023981907,0.00018810331,0.00027192768,0.0001932947,0.00042746204,0.000070988426],"category_scores_gemma":[0.0001660388,0.0005240522,0.00012218814,0.00029052357,0.000030065874,0.00015591248,0.00059275725,0.0016652094,8.2174444e-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":[0.000022317721,0.00010342921,0.029369285,0.0051174364,0.0007894877,0.00045782293,0.0030051458,0.57606363,0.13635245,0.0006431429,0.00012807603,0.2479478],"study_design_scores_gemma":[0.0000886569,0.000025131465,0.0000830936,0.00072433945,0.000055428158,0.00012518937,0.000042233027,0.9287009,0.06734022,0.00010343421,0.0020479052,0.00066343485],"about_ca_topic_score_codex":0.00059629045,"about_ca_topic_score_gemma":0.00015346847,"teacher_disagreement_score":0.35263732,"about_ca_system_score_codex":0.00015176133,"about_ca_system_score_gemma":0.000055781195,"threshold_uncertainty_score":0.9997211},"labels":[],"label_agreement":null},{"id":"W4243045133","doi":"10.32920/ryerson.14655294","title":"Techno-Economic Models for Integration of Wind Energy","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Toronto Metropolitan University; Toronto Public Health","funders":"","keywords":"Wind power; Probabilistic logic; Wind speed; Computer science; Electric power system; Induction generator; Electricity generation; Control theory (sociology); Power (physics); Engineering; Electrical engineering","score_opus":0.020701840992281242,"score_gpt":0.21607150809466683,"score_spread":0.19536966710238557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243045133","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":[0.052464843,0.0005869867,0.9023993,0.000013598318,0.0010132237,0.00008446236,0.000045265482,0.00024359662,0.043148696],"genre_scores_gemma":[0.97895634,0.000154869,0.019948455,0.000008378788,0.00013166989,0.000034887922,0.00024195998,0.00004165251,0.00048177704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993622,0.00000398905,0.0002858199,0.00018031207,0.000042676576,0.0001250333],"domain_scores_gemma":[0.9996061,0.00003318667,0.00005134672,0.0002472527,0.00003674622,0.000025401749],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059833565,0.00016739074,0.00025836815,0.0000971707,0.000013018537,0.000030038023,0.000137979,0.00025868896,0.000048995036],"category_scores_gemma":[0.000005376675,0.00017196969,0.0001365349,0.000028450424,0.000011227452,0.00007423263,0.000090625355,0.00013561164,4.512587e-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":[0.0000015655618,0.0000039398237,0.0000014959609,0.00011557262,0.00004999332,2.3401991e-7,0.000078095196,0.96035767,0.002719822,0.022230804,0.0001689636,0.0142718665],"study_design_scores_gemma":[0.00007024217,0.000007747867,0.0000010561938,0.00015957023,0.000015330474,0.0000010956055,0.00005102665,0.89074624,0.10131548,0.0067782053,0.00069386757,0.00016011296],"about_ca_topic_score_codex":0.00035255353,"about_ca_topic_score_gemma":0.00034172923,"teacher_disagreement_score":0.9264915,"about_ca_system_score_codex":0.00006966935,"about_ca_system_score_gemma":0.00004308498,"threshold_uncertainty_score":0.701272},"labels":[],"label_agreement":null},{"id":"W4243376162","doi":"10.1016/s1365-6937(19)30195-9","title":"Mann+Hummel acquires Canada's Hardy Filtration","year":2019,"lang":"en","type":"article","venue":"Filtration Industry Analyst","topic":"Energy Load and Power Forecasting","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":"Filtration (mathematics); Mathematics","score_opus":0.00992923247279961,"score_gpt":0.19848311520097348,"score_spread":0.18855388272817386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243376162","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":[0.9093743,0.00010565789,0.0009612824,0.00021618692,0.0013309447,0.00013472293,0.00006488698,0.00025443174,0.08755761],"genre_scores_gemma":[0.995323,0.0000050027797,0.00010615133,0.00014072214,0.00036902414,0.000011890654,0.00028827277,0.000023615525,0.003732319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901146,0.000025768688,0.0002908521,0.000184659,0.00024194116,0.00024532713],"domain_scores_gemma":[0.99951726,0.000040764287,0.000056875924,0.00024182048,0.000048602302,0.00009469469],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0000970174,0.00016883427,0.00016253768,0.00007917398,0.000088904395,0.00007639118,0.00013499436,0.00025438276,0.0016830667],"category_scores_gemma":[0.000021861548,0.00017811195,0.00004853243,0.00029818117,0.000011971241,0.00029275686,0.000009824822,0.00037972655,0.000073479314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000011541418,0.000033852197,0.06256293,0.00014746624,0.00022395176,0.00002262754,0.00027579392,0.8198626,0.019834116,0.0021712077,0.09207996,0.0027739608],"study_design_scores_gemma":[0.0013096891,0.00012454801,0.09185597,0.0004708752,0.00017387657,0.00006705143,0.0013004933,0.47230735,0.12072289,0.00017408725,0.3095315,0.0019616503],"about_ca_topic_score_codex":0.016018867,"about_ca_topic_score_gemma":0.079891376,"teacher_disagreement_score":0.34755525,"about_ca_system_score_codex":0.00011505593,"about_ca_system_score_gemma":0.00013133176,"threshold_uncertainty_score":0.99922955},"labels":[],"label_agreement":null},{"id":"W4244250304","doi":"10.32920/ryerson.14663082","title":"Estimating power consumption in City of Toronto: a case study","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Electricity; Electricity demand; Electricity generation; Renewable energy; Consumption (sociology); Environmental economics; Peak demand; Electricity retailing; Energy demand; Business; Economics; Power (physics); Electricity market; Engineering; Electrical engineering","score_opus":0.03022307324850706,"score_gpt":0.28226930144755247,"score_spread":0.25204622819904543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4244250304","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":[0.985913,0.00083236495,0.0038258103,8.4843924e-7,0.0009860966,0.00018406699,0.000005306189,0.00014563726,0.008106888],"genre_scores_gemma":[0.98894125,0.000015176094,0.010917567,0.0000028788843,0.000034892335,0.00002399445,0.000011235795,0.000024985784,0.00002803944],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990223,0.000039640472,0.00043250486,0.00022394484,0.00011918876,0.00016239076],"domain_scores_gemma":[0.9994997,0.000065939785,0.000060024053,0.00030054056,0.000033801305,0.00003999793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023590862,0.00019437924,0.0003407837,0.000036016725,0.000016831402,0.000035300123,0.00008936764,0.00014929575,0.0006469409],"category_scores_gemma":[0.000039852566,0.00020726032,0.0000638309,0.000037076505,0.0000112285015,0.00008247566,0.0002176642,0.00031997907,9.049514e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005420569,0.00030428823,0.16182806,0.0009806929,0.00020846374,0.003855144,0.019426757,0.805849,0.00038325734,0.000042511678,0.000031951124,0.007084437],"study_design_scores_gemma":[0.0010802869,0.0000955078,0.021159988,0.0016310494,0.00008971164,0.0008674811,0.011809391,0.96105075,0.001173912,0.000038006325,0.000018815133,0.0009851091],"about_ca_topic_score_codex":0.015406033,"about_ca_topic_score_gemma":0.040064797,"teacher_disagreement_score":0.15520173,"about_ca_system_score_codex":0.00012964806,"about_ca_system_score_gemma":0.000022173803,"threshold_uncertainty_score":0.99115044},"labels":[],"label_agreement":null},{"id":"W4247015375","doi":"10.1109/scored50371.2020.9250747","title":"About The Conference","year":2020,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Universiti Teknikal Malaysia Melaka","keywords":"Computer science","score_opus":0.03131457700173069,"score_gpt":0.20406937488883636,"score_spread":0.17275479788710568,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247015375","genre_codex":"other","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":[0.06107379,0.00032888466,0.021693459,0.0013175608,0.00020965272,0.000027977138,0.0000010648941,0.0005375895,0.91481],"genre_scores_gemma":[0.9986605,0.000017583518,0.00021253584,0.00076003163,0.000085481304,0.0000012720191,7.9683724e-7,0.0000051320353,0.0002566815],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998372,0.0000022768445,0.000038262147,0.00003066384,0.000029190038,0.00006237471],"domain_scores_gemma":[0.99989986,0.000013290409,0.000002303651,0.00004059044,0.0000045513625,0.00003938988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00001121278,0.000032085,0.000027453396,0.0000023000862,0.00001794057,0.000016299342,0.00006791288,0.000010744003,0.0002845808],"category_scores_gemma":[0.000008380826,0.000019428588,0.000011564689,0.000040215455,0.000007410371,0.00002220208,0.000008911514,0.000049302573,0.00009249939],"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":[0.000012330757,0.000010027916,0.0031127355,0.0001713691,0.00013163946,0.000023925997,0.019522762,0.16852134,0.023541069,0.42497018,0.1735208,0.18646182],"study_design_scores_gemma":[0.00009630503,0.000017559767,0.00017001384,0.000011877297,0.0000033388937,0.000002443602,0.00026103508,0.47284228,0.009329899,0.00019705553,0.5169356,0.00013261258],"about_ca_topic_score_codex":0.000002244403,"about_ca_topic_score_gemma":0.0000026895657,"teacher_disagreement_score":0.93758667,"about_ca_system_score_codex":0.0000014039804,"about_ca_system_score_gemma":0.0000029252947,"threshold_uncertainty_score":0.31159604},"labels":[],"label_agreement":null},{"id":"W4247045770","doi":"10.4018/978-1-5225-1908-9.ch013","title":"A Fuzzy Model with Thermodynamic Based Consequents and a Niching Swarm-Based Supervisor to Capture the Uncertainties of Damavand Power System","year":2017,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Energy Load and Power Forecasting","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 Waterloo","funders":"","keywords":"Particle swarm optimization; Fuzzy logic; Supervisor; Robustness (evolution); Computer science; Metaheuristic; Artificial intelligence; Mathematical optimization; Process (computing); Machine learning; Swarm behaviour; Mathematics","score_opus":0.012738309422964705,"score_gpt":0.20452038320754312,"score_spread":0.19178207378457843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247045770","genre_codex":"other","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":[0.032359205,0.0006698246,0.0035674586,0.000033167737,0.00024254282,0.0004958104,0.0005404267,0.00021814532,0.9618734],"genre_scores_gemma":[0.99601847,0.0000011456564,0.0005365542,0.00013205147,0.000035102363,0.000022582619,0.0000082152055,0.00009393955,0.0031519516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986461,0.000019001334,0.00032730892,0.00034352602,0.000323101,0.0003409548],"domain_scores_gemma":[0.9988682,0.000060217484,0.00015316668,0.0006815314,0.000097677075,0.00013917209],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013888975,0.00054386386,0.0005702281,0.00007623124,0.00017266927,0.00011456924,0.00039902644,0.00029771967,0.0000023987027],"category_scores_gemma":[0.00000997723,0.0003763163,0.00012989945,0.000011291932,0.00017117804,0.000037463742,0.000063700245,0.00031584175,0.0000028512409],"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":[0.0002829521,0.000007409047,0.00007876427,0.0010877522,0.0004620795,0.00010064776,0.00080925936,0.50536424,0.00040880736,0.490677,0.00018744323,0.00053362845],"study_design_scores_gemma":[0.0027473844,0.0003469825,0.000034155797,0.011569573,0.00063465344,0.00013423746,0.00042795343,0.9664911,0.00039174545,0.0132447975,0.0018626534,0.0021147616],"about_ca_topic_score_codex":0.0003186409,"about_ca_topic_score_gemma":0.000795418,"teacher_disagreement_score":0.9636592,"about_ca_system_score_codex":0.0002267746,"about_ca_system_score_gemma":0.00019839997,"threshold_uncertainty_score":0.99986887},"labels":[],"label_agreement":null},{"id":"W4247989823","doi":"10.4324/9780429061455","title":"Renewable Energy Uptake in Urban Latin America","year":2020,"lang":"en","type":"book","venue":"","topic":"Energy Load and Power Forecasting","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":"Carleton University","funders":"","keywords":"Latin Americans; Renewable energy; Geography; Political science; Engineering; Electrical engineering","score_opus":0.011945781563550105,"score_gpt":0.18042192887750977,"score_spread":0.16847614731395966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4247989823","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000005406716,0.0010998077,0.002206212,0.000023255816,0.00040219096,0.000033013748,0.000014857235,0.00051436515,0.9957009],"genre_scores_gemma":[0.0013842057,0.00031315742,0.0016174455,0.0002987738,0.00062644656,0.000013038832,0.0002436157,0.00014192883,0.9953614],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989261,0.000008837909,0.0003488624,0.00025735417,0.00014558824,0.00031322642],"domain_scores_gemma":[0.99957114,0.000048203023,0.000042333777,0.00021626474,0.000010974991,0.00011107458],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000027991913,0.00032078227,0.00040533423,0.00013923454,0.0000196718,0.000029153109,0.00019815957,0.00025690638,0.000728408],"category_scores_gemma":[0.000011054384,0.00033564967,0.00009507132,0.00015946227,0.000019597897,0.000049142887,0.00004980984,0.00030651386,0.0000839289],"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":[0.0000030320132,0.0000045029997,0.000014452716,0.00008492776,0.00004584287,0.0000647148,0.00017158849,0.12560867,0.000067161,0.0026408874,0.8613489,0.009945299],"study_design_scores_gemma":[0.00010588664,0.000020573721,0.000004585715,0.00014050823,0.00000897629,0.0000015763909,0.0000065957,0.026622292,0.00014634237,0.00052244664,0.9720643,0.0003558756],"about_ca_topic_score_codex":0.0005652013,"about_ca_topic_score_gemma":0.0012942952,"teacher_disagreement_score":0.11071542,"about_ca_system_score_codex":0.00012880292,"about_ca_system_score_gemma":0.00007582971,"threshold_uncertainty_score":0.9999096},"labels":[],"label_agreement":null},{"id":"W4250715387","doi":"10.1109/cac53003.2021.9728612","title":"Temperature prediction of generator carbon brush based on LSTM neural network","year":2021,"lang":"en","type":"article","venue":"2021 China Automation Congress (CAC)","topic":"Energy Load and Power Forecasting","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":"Concordia University; University of Alberta","funders":"","keywords":"Brush; Generator (circuit theory); Computer science; Artificial neural network; Mean squared prediction error; Power (physics); Approximation error; Control theory (sociology); Artificial intelligence; Algorithm; Materials science; Composite material; Thermodynamics","score_opus":0.006326768019421619,"score_gpt":0.1915606640763481,"score_spread":0.18523389605692647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4250715387","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":[0.98818916,0.0006454791,0.00026480813,0.00011132778,0.004019838,0.0001136871,0.000076911616,0.00038456373,0.0061942465],"genre_scores_gemma":[0.9978784,0.000028305556,0.00081822375,0.00007455762,0.0006346885,0.000018733128,0.0002805679,0.000045404806,0.00022113489],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99880695,0.00009140441,0.00035252364,0.00023376303,0.00027011364,0.00024524698],"domain_scores_gemma":[0.99936455,0.0000626095,0.000084420615,0.00030840747,0.000103640035,0.00007633997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012315305,0.00021250058,0.0002472958,0.00008067478,0.00008888125,0.000066939996,0.000097473974,0.00016911926,0.00017651686],"category_scores_gemma":[0.000057932153,0.00021767606,0.000092572554,0.00043447418,0.000021368704,0.00012119906,0.000021216501,0.00024679463,0.000004176735],"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":[0.000008948414,0.000025455878,0.0024631731,0.00009862809,0.00004012038,0.000022897722,0.00011350087,0.9784044,0.012548566,0.0002173305,0.0020749294,0.0039820354],"study_design_scores_gemma":[0.0004137436,0.000038467522,0.014394159,0.00022735003,0.000025232339,0.0000073571655,0.0000131448205,0.93800616,0.045671847,0.000021559868,0.0010139749,0.00016699044],"about_ca_topic_score_codex":0.000015919995,"about_ca_topic_score_gemma":0.00003280879,"teacher_disagreement_score":0.04039824,"about_ca_system_score_codex":0.000053129785,"about_ca_system_score_gemma":0.00005555048,"threshold_uncertainty_score":0.8876572},"labels":[],"label_agreement":null},{"id":"W4252514138","doi":"10.32920/ryerson.14656170","title":"ANN-Based Day-Ahead Short Term Load Forecasting","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Electric power system; Term (time); Reliability engineering; Reliability (semiconductor); Artificial intelligence; Artificial neural network; Industrial engineering; Operations research; Machine learning; Engineering; Power (physics)","score_opus":0.032747771381765864,"score_gpt":0.2350733568164505,"score_spread":0.20232558543468465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252514138","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":[0.50636184,0.0028796277,0.122214116,0.00006875445,0.006780396,0.00040263816,0.00007115733,0.0029538986,0.3582676],"genre_scores_gemma":[0.988476,0.000040040857,0.009527057,0.000088701,0.0006474095,0.000059071797,0.0003738376,0.00015202096,0.00063586084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99782646,0.00003742276,0.0005442205,0.0005819726,0.00039383097,0.00061610603],"domain_scores_gemma":[0.9987769,0.00013996773,0.0000489737,0.00071343407,0.0001329393,0.00018776755],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033094265,0.00057113334,0.0005526873,0.00013669209,0.00008857815,0.00025979176,0.0003966506,0.00049963954,0.00045073457],"category_scores_gemma":[0.00008106375,0.00059601816,0.0003074073,0.00018117555,0.0000352503,0.000097935685,0.0003408469,0.0009985795,0.000016611772],"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":[0.0000051487077,0.00004370247,0.0046184724,0.0011618999,0.00021827183,0.00029182987,0.0005304935,0.92106557,0.0022207047,0.000048351278,0.0018400473,0.06795549],"study_design_scores_gemma":[0.00021312837,0.000019861121,0.0006192359,0.0014179166,0.00007299382,0.000024137373,0.000070519,0.97710955,0.015173578,0.00005171646,0.0041492456,0.0010780904],"about_ca_topic_score_codex":0.000091020695,"about_ca_topic_score_gemma":0.0003234344,"teacher_disagreement_score":0.4821142,"about_ca_system_score_codex":0.00025206798,"about_ca_system_score_gemma":0.00022687326,"threshold_uncertainty_score":0.9996491},"labels":[],"label_agreement":null},{"id":"W4254431797","doi":"10.32920/ryerson.14649180.v1","title":"A probabilistic approach for optimal capacitor planning in distribution systems with wind generators","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cumulant; Probabilistic logic; Mathematical optimization; Logarithm; Monte Carlo method; Wind power; Interior point method; Computer science; Wind speed; Point (geometry); Probability density function; Electric power system; Control theory (sociology); Engineering; Mathematics; Power (physics); Electrical engineering; Statistics","score_opus":0.020639041308049517,"score_gpt":0.21246030569916727,"score_spread":0.19182126439111774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254431797","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":[0.51483667,0.0007477487,0.4815838,0.0000017658404,0.00072741974,0.00055026927,0.0001302825,0.00021408101,0.0012079715],"genre_scores_gemma":[0.97576064,0.0000046302175,0.020905184,0.000002117464,0.0004471099,0.00038652282,0.0023718788,0.000064717446,0.00005720231],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99869436,0.00002806754,0.00034805812,0.0004103536,0.00015203668,0.00036712928],"domain_scores_gemma":[0.99949646,0.0000493974,0.00005802327,0.00024972862,0.000067940455,0.00007846705],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018722001,0.00033314843,0.0004173504,0.00007275294,0.000047323512,0.00019828766,0.00013864523,0.00030668298,0.0000030913347],"category_scores_gemma":[0.000029355035,0.00029360285,0.00006953592,0.0001418498,0.000024005867,0.00007048282,0.000066511566,0.00044090505,2.5101457e-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":[0.0000119419365,0.000021263842,0.00051113084,0.0015764072,0.00006873032,0.000011707294,0.0004136437,0.9967792,0.00006351638,0.00031705076,0.00012775193,0.00009766373],"study_design_scores_gemma":[0.00029067544,0.000029429291,0.00008724937,0.0007216206,0.00003370702,0.000020931879,0.0005187325,0.9972767,0.00037203866,0.00000502187,0.00022258665,0.00042128374],"about_ca_topic_score_codex":0.00009181481,"about_ca_topic_score_gemma":0.000016540773,"teacher_disagreement_score":0.46092397,"about_ca_system_score_codex":0.00025099883,"about_ca_system_score_gemma":0.000084395935,"threshold_uncertainty_score":0.9999516},"labels":[],"label_agreement":null},{"id":"W4254585867","doi":"10.17722/ijme.v8i2.874","title":"A Taxonomy of electricity demand forecasting techniques and a selection strategy","year":2017,"lang":"en","type":"article","venue":"International Journal of Management Excellence","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Exponential smoothing; Autoregressive integrated moving average; Computer science; Demand forecasting; Electricity; Adaptability; Operations research; Kalman filter; Electricity demand; Artificial intelligence; Machine learning; Time series; Electricity generation; Economics; Engineering","score_opus":0.026319276029324358,"score_gpt":0.24311917693099097,"score_spread":0.21679990090166662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254585867","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":[0.75467277,0.00095156144,0.13793081,0.0001496806,0.000714042,0.00020864226,0.000003858607,0.000068692774,0.105299965],"genre_scores_gemma":[0.9930689,0.00084551756,0.005820195,0.000005189461,0.0001581341,0.000003672818,2.7430576e-7,0.00000680104,0.0000913266],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938774,0.000008902786,0.0002638414,0.0000626247,0.00018903527,0.00008788372],"domain_scores_gemma":[0.99949366,0.000026761596,0.00028131978,0.000052592994,0.000111770234,0.000033888307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025703217,0.00007399001,0.000103324484,0.00015883357,0.00006276001,0.000077024764,0.00030438713,0.000024890993,0.000016427062],"category_scores_gemma":[0.00002557001,0.00007059473,0.000037579102,0.000030124704,0.000030322175,0.0003003862,0.000055496468,0.00009891633,2.706422e-7],"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":[0.000099644,0.00005986639,0.010466807,0.00021431269,0.00056246313,0.00026961105,0.00014275963,0.02599998,0.0035516885,0.0030259446,0.00080464344,0.9548023],"study_design_scores_gemma":[0.0055806087,0.0015678345,0.030939445,0.005015919,0.0004248052,0.003947416,0.0006872247,0.49765822,0.31911352,0.012453123,0.12100203,0.0016098619],"about_ca_topic_score_codex":0.00001414399,"about_ca_topic_score_gemma":0.0000055311302,"teacher_disagreement_score":0.9531924,"about_ca_system_score_codex":0.00003666986,"about_ca_system_score_gemma":0.000007790991,"threshold_uncertainty_score":0.28787696},"labels":[],"label_agreement":null},{"id":"W4281565149","doi":"10.1109/icpc2t53885.2022.9776757","title":"Short-Term Forecasting in Smart Electric Grid Using N-BEATS","year":2022,"lang":"en","type":"article","venue":"2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"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":"Smart grid; Computer science; Robustness (evolution); Autoregressive integrated moving average; Electric power system; Electricity; Time series; Electricity price forecasting; Python (programming language); Demand response; Wind power; Demand forecasting; Grid; Econometrics; Electricity market; Operations research; Machine learning; Engineering; Power (physics); Economics; Electrical engineering","score_opus":0.026446872765218857,"score_gpt":0.23618032711384382,"score_spread":0.20973345434862495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281565149","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":[0.9831638,0.00065471925,0.0022863613,0.00021226631,0.0016612554,0.00020213162,0.000064969,0.0008396384,0.010914874],"genre_scores_gemma":[0.99937254,0.000052071802,0.00025577898,0.00010331517,0.000059789196,0.000025895746,0.000030615658,0.000036831683,0.00006313966],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981449,0.00006203692,0.00049059704,0.00045084785,0.0003337882,0.0005177806],"domain_scores_gemma":[0.99939454,0.00015780788,0.00010425057,0.00023119441,0.00006723329,0.000044998586],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004098976,0.000317001,0.00035423134,0.00062624493,0.00029033757,0.00013183727,0.00064203894,0.000115632116,0.0002948627],"category_scores_gemma":[0.00007989114,0.00034651946,0.00008319893,0.00039015256,0.00005884316,0.00015438457,0.00038006133,0.00093559275,0.0000026582925],"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":[0.00035897412,0.00041425187,0.063543275,0.00018080497,0.0008272102,0.00077482447,0.0020124298,0.16915537,0.12838969,0.06848776,0.0014396305,0.56441575],"study_design_scores_gemma":[0.0008659716,0.00021652212,0.002384089,0.00008673658,0.000010903506,0.00016235157,0.0008753385,0.9911519,0.0009777562,0.0014017927,0.0014092865,0.0004573283],"about_ca_topic_score_codex":0.000016914566,"about_ca_topic_score_gemma":0.000028534037,"teacher_disagreement_score":0.82199657,"about_ca_system_score_codex":0.00027428314,"about_ca_system_score_gemma":0.00003713691,"threshold_uncertainty_score":0.9998987},"labels":[],"label_agreement":null},{"id":"W4281626910","doi":"10.3390/math10111824","title":"A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Energy Load and Power Forecasting","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":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Subnetwork; Computer science; Robustness (evolution); Artificial intelligence; Deep learning; Artificial neural network; Machine learning; Wind power; Encoder; Competitive learning; Autoencoder; Term (time); Engineering","score_opus":0.048514297256266575,"score_gpt":0.25014848047044713,"score_spread":0.20163418321418056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281626910","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":[0.85093135,0.0024688973,0.12841646,0.000049822695,0.003005636,0.0011581037,0.000087376815,0.00115563,0.01272674],"genre_scores_gemma":[0.8956219,0.0000094499455,0.10336246,0.00006588691,0.00022815033,0.000115503724,0.00003633784,0.0001316504,0.00042866723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864113,0.000019854768,0.0004136497,0.00018824925,0.00019635323,0.0005407441],"domain_scores_gemma":[0.9993394,0.00021764464,0.00006183665,0.0002695906,0.000026686226,0.00008485479],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00034106514,0.00024352125,0.00031639164,0.000052423307,0.0003205934,0.00005568731,0.00024013512,0.000052058203,0.00012534231],"category_scores_gemma":[0.000042544387,0.00024930292,0.000148585,0.00020631438,0.000017171053,0.00008430874,0.00012558892,0.00023983541,0.000005922614],"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":[0.000009077217,0.000106195745,0.00079679367,0.0006762948,0.00011103998,0.000034228742,0.003462075,0.9722084,0.001107265,0.0009726701,0.004106989,0.016408993],"study_design_scores_gemma":[0.00032926025,0.00008503505,0.000068612484,0.00011459412,0.00004347137,0.000110203546,0.00017872552,0.9880557,0.0001877324,0.00083740806,0.009628547,0.0003606906],"about_ca_topic_score_codex":0.0000013877931,"about_ca_topic_score_gemma":0.000014975462,"teacher_disagreement_score":0.044690564,"about_ca_system_score_codex":0.000065113265,"about_ca_system_score_gemma":0.000012022338,"threshold_uncertainty_score":0.99999595},"labels":[],"label_agreement":null},{"id":"W4281642482","doi":"10.3389/fenrg.2022.899692","title":"Hybrid Short-Term Wind Power Prediction Based on Markov Chain","year":2022,"lang":"en","type":"article","venue":"Frontiers in Energy Research","topic":"Energy Load and Power Forecasting","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":"University of Alberta","funders":"","keywords":"Markov chain; Term (time); Wind power; Chaotic; Computer science; Markov model; Wind power forecasting; Markov process; Hidden Markov model; Variable-order Markov model; Power (physics); Engineering; Electric power system; Machine learning; Mathematics; Artificial intelligence; Statistics; Physics","score_opus":0.017109116184060743,"score_gpt":0.24649810180280465,"score_spread":0.2293889856187439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281642482","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":[0.76409453,0.0026052857,0.025055926,0.0003035304,0.010577768,0.0004255806,0.00026947403,0.00077544205,0.19589248],"genre_scores_gemma":[0.9975378,0.00006873658,0.0005728216,0.00005011249,0.00016491766,0.00012150546,0.000121517616,0.00007062266,0.001292004],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976001,0.0002860597,0.00024455495,0.00033774387,0.00084277266,0.0006887865],"domain_scores_gemma":[0.9993635,0.00009444658,0.0000122245,0.00037131578,0.00003370762,0.00012480751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011191147,0.00017002854,0.0001888485,0.0008984715,0.0002777904,0.000041380586,0.00037008902,0.000063539876,0.00037134156],"category_scores_gemma":[0.000038533934,0.00019334923,0.00006666602,0.0006226647,0.0000682988,0.00008777101,0.00012173774,0.0008318233,0.000002686837],"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":[0.00012663272,0.00008748755,0.017617173,0.000020919299,0.000028913844,0.00016347879,0.00014359449,0.89854634,0.0004478315,0.00027070433,0.05981179,0.02273511],"study_design_scores_gemma":[0.00049835857,0.000281211,0.0026832623,0.000051689767,0.0000028338914,0.000009266095,0.00018913079,0.8717936,0.0018338984,0.00025651217,0.122147,0.0002532067],"about_ca_topic_score_codex":0.000044810255,"about_ca_topic_score_gemma":0.000011803097,"teacher_disagreement_score":0.23344326,"about_ca_system_score_codex":0.00054192974,"about_ca_system_score_gemma":0.00005795684,"threshold_uncertainty_score":0.7884553},"labels":[],"label_agreement":null},{"id":"W4281664721","doi":"10.5539/eer.v12n1p45","title":"Long Term Electricity Load Forecast Based on Machine Learning for Cameroon’s Power System","year":2022,"lang":"en","type":"article","venue":"Energy and Environment Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"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":"Computer science; Mean absolute percentage error; Electrical load; Electric power system; Electricity; Term (time); Recurrent neural network; Particle swarm optimization; Population; Time horizon; Econometrics; Artificial intelligence; Artificial neural network; Power (physics); Machine learning; Mathematical optimization; Mathematics; Engineering","score_opus":0.01849066203069434,"score_gpt":0.22965250255153236,"score_spread":0.21116184052083803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4281664721","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":[0.90335995,0.005213941,0.04065731,0.00025943344,0.000590123,0.0006247296,0.000079454134,0.0005625391,0.048652515],"genre_scores_gemma":[0.9973878,0.00013212941,0.00009723242,0.000026443084,0.0000730559,0.00027978182,0.00006179482,0.000054529995,0.0018871773],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981558,0.00016622775,0.00017486095,0.0003152782,0.0006056772,0.00058216666],"domain_scores_gemma":[0.9993736,0.00026616367,0.000024032342,0.00020116665,0.000010348166,0.00012464948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090041093,0.00017655262,0.00016853663,0.00018910306,0.0007653019,0.000039489645,0.00016616109,0.000059420418,0.00027735662],"category_scores_gemma":[0.000020218426,0.00017590239,0.00006340035,0.00016520369,0.000048036654,0.000047883732,0.00011924955,0.0004888287,0.0000047357125],"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":[0.00016159586,0.00007817557,0.003939417,0.000105741754,0.000046742865,0.00003949446,0.00015119482,0.9570879,0.0022915062,0.0014171402,0.00044872152,0.034232378],"study_design_scores_gemma":[0.0009438764,0.0009629715,0.001672632,0.000049831633,0.000011295405,0.000017172286,0.00011565354,0.8802272,0.0048684613,0.000032660082,0.11073672,0.00036151064],"about_ca_topic_score_codex":0.00009855404,"about_ca_topic_score_gemma":0.00002223969,"teacher_disagreement_score":0.110288,"about_ca_system_score_codex":0.000591825,"about_ca_system_score_gemma":0.000021201447,"threshold_uncertainty_score":0.7173091},"labels":[],"label_agreement":null},{"id":"W4282823484","doi":"10.1007/s42835-022-01127-x","title":"A Decomposition-Based Improved Broad Learning System Model for Short-Term Load Forecasting","year":2022,"lang":"en","type":"article","venue":"Journal of Electrical Engineering and Technology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Jiangxi Provincial Department of Science and Technology; National Natural Science Foundation of China","keywords":"Computer science; Hilbert–Huang transform; Robustness (evolution); Artificial neural network; Artificial intelligence; Machine learning; Algorithm","score_opus":0.008052339355184429,"score_gpt":0.2044115246556603,"score_spread":0.19635918530047586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4282823484","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":[0.46438557,0.0018327092,0.5328607,0.00005193573,0.0002581199,0.00010309559,0.0000042457564,0.00044178218,0.000061811625],"genre_scores_gemma":[0.98904026,0.0000122420815,0.010767375,0.0000054615043,0.00007856951,0.000036425023,0.0000025661477,0.000044079054,0.000013010835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899024,0.000009236151,0.0003946875,0.000120716555,0.00014042159,0.00034467634],"domain_scores_gemma":[0.9995266,0.00014892059,0.00008098741,0.00007344845,0.00009396313,0.00007607199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027526828,0.00016031152,0.0003025112,0.00048773663,0.00016666399,0.000022037557,0.00015625081,0.00011038022,0.0000014255953],"category_scores_gemma":[0.00011016041,0.00016621196,0.00009212989,0.00044278015,0.00001223247,0.00005446892,0.00003271457,0.0007628509,7.546367e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000024169853,0.000013606601,0.00011851611,0.00009458635,0.000051338975,0.000020249934,0.000029789951,0.9482754,0.036531214,0.0005774523,0.0000203652,0.014243306],"study_design_scores_gemma":[0.00049523555,0.00042066467,0.000004909542,0.000062591236,0.000037529473,0.00057429314,0.000020294514,0.99475104,0.0029754061,0.000040713054,0.0004500412,0.00016729825],"about_ca_topic_score_codex":8.2555266e-7,"about_ca_topic_score_gemma":4.3914582e-7,"teacher_disagreement_score":0.5246547,"about_ca_system_score_codex":0.0002915581,"about_ca_system_score_gemma":0.000058706588,"threshold_uncertainty_score":0.6777927},"labels":[],"label_agreement":null},{"id":"W4283728899","doi":"10.5539/ijsp.v11n4p53","title":"Modeling Average Rainfall in Nigeria With Artificial Neural Network (ANN) Models and Seasonal Autoregressive Integrated Moving Average (SARIMA) Models","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"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":"Autoregressive integrated moving average; Artificial neural network; Levenberg–Marquardt algorithm; Conjugate gradient method; Gradient descent; Mathematics; Bayesian probability; Box–Jenkins; Mean squared error; Mean absolute percentage error; Statistics; Econometrics; Computer science; Algorithm; Time series; Artificial intelligence","score_opus":0.018099724056580044,"score_gpt":0.22888731602384269,"score_spread":0.21078759196726266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283728899","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":[0.6081717,0.00027117736,0.39067242,0.00005673127,0.00037913484,0.0000708081,0.0002040116,0.000015972848,0.0001580434],"genre_scores_gemma":[0.9803367,0.00007119789,0.019329771,0.00004637056,0.00014365777,0.0000073983715,0.000036506197,0.000021239419,0.000007115496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985728,0.00008781608,0.00052032224,0.00017491229,0.00042352045,0.00022059375],"domain_scores_gemma":[0.99930114,0.00014951797,0.00013642998,0.00006770613,0.00024695037,0.00009825269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005633106,0.00017595718,0.0002527641,0.00010596164,0.0001152955,0.00011946493,0.00017079434,0.000040617506,0.000033148666],"category_scores_gemma":[0.00004175055,0.00015746371,0.000033723172,0.000086783795,0.00005431483,0.0004019445,0.00010010898,0.0005394456,6.333599e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002745904,0.000032974658,0.0014765495,0.000023012697,0.00007683372,0.00014163046,0.0012756619,0.98112255,0.000033267977,0.008887542,0.000028570443,0.00662682],"study_design_scores_gemma":[0.00042783745,0.00008825085,0.00019390417,0.000075259704,0.0000090002095,0.00014931658,0.00008857536,0.90007716,0.000005050057,0.09870482,0.00003172453,0.0001490702],"about_ca_topic_score_codex":0.00006654019,"about_ca_topic_score_gemma":0.00017583433,"teacher_disagreement_score":0.37216505,"about_ca_system_score_codex":0.00017560206,"about_ca_system_score_gemma":0.000093180104,"threshold_uncertainty_score":0.6421184},"labels":[],"label_agreement":null},{"id":"W4283792445","doi":"10.3390/en15134895","title":"A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","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":"Carleton University","funders":"Jiangxi Provincial Department of Science and Technology","keywords":"Computer science; Algorithm; Hilbert–Huang transform; Residual; Robustness (evolution); Wind speed; Particle swarm optimization; Artificial intelligence; Pattern recognition (psychology); White noise","score_opus":0.01930731691702135,"score_gpt":0.2131837477409643,"score_spread":0.19387643082394296,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283792445","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":[0.9220984,0.0002935887,0.06789591,0.00001669188,0.0016897313,0.00019255141,0.0010144212,0.0007409935,0.0060577043],"genre_scores_gemma":[0.9972711,0.000024000374,0.0010954362,0.000022334329,0.00027777124,0.000054464428,0.00021647038,0.0000568803,0.000981529],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991461,0.000008174676,0.00019476724,0.00019589593,0.00018334546,0.00027169287],"domain_scores_gemma":[0.9996927,0.00005231315,0.000022571181,0.00015575277,0.000024538398,0.000052154315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010147598,0.00016079807,0.00014545723,0.00009216357,0.00022302284,0.000026894284,0.00014254566,0.00003073672,0.00003133672],"category_scores_gemma":[0.000013557352,0.00017835794,0.000087527034,0.000096722164,0.000023018929,0.00016036583,0.0000379482,0.00015601894,8.2034217e-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":[0.000027829617,0.000023433386,0.00016552066,0.000024191177,0.000055775232,0.0000018419913,0.0007227849,0.9499221,0.0459142,0.00030823896,0.002187716,0.00064641243],"study_design_scores_gemma":[0.00034195726,0.00008327842,0.00016457739,0.000014671725,0.000028016158,0.000025966148,0.00015214474,0.97719544,0.01875155,0.00016330047,0.002900942,0.0001781387],"about_ca_topic_score_codex":0.0000070624633,"about_ca_topic_score_gemma":0.0000037086609,"teacher_disagreement_score":0.07517271,"about_ca_system_score_codex":0.00009929191,"about_ca_system_score_gemma":0.00002238273,"threshold_uncertainty_score":0.7273225},"labels":[],"label_agreement":null},{"id":"W4283823152","doi":"10.18280/mmep.090311","title":"Modelling the Effect of Temperature on Power Generation at a Nigerian Agricultural Institute","year":2022,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"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":"Environmental science; Climate change; Linear regression; Wind speed; Meteorology; Energy consumption; Wind power; Multivariate statistics; Electricity; Bayesian multivariate linear regression; Heating degree day; Regression analysis; Artificial neural network; Statistics; Computer science; Mathematics; Geography; Engineering; Machine learning; Ecology","score_opus":0.012876422522340161,"score_gpt":0.17671762380581252,"score_spread":0.16384120128347235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283823152","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":[0.8934485,0.00040313168,0.10490008,0.000033379398,0.00029170766,0.0001827453,0.0000068964955,0.00017275642,0.0005608113],"genre_scores_gemma":[0.99805874,0.000021126145,0.0016051708,0.000006357489,0.000061900624,0.00009126318,0.000017567385,0.000036778245,0.00010112694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990947,0.000023266231,0.00026482553,0.00016580592,0.00021484733,0.00023650563],"domain_scores_gemma":[0.9996417,0.00009779937,0.000029228395,0.00016104507,0.000011573308,0.00005861184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002846122,0.00021618737,0.00023704159,0.00005666322,0.0002342583,0.000039193255,0.00011379922,0.000060728056,0.000016770608],"category_scores_gemma":[0.000008192999,0.00014117132,0.00007085528,0.0001459182,0.000020669668,0.00007017644,0.00005539419,0.00033228818,0.000002567308],"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":[0.000005325621,0.0000074050317,0.0000020140828,0.00019100898,0.00003113428,0.0000012436791,0.00088108965,0.98732495,0.007944197,0.003537885,0.00004266433,0.00003109277],"study_design_scores_gemma":[0.00016434451,0.00013077192,6.0196214e-7,0.00010807085,0.000017876519,0.00002860648,0.000014706815,0.99555504,0.0030557383,0.0001081621,0.0006423996,0.00017367015],"about_ca_topic_score_codex":0.000003775594,"about_ca_topic_score_gemma":3.2397318e-7,"teacher_disagreement_score":0.10461021,"about_ca_system_score_codex":0.000053656142,"about_ca_system_score_gemma":0.0000027371061,"threshold_uncertainty_score":0.57567996},"labels":[],"label_agreement":null},{"id":"W4284962670","doi":"10.3390/en15144993","title":"Transformer-Based Model for Electrical Load Forecasting","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":157,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Transformer; Computer science; Workflow; Electricity; Recurrent neural network; Architecture; Artificial intelligence; Artificial neural network; Engineering; Voltage; Electrical engineering","score_opus":0.025831570438522432,"score_gpt":0.2125115797646871,"score_spread":0.18668000932616466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4284962670","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":[0.5875633,0.0016773056,0.3852851,0.00011727067,0.0008963857,0.00027052566,0.0001117975,0.0013435605,0.022734772],"genre_scores_gemma":[0.9926376,0.0000068802306,0.0062925653,0.000083658415,0.00009552637,0.000250211,0.00003389823,0.000053290605,0.000546403],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910635,0.000010328629,0.00017867179,0.0001451575,0.00019808355,0.0003613941],"domain_scores_gemma":[0.9996967,0.000111041045,0.000018055254,0.00010496956,0.000021840338,0.000047434354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014804851,0.00014239004,0.00014258893,0.0000796652,0.00026414875,0.000021578822,0.00014689201,0.000034486853,0.000044441524],"category_scores_gemma":[0.000020557087,0.00015305728,0.00011343429,0.00019039489,0.00001329816,0.00007134071,0.000011490728,0.00015660095,9.171808e-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":[0.00002037615,0.000011655909,0.000016287795,0.000025046318,0.000014436216,0.0000020329433,0.00027761917,0.98301804,0.0030166097,0.0018433706,0.0013921526,0.010362398],"study_design_scores_gemma":[0.00038178673,0.00006369458,0.0000018954117,0.00000620207,0.000011907963,0.0000053147437,0.00003464629,0.9689882,0.010227249,0.00049565436,0.019588646,0.00019480714],"about_ca_topic_score_codex":0.000008628082,"about_ca_topic_score_gemma":0.000030539977,"teacher_disagreement_score":0.4050743,"about_ca_system_score_codex":0.00013869444,"about_ca_system_score_gemma":0.000069095055,"threshold_uncertainty_score":0.62414944},"labels":[],"label_agreement":null},{"id":"W4285122835","doi":"10.1109/access.2022.3187839","title":"Load Forecasting Techniques for Power System: Research Challenges and Survey","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":298,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Mean absolute percentage error; Mean squared error; Computer science; Electrical load; Electric power system; Electricity; Mean absolute error; Artificial neural network; Artificial intelligence; Power (physics); Machine learning; Operations research; Statistics; Engineering; Mathematics","score_opus":0.19008342578544343,"score_gpt":0.3457127124009007,"score_spread":0.15562928661545725,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285122835","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":[0.9108356,0.014573114,0.0034415007,0.00009416673,0.0023455478,0.0009636983,0.00018105727,0.0014336949,0.06613162],"genre_scores_gemma":[0.9988423,0.0001636712,0.00030415476,0.000009922315,0.00014677706,0.00037251358,0.000009467846,0.000056632154,0.00009456204],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987049,0.00013295037,0.00019890402,0.0002423503,0.0003148789,0.00040597745],"domain_scores_gemma":[0.9990043,0.0005628274,0.00003008888,0.00019973087,0.00013807185,0.000064979795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024898862,0.00013007659,0.00017872041,0.0001594221,0.00035962055,0.00009687752,0.00036555715,0.000054946697,0.000013547351],"category_scores_gemma":[0.00009676403,0.00013596746,0.00003211376,0.00021841361,0.0000315067,0.00021259178,0.00018387118,0.00029020038,9.756492e-7],"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":[0.0007316847,0.00032836737,0.010908265,0.011608661,0.00081932504,0.0003285549,0.026260884,0.061592504,0.014035409,0.018742202,0.06876847,0.7858757],"study_design_scores_gemma":[0.004003441,0.0025075413,0.0148249185,0.002272712,0.00011314297,0.0008699147,0.0124021,0.3857652,0.12457103,0.0053509925,0.442255,0.005064001],"about_ca_topic_score_codex":0.00013486952,"about_ca_topic_score_gemma":0.00022578688,"teacher_disagreement_score":0.78081167,"about_ca_system_score_codex":0.00016193582,"about_ca_system_score_gemma":0.000028388573,"threshold_uncertainty_score":0.5544592},"labels":[],"label_agreement":null},{"id":"W4285174309","doi":"10.2139/ssrn.4133744","title":"Foreseeing the Worst: Forecasting Electricity DART Spikes","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; HEC Montréal","funders":"","keywords":"Dart; Electricity; Computer science; Econometrics; Economics; Engineering; Electrical engineering","score_opus":0.010482392701904474,"score_gpt":0.19357717760637436,"score_spread":0.18309478490446987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285174309","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":[0.9223703,0.01518162,0.03681782,0.0006166554,0.0017150164,0.00023261535,0.0000066185744,0.0004581145,0.022601238],"genre_scores_gemma":[0.9979721,0.00051893183,0.00007075171,0.000064276195,0.00058399834,0.00002318386,0.0000046546797,0.000061285726,0.0007008739],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99643475,0.00009622626,0.0003207255,0.0001572249,0.00039588189,0.0025952016],"domain_scores_gemma":[0.999495,0.00013255516,0.000092976275,0.0001798391,0.000029834764,0.00006976362],"candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0017692053,0.00020239981,0.00017088364,0.00014468467,0.0014112891,0.000094142924,0.0005326765,0.000037429316,0.00009081733],"category_scores_gemma":[0.00006543968,0.00016163521,0.00015484326,0.0005512704,0.000026944705,0.00017968767,0.000104726896,0.0036546546,0.0000060287657],"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":[0.00007412571,0.000060238504,0.004122141,0.000027219063,0.00043738267,0.00007890984,0.0016291871,0.6390105,0.0023859618,0.12859312,0.0020184205,0.22156282],"study_design_scores_gemma":[0.0018694383,0.0011453814,0.00033225943,0.00008253127,0.00018027754,0.018479897,0.007994042,0.531834,0.0020234522,0.29206625,0.14242078,0.001571668],"about_ca_topic_score_codex":0.000034534303,"about_ca_topic_score_gemma":0.0003560328,"teacher_disagreement_score":0.21999115,"about_ca_system_score_codex":0.0010483322,"about_ca_system_score_gemma":0.00039310261,"threshold_uncertainty_score":0.9998887},"labels":[],"label_agreement":null},{"id":"W4285310856","doi":"10.1109/icjece.2022.3152524","title":"An Effective Very Short-Term Wind Speed Prediction Approach Using Multiple Regression Models","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wind speed; Wind power; Statistic; Computer science; Random forest; Renewable energy; Term (time); Decision tree; Pearson product-moment correlation coefficient; Statistics; Data mining; Meteorology; Artificial intelligence; Engineering; Mathematics","score_opus":0.011074144546544389,"score_gpt":0.18117398546065305,"score_spread":0.17009984091410865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285310856","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":[0.7184825,0.00094637985,0.27982643,0.0000014425522,0.000610161,0.00005661917,0.000006352128,0.00003423008,0.000035864457],"genre_scores_gemma":[0.99617535,0.000010229611,0.0033355474,0.000008962898,0.00042997467,9.1475124e-7,0.000007106652,0.000030007066,0.0000019347135],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991921,0.000035042656,0.00022204616,0.00012025698,0.00013144904,0.00029907032],"domain_scores_gemma":[0.9994115,0.000057590634,0.000028784696,0.00007497131,0.00002981368,0.00039733853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016932354,0.0001494593,0.00020107017,0.00034037768,0.0001586729,0.000055793276,0.00012963897,0.00005118918,0.000003926655],"category_scores_gemma":[0.0000071848067,0.00014781971,0.00005880167,0.00025389518,0.000009481132,0.00025855072,0.000015060979,0.00046382932,4.9467392e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000068140153,0.000008610075,0.0014845468,0.000018903978,0.000038615824,0.000059182414,0.00024745282,0.9820416,0.0029060997,0.000043428823,0.000039626608,0.013105098],"study_design_scores_gemma":[0.00021022125,0.0001813755,0.0015048368,0.00004710686,0.000018728517,0.0007033282,0.000008363958,0.996749,0.00021851706,0.000018983003,0.00019115656,0.00014838735],"about_ca_topic_score_codex":0.00006913271,"about_ca_topic_score_gemma":0.0000105404,"teacher_disagreement_score":0.2776928,"about_ca_system_score_codex":0.00023398324,"about_ca_system_score_gemma":0.000055053508,"threshold_uncertainty_score":0.60279125},"labels":[],"label_agreement":null},{"id":"W4285394250","doi":"10.3390/math10142446","title":"A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Artificial neural network; Leverage (statistics); Sliding window protocol; Artificial intelligence; Robustness (evolution); Backpropagation; Deep belief network; Machine learning; Time series; Algorithm; Window (computing)","score_opus":0.04615669422489035,"score_gpt":0.2367919893232591,"score_spread":0.19063529509836874,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285394250","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":[0.27971846,0.000057502704,0.70855105,0.00001829311,0.00028788697,0.0003686932,0.000064104715,0.00065863016,0.010275409],"genre_scores_gemma":[0.94087875,0.0000014732175,0.058504447,0.000042879085,0.00007236498,0.00016652337,0.000042390944,0.000114293805,0.00017688383],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987347,0.000017364522,0.00032167943,0.00019147972,0.00033542074,0.00039934972],"domain_scores_gemma":[0.998998,0.0006210728,0.000057289966,0.0002225656,0.00003696347,0.00006413948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044867123,0.0002204787,0.00023953375,0.000095318195,0.00041508034,0.00005110425,0.00019281813,0.000047864996,0.000040963794],"category_scores_gemma":[0.00028309884,0.00023904508,0.00012124431,0.00013888895,0.0000115300645,0.00006404199,0.00007284318,0.00033619226,0.000002175983],"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":[0.000008956021,0.000044853707,0.0002453203,0.00031417512,0.000018328014,0.0000061124515,0.0016459499,0.9931376,0.0012238388,0.00031108994,0.0001640077,0.0028797488],"study_design_scores_gemma":[0.00035422493,0.00008291812,0.0000023275813,0.000114908085,0.00002238392,0.0000059964686,0.0002736414,0.99592763,0.0011865033,0.00059340615,0.0011681295,0.00026794602],"about_ca_topic_score_codex":0.0000010222274,"about_ca_topic_score_gemma":0.000005098615,"teacher_disagreement_score":0.6611603,"about_ca_system_score_codex":0.00019322455,"about_ca_system_score_gemma":0.000032692846,"threshold_uncertainty_score":0.97479755},"labels":[],"label_agreement":null},{"id":"W4285495285","doi":"10.5539/eer.v12n2p11","title":"Wind Speed Forecasting using Machine Learning Approach based on Meteorological Data-A case study","year":2022,"lang":"en","type":"article","venue":"Energy and Environment Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"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":"Wind speed; Computer science; Artificial neural network; MATLAB; Wind power; Machine learning; Artificial intelligence; Time series; Software; Data mining; Meteorology","score_opus":0.15425061605504006,"score_gpt":0.2971544442610145,"score_spread":0.14290382820597444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285495285","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":[0.99465334,0.0003913231,0.0013915969,0.000019220248,0.00008717322,0.00014728139,0.000020555191,0.00008216107,0.0032073713],"genre_scores_gemma":[0.99810565,0.00002653872,0.0012804166,0.0000248775,0.00010235316,0.00001364601,0.0001319456,0.000047973346,0.00026661833],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99739206,0.0006152727,0.00023371035,0.00053374044,0.00068029406,0.00054489664],"domain_scores_gemma":[0.999066,0.00030372688,0.000027977669,0.00045562987,0.00000663238,0.00014006771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022757626,0.00020552041,0.00021037576,0.00022687475,0.001178649,0.000058927624,0.00030183047,0.0000560653,0.0003616027],"category_scores_gemma":[0.00005462007,0.00019147908,0.00003169017,0.0002478874,0.000084274754,0.00009598254,0.00081199204,0.0008700265,0.0000014806432],"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":[0.000038754282,0.00021790837,0.0027789187,0.000012024891,0.000041763004,0.0013826787,0.00024918214,0.98813677,0.00041699607,0.000032707554,0.000032843134,0.0066594426],"study_design_scores_gemma":[0.0005154267,0.00050743594,0.00004689382,0.0000041454527,0.000017086035,0.0004577051,0.0014604244,0.9863216,0.00007620486,0.0000148388235,0.010379726,0.00019846176],"about_ca_topic_score_codex":0.0006304987,"about_ca_topic_score_gemma":0.000021185995,"teacher_disagreement_score":0.010346883,"about_ca_system_score_codex":0.00013717121,"about_ca_system_score_gemma":0.00001226333,"threshold_uncertainty_score":0.9065335},"labels":[],"label_agreement":null},{"id":"W4285654064","doi":"10.14741/ijcet/v.10.5.6","title":"Prediction Rainfall in 2020 in Telangana","year":2020,"lang":"en","type":"article","venue":"International Journal of Current Engineering and Technology","topic":"Energy Load and Power Forecasting","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":"Memorial University of Newfoundland","funders":"","keywords":"Fast Fourier transform; Linear regression; Statistics; Artificial neural network; Root mean square; Value (mathematics); Mathematics; Regression; Mean squared error; Power (physics); Series (stratigraphy); Econometrics; Computer science; Algorithm; Artificial intelligence; Engineering; Geology; Electrical engineering","score_opus":0.011113092007457224,"score_gpt":0.2151301442501081,"score_spread":0.20401705224265088,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285654064","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":[0.9879091,0.0043304884,0.0052183988,0.00094105373,0.00134785,0.000026138025,0.000005241516,0.00010018142,0.00012155905],"genre_scores_gemma":[0.9988554,0.00064678834,0.00029670264,0.000008044282,0.00018086589,0.0000013497614,0.0000017953947,0.000008368083,6.514839e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995149,0.0000029029513,0.00025336523,0.000052926574,0.00008821145,0.00008769348],"domain_scores_gemma":[0.9998625,0.000018937744,0.000031208514,0.000022752636,0.000031625445,0.00003295884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000641093,0.000066887595,0.00010975922,0.0003574949,0.0000029197383,0.000009442791,0.00012655208,0.000054322714,0.0000039163883],"category_scores_gemma":[0.00008758194,0.000067817346,0.00001753572,0.00021043755,0.000008980758,0.00008484072,0.000021182948,0.00034169198,7.7368344e-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":[0.0000337593,0.000052953288,0.085616,0.00011500988,0.00011753089,0.00036550223,0.0009330529,0.72210646,0.014494645,0.004453257,0.00060119404,0.17111063],"study_design_scores_gemma":[0.002578266,0.0001825796,0.011154982,0.0010523055,0.000013006305,0.00048639168,0.00014200596,0.904258,0.0049148155,0.0006934934,0.07419033,0.00033378878],"about_ca_topic_score_codex":8.2009973e-7,"about_ca_topic_score_gemma":0.0000026132707,"teacher_disagreement_score":0.18215157,"about_ca_system_score_codex":0.000036361147,"about_ca_system_score_gemma":0.000007842358,"threshold_uncertainty_score":0.27655113},"labels":[],"label_agreement":null},{"id":"W4286490390","doi":"10.3390/en15145299","title":"Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"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":"Computer science; Term (time); Wavelet; Wavelet transform; Energy (signal processing); Data mining; Electricity; Grid; Decomposition; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Machine learning; Engineering; Statistics; Mathematics","score_opus":0.025134761923050326,"score_gpt":0.22908317012224616,"score_spread":0.20394840819919582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286490390","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":[0.86508805,0.00052019715,0.10611136,0.00003923156,0.0006162304,0.00010396187,0.00004475135,0.00090284564,0.026573382],"genre_scores_gemma":[0.938063,0.000003052306,0.06114165,0.00005851665,0.00012261007,0.00009860741,0.00003135329,0.00007839067,0.0004027654],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99876285,0.000030055422,0.00020600857,0.00024067903,0.00035102203,0.00040941712],"domain_scores_gemma":[0.9994643,0.00015630371,0.000031592866,0.00024394656,0.00003424095,0.00006962677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026217932,0.00023055304,0.0002185024,0.000109246525,0.00027441003,0.000047597183,0.00020284386,0.000039054343,0.00009671785],"category_scores_gemma":[0.000017245713,0.0002176703,0.00007191241,0.0003166642,0.00002375337,0.000100224315,0.00008323891,0.00025224866,0.0000017065063],"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":[0.00002200337,0.000021418195,0.00019232383,0.000039477192,0.00004467201,0.000045142995,0.00041207994,0.94334924,0.025328033,0.0007340272,0.0003088442,0.029502755],"study_design_scores_gemma":[0.0009339013,0.00020713333,0.00039023234,0.000095347044,0.000053357882,0.00021654925,0.00042525964,0.87502515,0.081978105,0.000060572092,0.039789155,0.0008252483],"about_ca_topic_score_codex":0.000035018926,"about_ca_topic_score_gemma":0.00007264918,"teacher_disagreement_score":0.07297502,"about_ca_system_score_codex":0.00015107289,"about_ca_system_score_gemma":0.000057659116,"threshold_uncertainty_score":0.88763374},"labels":[],"label_agreement":null},{"id":"W4287021065","doi":"10.48550/arxiv.2108.10825","title":"Adaptive Group Lasso Neural Network Models for Functions of Few Variables and Time-Dependent Data","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Dalhousie University; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial neural network; Lasso (programming language); Constraint (computer-aided design); Penalty method; Computer science; Function (biology); Algorithm; Mathematical optimization; Artificial intelligence; Mathematics","score_opus":0.08335694360462259,"score_gpt":0.17255098263507576,"score_spread":0.08919403903045317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287021065","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":[0.14326309,0.0010656365,0.84771883,0.0000061774476,0.0010454969,0.0003049981,0.00066407816,0.00024616445,0.0056854985],"genre_scores_gemma":[0.9961003,0.00029438976,0.0020926083,0.000007612554,0.00017066157,0.0000015074993,0.00058451446,0.000042198204,0.00070618326],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988955,0.00004332248,0.00018070456,0.00055899704,0.000048978316,0.00027244823],"domain_scores_gemma":[0.99889076,0.00018521679,0.000094434086,0.0006644472,0.00007424303,0.00009090183],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017453398,0.0002445251,0.00034745777,0.00008001036,0.00009565324,0.000040930747,0.00041404733,0.00022199731,0.000028124598],"category_scores_gemma":[0.000013001838,0.00030102764,0.00009014377,0.0001852468,0.00004705028,0.00029983526,0.0009334991,0.0003120849,0.0000014636597],"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":[0.000030471107,0.000019153233,0.00013561892,0.0001331166,0.00026925583,0.000019419027,0.00004968526,0.99166566,0.000038477214,0.006897379,0.00046325068,0.0002785234],"study_design_scores_gemma":[0.00029811385,0.00003071588,0.000032320386,0.00017932939,0.00024608793,0.0000033585902,0.00009941215,0.9945757,0.00001581118,0.004040989,0.00021045055,0.0002677388],"about_ca_topic_score_codex":0.000119769866,"about_ca_topic_score_gemma":0.00015633856,"teacher_disagreement_score":0.85283726,"about_ca_system_score_codex":0.000054751992,"about_ca_system_score_gemma":0.000039692484,"threshold_uncertainty_score":0.9999442},"labels":[],"label_agreement":null},{"id":"W4287219816","doi":"10.3390/su14159021","title":"A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Energy Load and Power Forecasting","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":"Carleton University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Wind speed; Robustness (evolution); Generator (circuit theory); Artificial neural network; Noise (video); Artificial intelligence; Machine learning; Algorithm","score_opus":0.012537485227988665,"score_gpt":0.2283206470817591,"score_spread":0.21578316185377042,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287219816","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":[0.9075145,0.00010479988,0.08860037,0.00006925624,0.001606182,0.0007501284,0.0002636663,0.00036855962,0.00072255806],"genre_scores_gemma":[0.9981491,0.000002873345,0.0005702697,0.000022929456,0.00065764645,0.00006551688,0.00017778866,0.000035998593,0.00031789532],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878365,0.00005555046,0.00026095271,0.0002841723,0.00016953201,0.00044615794],"domain_scores_gemma":[0.99944437,0.000074653304,0.000024536599,0.00024038952,0.00013765118,0.00007837706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044538116,0.00017042083,0.00018298835,0.000040489565,0.00039544422,0.000026797841,0.00013652867,0.000041378597,0.000049608236],"category_scores_gemma":[0.0000916066,0.00019325598,0.00012254472,0.00012635402,0.000039284663,0.00013976499,0.000054616565,0.00024071902,2.7015406e-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":[0.00009673333,0.00002970787,0.0024431015,0.00008257742,0.00003747187,0.000004729612,0.0010750991,0.99174565,0.000185227,0.0006062912,0.0023096793,0.0013837534],"study_design_scores_gemma":[0.00038060598,0.0000955365,0.00043758267,0.0000034333152,0.000027206754,0.00000585581,0.00025318158,0.9869896,0.0003684409,0.009190089,0.002060864,0.00018759286],"about_ca_topic_score_codex":0.000012066451,"about_ca_topic_score_gemma":0.0000069006865,"teacher_disagreement_score":0.0906346,"about_ca_system_score_codex":0.00096719986,"about_ca_system_score_gemma":0.00015415839,"threshold_uncertainty_score":0.78807503},"labels":[],"label_agreement":null},{"id":"W4288443117","doi":"10.3390/en15155472","title":"A Multi-Hour Ahead Wind Power Forecasting System Based on a WRF-TOPSIS-ANFIS Model","year":2022,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":9,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adaptive neuro fuzzy inference system; Weather Research and Forecasting Model; Wind power forecasting; Wind speed; Wind power; TOPSIS; Renewable energy; Computer science; Meteorology; Electric power system; Power (physics); Engineering; Fuzzy logic; Artificial intelligence; Operations research; Fuzzy control system; Geography","score_opus":0.02480131585906165,"score_gpt":0.20627903493480818,"score_spread":0.18147771907574653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288443117","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":[0.9523825,0.0004471589,0.009849477,0.000053254098,0.0013682571,0.00015773292,0.000092702045,0.0016161326,0.034032807],"genre_scores_gemma":[0.9913619,0.0000020169934,0.007468951,0.00011415921,0.00009618223,0.0000841319,0.000021292975,0.000109084984,0.00074228534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985023,0.000057612888,0.00030740685,0.00030049615,0.00035448186,0.00047766863],"domain_scores_gemma":[0.9993309,0.00014589305,0.000059410337,0.00034199638,0.000028382266,0.00009344523],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027205225,0.00029131267,0.00027463253,0.00021434328,0.00036209045,0.00005824906,0.00026583756,0.00006829281,0.00008850751],"category_scores_gemma":[0.000041247436,0.00030190748,0.00014909115,0.00028434963,0.000020372483,0.000112028836,0.00010333382,0.00033118378,0.000010681379],"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":[0.000018602484,0.000028228125,0.00013231285,0.00006969489,0.000030066318,0.000042607862,0.0007226348,0.99516934,0.0014744633,0.00082026224,0.0007579697,0.00073381595],"study_design_scores_gemma":[0.00045279477,0.00006427223,0.000044680604,0.00013279718,0.000016491507,0.000019266068,0.0005648305,0.9933388,0.0016864996,0.000010397982,0.0033267962,0.0003423411],"about_ca_topic_score_codex":0.00004062203,"about_ca_topic_score_gemma":0.000012203647,"teacher_disagreement_score":0.038979415,"about_ca_system_score_codex":0.00022687213,"about_ca_system_score_gemma":0.000038273647,"threshold_uncertainty_score":0.9999433},"labels":[],"label_agreement":null},{"id":"W4289638334","doi":"10.1063/5.0098090","title":"Short-term wind speed forecasting with regime-switching and mixture models at multiple weather stations over a large geographical area","year":2022,"lang":"en","type":"article","venue":"Journal of Renewable and Sustainable Energy","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wind speed; Cluster analysis; Meteorology; Term (time); Environmental science; Markov chain; Wind direction; Atmospheric model; Computer science; Geography; Machine learning","score_opus":0.009903022148437843,"score_gpt":0.19288954942625033,"score_spread":0.1829865272778125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289638334","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":[0.9784665,0.0034628254,0.016095882,0.000042640757,0.00011384424,0.00006181902,0.000011310058,0.000039869385,0.0017053093],"genre_scores_gemma":[0.99690706,0.00036956885,0.0005871032,0.00004530638,0.00011454077,0.0000037299417,0.000012278215,0.000055717155,0.0019046951],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985027,0.000049756723,0.00035082983,0.00019186662,0.00031970127,0.0005852031],"domain_scores_gemma":[0.9992881,0.00013649372,0.00012697895,0.00013387548,0.000115961884,0.00019859387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041368432,0.0002335347,0.00033253836,0.00032294673,0.0006745668,0.00010399055,0.00011843919,0.00007645139,0.000034476237],"category_scores_gemma":[0.000022402357,0.00019713302,0.000079599304,0.00033644246,0.000029308803,0.0004784541,0.00017218257,0.00031807052,1.7205544e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016091828,0.00004212345,0.017961802,0.00008453151,0.0001655175,0.00055083196,0.00090140017,0.97614795,0.001156624,0.0009738702,0.00063570664,0.0012187163],"study_design_scores_gemma":[0.002725849,0.0005422888,0.0005827616,0.00019654777,0.00016842692,0.002163628,0.008901203,0.93887526,0.0007748501,0.0027387256,0.04165599,0.00067443936],"about_ca_topic_score_codex":0.00034294283,"about_ca_topic_score_gemma":0.00037742397,"teacher_disagreement_score":0.041020285,"about_ca_system_score_codex":0.00016976277,"about_ca_system_score_gemma":0.000053001168,"threshold_uncertainty_score":0.80388516},"labels":[],"label_agreement":null},{"id":"W4290996503","doi":"10.1109/icc45855.2022.9838768","title":"Pandemic-Aware Electric Load Forecasting: A Multitask Bidirectional LSTM/CNN Model","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Pandemic; Artificial intelligence; Machine learning; Coronavirus disease 2019 (COVID-19); Medicine","score_opus":0.1385256884350423,"score_gpt":0.3120193157355071,"score_spread":0.17349362730046483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4290996503","genre_codex":"other","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":[0.26008952,0.0015352977,0.034292027,0.0040126364,0.0072189593,0.0009630115,0.0019050495,0.0029542835,0.68702924],"genre_scores_gemma":[0.99501795,0.00048046134,0.0013671463,0.00020156313,0.000115298324,0.0004169435,0.00033258772,0.000048242975,0.002019803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831903,0.000095561176,0.00039830126,0.00027233205,0.00062233256,0.00029241707],"domain_scores_gemma":[0.99857396,0.0002544604,0.00011153578,0.00073929905,0.00022898064,0.000091735616],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00030904426,0.00022717344,0.00017717617,0.0002969048,0.00061260373,0.00008568406,0.0015361848,0.00006522877,0.0010103772],"category_scores_gemma":[0.000065946486,0.00027103603,0.000121613346,0.00041548614,0.000053189236,0.00018377724,0.00032140285,0.0008997499,0.000053330616],"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":[0.000058257316,0.00033251778,0.0009280868,0.000015072263,0.00028161283,0.000007419229,0.0010142003,0.9011116,0.017292747,0.051787186,0.00974464,0.017426645],"study_design_scores_gemma":[0.0003123322,0.000046005083,0.000060524922,0.000030288766,0.000014418231,0.000040744613,0.00015751693,0.9762832,0.00034508435,0.002861518,0.019569548,0.00027880326],"about_ca_topic_score_codex":0.00008249202,"about_ca_topic_score_gemma":0.00021132173,"teacher_disagreement_score":0.7349284,"about_ca_system_score_codex":0.0006911496,"about_ca_system_score_gemma":0.00021745749,"threshold_uncertainty_score":0.9999742},"labels":[],"label_agreement":null},{"id":"W4292685294","doi":"10.1016/j.cliser.2022.100318","title":"A simplified seasonal forecasting strategy, applied to wind and solar power in Europe","year":2022,"lang":"en","type":"article","venue":"Climate Services","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Impact","funders":"European Centre for Medium-Range Weather Forecasts; European Commission","keywords":"Hindcast; Climatology; Meteorology; Environmental science; Wind speed; Solar irradiance; Probabilistic logic; Renewable energy; Scale (ratio); Wind power; Geography; Statistics; Mathematics; Engineering","score_opus":0.013752941058531335,"score_gpt":0.2067053906813876,"score_spread":0.19295244962285626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292685294","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":[0.96710235,0.0002260716,0.000013148377,0.000027612201,0.00016123315,0.000118310534,0.00004728038,0.00016015809,0.032143835],"genre_scores_gemma":[0.99935883,0.000017902037,0.00020539747,0.000247639,0.000045411027,0.000019563275,0.000038637078,0.000049394843,0.00001724959],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989533,0.000021327334,0.00021443676,0.0002376942,0.00014554705,0.0004277105],"domain_scores_gemma":[0.99967456,0.000044680208,0.000030549392,0.00012796377,0.000017771159,0.000104501254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022712548,0.00017339675,0.00017396646,0.0001063638,0.00017567279,0.00008131983,0.00017559182,0.000032727563,0.00013762711],"category_scores_gemma":[0.0000032748057,0.00019078747,0.000017664313,0.00046116216,0.000009228886,0.00010680123,0.00025953105,0.00022562307,0.000014454989],"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":[0.000101218386,0.000052060455,0.017683633,0.0006596093,0.00004415823,0.0001199976,0.0063438555,0.9488449,0.009700584,0.0020393436,0.000080997655,0.014329647],"study_design_scores_gemma":[0.003417615,0.00061678456,0.0865009,0.0005041304,0.000070458,0.00024707604,0.015244653,0.7956591,0.0024312397,0.00073954597,0.0917433,0.0028251754],"about_ca_topic_score_codex":0.000023177297,"about_ca_topic_score_gemma":0.00013963322,"teacher_disagreement_score":0.15318577,"about_ca_system_score_codex":0.000030954336,"about_ca_system_score_gemma":0.000010419677,"threshold_uncertainty_score":0.77800876},"labels":[],"label_agreement":null},{"id":"W4292995155","doi":"10.1155/2022/1696663","title":"An Improved Load Forecasting Method Based on the Transfer Learning Structure under Cyber-Threat Condition","year":2022,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Energy Load and Power Forecasting","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 British Columbia","funders":"China Scholarship Council","keywords":"Computer science; Smart grid; Scheduling (production processes); Cyber-physical system; Transfer of learning; Electric power system; Grid; Reliability (semiconductor); Distributed computing; Artificial intelligence; Real-time computing; Machine learning; Power (physics); Mathematical optimization; Engineering","score_opus":0.04086422542575698,"score_gpt":0.2770740549681589,"score_spread":0.2362098295424019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292995155","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":[0.25518665,0.000030316578,0.7439185,0.000112232075,0.0002965846,0.000088945366,0.000019637158,0.0000953329,0.00025183757],"genre_scores_gemma":[0.997621,0.0000031897507,0.001353159,0.0009236722,0.000033104025,0.000015025,0.000017363443,0.000015529798,0.00001796111],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998981,0.0001101939,0.00015182764,0.00026619268,0.00029915042,0.00019163085],"domain_scores_gemma":[0.99934363,0.00045082739,0.000021709324,0.00008795045,0.00003783946,0.0000580451],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026300474,0.00012938402,0.00008237498,0.00006550515,0.00073097664,0.000090806774,0.00019764775,0.000022305801,0.000088443354],"category_scores_gemma":[0.00004148419,0.00010663254,0.00003016645,0.00032182812,0.00008205099,0.00015199691,0.00002437302,0.00035736532,6.711501e-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":[0.000006811834,0.000009293467,0.000053226267,0.000005195357,0.0000012211909,0.0000027190333,0.0002505052,0.9796733,0.008982988,0.0030343784,0.000005691153,0.00797467],"study_design_scores_gemma":[0.000043935295,0.0001833789,0.00031608526,0.000009367606,0.000003982638,0.000033024677,0.00017330959,0.98790723,0.007712205,0.0032817086,0.00020993986,0.00012583076],"about_ca_topic_score_codex":0.000008198748,"about_ca_topic_score_gemma":0.000004171461,"teacher_disagreement_score":0.74256533,"about_ca_system_score_codex":0.000043399523,"about_ca_system_score_gemma":0.000033834927,"threshold_uncertainty_score":0.5622155},"labels":[],"label_agreement":null},{"id":"W4293194132","doi":"10.1109/tii.2022.3151798","title":"Wind Power Prediction Interval Based on Predictive Density Estimation Within a New Hybrid Structure","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":24,"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 Saskatchewan; University of Regina","funders":"","keywords":"Model predictive control; Computer science; Prediction interval; Quadratic programming; Quantile; Mathematical optimization; Algorithm; Wind power; Probabilistic logic; Mathematics; Artificial intelligence; Machine learning; Statistics; Engineering","score_opus":0.018185300335042474,"score_gpt":0.20649239944236047,"score_spread":0.188307099107318,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293194132","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":[0.37903652,0.000002079132,0.6126703,0.00003655084,0.005465825,0.0003428225,0.0007813156,0.00047586198,0.0011887396],"genre_scores_gemma":[0.9982878,7.290735e-7,0.0011853721,0.00014622827,0.00014761859,0.000017443874,0.00009431349,0.000034820954,0.000085685126],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852306,0.000050733976,0.0005853881,0.00011831312,0.0004790668,0.00024342047],"domain_scores_gemma":[0.999352,0.00009749978,0.00012775378,0.00024790558,0.00003465217,0.00014020732],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018619142,0.0002522049,0.00021031541,0.00030732385,0.00037575088,0.000071510265,0.0001592404,0.00014578845,0.00040562454],"category_scores_gemma":[0.000016867802,0.000267073,0.00010410721,0.0003526058,0.000028732411,0.00039497876,0.0000028296247,0.0012784105,0.000011341344],"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":[0.0002178397,0.000037701866,0.0000133268795,0.000012867512,0.00006090281,0.000002240583,0.0016285819,0.98859406,0.00005524537,0.000012933011,0.0020322276,0.007332062],"study_design_scores_gemma":[0.0013806807,0.00069578097,0.00001767407,0.000080748905,0.000060147446,0.000028773602,0.00040194488,0.97753227,0.018730747,0.000054967993,0.00079517136,0.00022107035],"about_ca_topic_score_codex":0.000029194181,"about_ca_topic_score_gemma":0.0000088551005,"teacher_disagreement_score":0.61925125,"about_ca_system_score_codex":0.0003952268,"about_ca_system_score_gemma":0.0001404667,"threshold_uncertainty_score":0.9999781},"labels":[],"label_agreement":null},{"id":"W4293208583","doi":"10.1007/978-981-16-7156-2_38","title":"Morphological Clustering Algorithm of Daily Output Curve of Wind Farm Based on Multi-scale and Entropy Weight Method","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Cluster analysis; Mathematics; Euclidean distance; Entropy (arrow of time); Quantile; Algorithm; Scale (ratio); Mathematical optimization; Measure (data warehouse); Computer science; Data mining; Statistics; Geography; Geometry","score_opus":0.01078831115807695,"score_gpt":0.21633390732578617,"score_spread":0.20554559616770923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293208583","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0004845998,0.0025805137,0.99272907,0.000014767347,0.00039765178,0.00024296786,0.00007269598,0.00019121997,0.0032864942],"genre_scores_gemma":[0.43674046,0.0007773563,0.5602465,0.00012874074,0.0005518146,0.000049049824,0.00019631782,0.0006017725,0.00070797146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99791914,0.000035180205,0.0006796728,0.00048192943,0.00037969265,0.00050436374],"domain_scores_gemma":[0.99849063,0.00092084927,0.00013042509,0.00030866623,0.00003355436,0.00011589246],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026331077,0.00060219766,0.00096831727,0.00063527375,0.00003382069,0.0000132566565,0.00026225467,0.00054927415,0.00010419835],"category_scores_gemma":[0.00014781668,0.00059608597,0.00021287697,0.0002564541,0.000038957838,0.000035537403,0.00009878919,0.0016086729,7.612222e-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":[0.000028309596,0.000030765306,0.00004257727,0.0002016512,0.0000679299,0.000068236826,0.0000841074,0.91727006,0.0048946557,0.00036355082,0.00000226964,0.0769459],"study_design_scores_gemma":[0.0007311286,0.00025987375,0.00005858834,0.00026124014,0.00005274484,0.000037613878,4.644701e-7,0.9866544,0.0074326214,0.00012845502,0.003852977,0.0005298638],"about_ca_topic_score_codex":0.000029226092,"about_ca_topic_score_gemma":0.000011096022,"teacher_disagreement_score":0.43625584,"about_ca_system_score_codex":0.00020845074,"about_ca_system_score_gemma":0.000026256914,"threshold_uncertainty_score":0.99964905},"labels":[],"label_agreement":null},{"id":"W4293223634","doi":"10.11159/eee22.102","title":"Electric Load Estimation and Prediction Using Periodic Steady State Kalman Filter","year":2022,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Energy Load and Power Forecasting","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":"Kalman filter; Control theory (sociology); Moving horizon estimation; Extended Kalman filter; Computer science; Steady state (chemistry); Fast Kalman filter; Estimation; State (computer science); Invariant extended Kalman filter; Artificial intelligence; Engineering; Algorithm","score_opus":0.007794277202114484,"score_gpt":0.18999275623388745,"score_spread":0.18219847903177297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293223634","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":[0.99667317,0.0008713315,0.0012599505,0.000017553106,0.0008286683,0.00013071149,0.000003103798,0.00009759782,0.000117922355],"genre_scores_gemma":[0.9995113,0.000021983742,0.00029090425,0.000009857959,0.0000572155,0.000013828535,1.8056201e-7,0.0000120341165,0.00008272311],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998992,0.00000503887,0.00019679742,0.0002237987,0.0003298615,0.0002524551],"domain_scores_gemma":[0.9997153,0.000037286678,0.000058564692,0.000056117504,0.000055393364,0.00007732739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033748202,0.00013525542,0.00016418517,0.00024934023,0.00030556277,0.00016871454,0.00016095921,0.000016731723,4.4750192e-7],"category_scores_gemma":[0.000014299533,0.000110814464,0.000018268214,0.0008099352,0.00005346145,0.00018356385,0.000108885404,0.00022199952,4.8435385e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015830923,0.00001887114,0.002269647,0.00028170107,0.000028924818,0.0000012400478,0.00062393816,0.949388,0.026925744,0.0041426267,0.00015639653,0.016147068],"study_design_scores_gemma":[0.00014031488,0.00010090297,0.0013443786,0.000108998654,0.000008896534,0.00007495059,0.000009553908,0.9964629,0.0012536014,0.000016111542,0.00036112565,0.00011829802],"about_ca_topic_score_codex":0.000014199483,"about_ca_topic_score_gemma":3.6741324e-7,"teacher_disagreement_score":0.047074854,"about_ca_system_score_codex":0.00009495543,"about_ca_system_score_gemma":0.000019945737,"threshold_uncertainty_score":0.4518883},"labels":[],"label_agreement":null},{"id":"W4293321959","doi":"10.1002/joc.7839","title":"Forecasting occurrence and quantity of monthly precipitation simultaneously while accounting for complex serial correlation","year":2022,"lang":"en","type":"article","venue":"International Journal of Climatology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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 New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Precipitation; Skewness; Series (stratigraphy); Autoregressive model; Poisson distribution; Time series; Autocorrelation; Autoregressive integrated moving average; Statistics; Poisson regression; Kurtosis; Econometrics; Mathematics; Environmental science; Climatology; Meteorology; Geography; Geology","score_opus":0.034769325427904216,"score_gpt":0.2718002830020563,"score_spread":0.23703095757415205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293321959","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":[0.98090404,0.00014332272,0.015647458,0.000063694526,0.0026424753,0.00006863981,0.00015676644,0.000015599753,0.000358013],"genre_scores_gemma":[0.99562764,0.000013656703,0.0041185073,0.000018412078,0.0001426015,0.0000046984037,0.00006313911,0.000009054114,0.0000022992826],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990686,0.00003345655,0.00051341014,0.00007011738,0.0002042431,0.00011016003],"domain_scores_gemma":[0.9986854,0.0005503997,0.00040512986,0.000034642217,0.0003002306,0.00002417717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003205399,0.000074822776,0.00018760478,0.00015667362,0.000072947856,0.000019523397,0.00016584058,0.000034069828,0.00004264398],"category_scores_gemma":[0.000267969,0.00008202901,0.000058435875,0.000060343646,0.000028393086,0.0001942003,0.000049231912,0.00014799924,2.0534235e-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":[0.00027394565,0.000033695916,0.029392023,0.000055830864,0.000108737986,0.000016938337,0.00094882713,0.9576201,0.0023768893,0.00158864,0.00039139795,0.0071929963],"study_design_scores_gemma":[0.00088544353,0.00018149184,0.0012566395,0.00004811785,0.000024752855,0.00052694476,0.00029435416,0.99243826,0.00033141352,0.00068405183,0.0032316286,0.000096925076],"about_ca_topic_score_codex":0.000008476591,"about_ca_topic_score_gemma":0.000020437992,"teacher_disagreement_score":0.034818158,"about_ca_system_score_codex":0.00004588577,"about_ca_system_score_gemma":0.000025095002,"threshold_uncertainty_score":0.33450457},"labels":[],"label_agreement":null},{"id":"W4294839706","doi":"10.5539/ijsp.v11n5p30","title":"Forecasting Hydropower Generation in Ghana Using ARIMA Models","year":2022,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"Autoregressive integrated moving average; Hydropower; Renewable energy; Electricity generation; Production (economics); Electricity; Investment (military); Sustainable development; Operations management; Environmental science; Environmental economics; Business; Agricultural economics; Engineering; Economics; Time series; Mathematics; Power (physics); Statistics; Political science","score_opus":0.04560892833374964,"score_gpt":0.2584614522356099,"score_spread":0.21285252390186027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4294839706","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":[0.84278286,0.0002221164,0.15559725,0.000028981696,0.00081198977,0.000039917555,0.00009511432,0.000006064044,0.00041571588],"genre_scores_gemma":[0.95322776,0.000016692637,0.04657059,0.00002138753,0.00013363184,0.000001665283,0.000014401417,0.000009445021,0.000004450566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991331,0.000037082766,0.0003881793,0.00007497331,0.00026908648,0.000097572665],"domain_scores_gemma":[0.99962586,0.00006238616,0.000107011656,0.000040825038,0.00012681591,0.000037088437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046408983,0.00007252681,0.00011183877,0.000113494105,0.000054038603,0.000039332583,0.00011025289,0.000018038116,0.00005674062],"category_scores_gemma":[0.000050868242,0.00007510058,0.000024062918,0.00006346609,0.000020791913,0.00016467419,0.00005165004,0.00020942047,7.020694e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019221581,0.000028297121,0.0011544584,0.0000126874465,0.000029969238,0.000048643666,0.0004814061,0.98193514,0.0003646523,0.004604906,0.00007863675,0.0112419985],"study_design_scores_gemma":[0.00024398259,0.00003990109,0.00008664352,0.000014731879,0.0000061754963,0.00015924257,0.000056126344,0.9742553,0.000113454655,0.02473438,0.00021641912,0.000073613526],"about_ca_topic_score_codex":0.000041456875,"about_ca_topic_score_gemma":0.000041743075,"teacher_disagreement_score":0.11044488,"about_ca_system_score_codex":0.00019639167,"about_ca_system_score_gemma":0.000036757614,"threshold_uncertainty_score":0.30625126},"labels":[],"label_agreement":null},{"id":"W4295064639","doi":"10.11113/aej.v12.17276","title":"NEXT-HOUR ELECTRICITY PRICE FORECASTING USING LEAST SQUARES SUPPORT VECTOR MACHINE AND GENETIC ALGORITHM","year":2022,"lang":"en","type":"article","venue":"ASEAN Engineering Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Universiti Teknikal Malaysia Melaka","keywords":"Bidding; Electricity price forecasting; Electricity market; Mean absolute percentage error; Genetic algorithm; Support vector machine; Electricity; Computer science; Least squares support vector machine; Least-squares function approximation; Econometrics; Algorithm; Mathematical optimization; Artificial neural network; Machine learning; Economics; Statistics; Mathematics; Engineering; Microeconomics","score_opus":0.014921460039499847,"score_gpt":0.1963336794711232,"score_spread":0.18141221943162336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295064639","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":[0.6903314,0.0035287852,0.3039667,0.000022985068,0.0013376223,0.000114090224,0.00003945499,0.000395519,0.00026347185],"genre_scores_gemma":[0.9626213,0.000088437235,0.03647743,0.000026021584,0.0006192639,0.0000074483564,0.000010125786,0.00011979642,0.000030175992],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834967,0.000037981816,0.00040194308,0.00020536635,0.0003491296,0.00065592077],"domain_scores_gemma":[0.99939066,0.000082197024,0.00008516851,0.00013546164,0.000036493915,0.00027002997],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032320982,0.00029548287,0.00026358324,0.00031779733,0.0004916663,0.00016702624,0.00021488853,0.000050353654,0.00017787999],"category_scores_gemma":[0.000051039904,0.00033230474,0.000095876196,0.0004172813,0.000015091012,0.0002543548,0.000112275906,0.0009421847,0.0000016723311],"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":[0.000005528955,0.00001592005,0.00054209394,0.00004983919,0.00007759938,0.00040597093,0.00024126422,0.95365494,0.005651763,0.000017329041,0.00013824108,0.03919949],"study_design_scores_gemma":[0.00037178613,0.00013005057,0.0010349234,0.000040506984,0.000036488385,0.007188588,0.00005551083,0.98583025,0.00063033623,0.000013314947,0.004294541,0.00037369004],"about_ca_topic_score_codex":0.000018345101,"about_ca_topic_score_gemma":0.0000013465324,"teacher_disagreement_score":0.27228993,"about_ca_system_score_codex":0.00024383744,"about_ca_system_score_gemma":0.000048210033,"threshold_uncertainty_score":0.9999129},"labels":[],"label_agreement":null},{"id":"W4296312576","doi":"10.3390/app12189288","title":"Hybrid Model Based on an SD Selection, CEEMDAN, and Deep Learning for Short-Term Load Forecasting of an Electric Vehicle Fleet","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Energy Load and Power Forecasting","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":"Natural Resources Canada","funders":"","keywords":"Hilbert–Huang transform; Computer science; Artificial neural network; Noise (video); Mean absolute percentage error; Term (time); Mode (computer interface); Artificial intelligence; Selection (genetic algorithm); Electric power system; White noise; Power (physics)","score_opus":0.019250747347459466,"score_gpt":0.22760118871693494,"score_spread":0.20835044136947548,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296312576","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":[0.97713405,0.000051672836,0.019240275,0.000004683331,0.000060130522,0.00013713397,0.0000035914663,0.00013740108,0.003231047],"genre_scores_gemma":[0.9955512,0.000002089499,0.0042482396,0.000031469986,0.000046611734,0.00007581314,0.0000107402775,0.000021994072,0.000011792314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988698,0.00002170019,0.00018723767,0.00029901476,0.00029587603,0.00032640542],"domain_scores_gemma":[0.9996664,0.00010229387,0.00004466979,0.00008229924,0.000029559786,0.00007481103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067061855,0.000129988,0.0001423543,0.00015488951,0.0007129877,0.000052774787,0.00019316976,0.000022875836,0.000009995248],"category_scores_gemma":[0.000019594983,0.0001344684,0.000026611893,0.0004122133,0.00004957403,0.0001608054,0.000026678521,0.00017077224,1.7195875e-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":[0.000019083083,0.00002104111,0.0005225795,0.000018012472,0.0000035337503,4.284636e-7,0.0002545067,0.92080927,0.04354885,0.00052758964,0.000005287887,0.034269825],"study_design_scores_gemma":[0.00017916583,0.00041303717,0.000113966424,0.0000058453998,0.0000082141605,0.000005987938,0.00013195681,0.9696816,0.028812636,0.00042396205,0.00006141596,0.00016216893],"about_ca_topic_score_codex":0.000010259879,"about_ca_topic_score_gemma":0.00002668437,"teacher_disagreement_score":0.04887238,"about_ca_system_score_codex":0.000054714073,"about_ca_system_score_gemma":0.000050911127,"threshold_uncertainty_score":0.5483797},"labels":[],"label_agreement":null},{"id":"W4296704829","doi":"10.1109/tsg.2022.3208211","title":"Efficient Residential Electric Load Forecasting via Transfer Learning and Graph Neural Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":100,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Transfer of learning; Artificial intelligence; Reuse; Machine learning; Graph; Artificial neural network; Domain (mathematical analysis); Maximum power transfer theorem; Electrical load; Data mining; Power (physics); Engineering","score_opus":0.00913551936133966,"score_gpt":0.1914121520910935,"score_spread":0.18227663272975383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4296704829","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":[0.5123715,0.0003596096,0.48403978,0.000016504535,0.0023223187,0.00011030088,0.000007990361,0.000341,0.000430991],"genre_scores_gemma":[0.99940664,0.000043659275,0.00007862997,0.000032380205,0.0001962447,0.000069560716,0.0000053283534,0.00006399255,0.0001035677],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985712,0.00008718293,0.0002816156,0.00028072402,0.0003116403,0.00046759547],"domain_scores_gemma":[0.9996064,0.0001232527,0.00001984769,0.00011559414,0.000024462097,0.00011046908],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024539803,0.0002325496,0.0001951899,0.00024385379,0.0008403375,0.000049529102,0.00010358815,0.000064387044,0.00015736435],"category_scores_gemma":[0.0000022957188,0.00026533467,0.00013695091,0.0006251486,0.000026373367,0.000058821726,0.0000023207467,0.0009775116,0.000002847276],"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":[0.000050216593,0.000029615632,0.000046740475,0.00001655397,0.00004447928,0.000020207714,0.00027143725,0.9755926,0.0022764907,0.00000406877,0.00004644313,0.021601154],"study_design_scores_gemma":[0.00045505996,0.00018736703,0.000052573927,0.0000147673145,0.000052690837,0.00015401389,0.000051712526,0.9947479,0.0029157845,0.000005557257,0.0010827597,0.0002798636],"about_ca_topic_score_codex":0.000070796,"about_ca_topic_score_gemma":0.000075058815,"teacher_disagreement_score":0.48703513,"about_ca_system_score_codex":0.00010961527,"about_ca_system_score_gemma":0.000015078847,"threshold_uncertainty_score":0.9999799},"labels":[],"label_agreement":null},{"id":"W4298143196","doi":"10.1049/rpg2.12595","title":"Day‐ahead wind power ramp forecasting using an image‐based similarity search strategy","year":2022,"lang":"en","type":"article","venue":"IET Renewable Power Generation","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Natural Resources Canada; University of New Brunswick","funders":"Natural Resources Canada","keywords":"Wind power; Wind power forecasting; Probabilistic forecasting; Electric power system; Wind speed; Probabilistic logic; Computer science; Meteorology; Reliability engineering; Power (physics); Engineering; Artificial intelligence","score_opus":0.07015025476217301,"score_gpt":0.26980870971174264,"score_spread":0.19965845494956963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298143196","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":[0.9214495,0.000311107,0.066660255,0.000039143,0.0014869632,0.0002634251,0.00009617459,0.0004107985,0.009282666],"genre_scores_gemma":[0.99132377,0.000004133079,0.00732808,0.0001287615,0.00037608657,0.000023308145,0.00047811802,0.00011848765,0.00021924863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99748874,0.00026079244,0.0004749814,0.0005048605,0.0005700473,0.00070057495],"domain_scores_gemma":[0.9990531,0.00005943737,0.000079704725,0.00048022714,0.00012616442,0.00020137057],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00095475675,0.00035209415,0.00028192456,0.00024286514,0.0008408373,0.00030723566,0.00028147918,0.00013233825,0.0010864484],"category_scores_gemma":[0.000033883643,0.00041573157,0.000109780805,0.00054529775,0.000045159486,0.00066085556,0.000099152705,0.0004497984,0.000005870206],"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":[0.000011353063,0.00006012806,0.00016142061,0.0000146071225,0.000021648917,0.000024298226,0.00046828046,0.76832664,0.22966076,0.000043634587,0.0006960061,0.00051122403],"study_design_scores_gemma":[0.00047161776,0.00020264044,0.00004470752,0.000016556569,0.000019608959,0.000031819385,0.0003671824,0.9117725,0.082989894,0.000048574257,0.0035780065,0.00045690223],"about_ca_topic_score_codex":0.00044176241,"about_ca_topic_score_gemma":0.0003219922,"teacher_disagreement_score":0.14667086,"about_ca_system_score_codex":0.00026934952,"about_ca_system_score_gemma":0.00019382553,"threshold_uncertainty_score":0.9998295},"labels":[],"label_agreement":null},{"id":"W4298143225","doi":"10.1049/rpg2.12597","title":"A post‐forecast weighing algorithm to improve wind power forecasting capabilities","year":2022,"lang":"en","type":"article","venue":"IET Renewable Power Generation","topic":"Energy Load and Power Forecasting","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":"Natural Resources Canada; University of Fredericton; University of New Brunswick","funders":"Natural Resources Canada","keywords":"Wind power; Computer science; Wind power forecasting; Power (physics); Meteorology; Electric power system; Engineering; Electrical engineering; Geography","score_opus":0.012187184923545752,"score_gpt":0.19932166753795968,"score_spread":0.18713448261441393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298143225","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":[0.94429374,0.0006556514,0.029756939,0.00020759387,0.00784274,0.00054977974,0.00033649738,0.0006938464,0.015663229],"genre_scores_gemma":[0.98415196,0.000006728572,0.012134862,0.00039675878,0.00069059036,0.00016642426,0.00023099255,0.00013728415,0.002084426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976317,0.00008013219,0.0005396856,0.00052367925,0.0004979481,0.00072683196],"domain_scores_gemma":[0.9990681,0.00006902254,0.0000836758,0.0004316325,0.00013391525,0.00021368722],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00052373897,0.00038790843,0.000311301,0.00027909904,0.0006892568,0.00021312262,0.00028466317,0.00010279015,0.00081635313],"category_scores_gemma":[0.00005869739,0.00043703572,0.00014237563,0.00047911968,0.000027443226,0.0004290922,0.00020409639,0.00032900882,0.000023298904],"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":[0.000015034634,0.00003467417,0.00007611522,0.000018681027,0.000056746747,0.000019775289,0.0042646164,0.8683012,0.11084712,0.00016702003,0.004962893,0.011236084],"study_design_scores_gemma":[0.00077966426,0.0007847482,0.000045743745,0.000046332203,0.00003786366,0.00011932755,0.0021766676,0.8071856,0.06858466,0.00024290469,0.11879644,0.0012000899],"about_ca_topic_score_codex":0.0004970552,"about_ca_topic_score_gemma":0.00025112633,"teacher_disagreement_score":0.11383355,"about_ca_system_score_codex":0.00036385484,"about_ca_system_score_gemma":0.000088964945,"threshold_uncertainty_score":0.99980813},"labels":[],"label_agreement":null},{"id":"W4300417637","doi":"","title":"Electrical resistivity survey to investigate biogas migration under leachate recirculation events","year":2004,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Energy Load and Power Forecasting","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":"Suez (Canada)","funders":"","keywords":"Leachate; Biogas; Electrical resistivity and conductivity; Environmental science; Waste management; Engineering; Electrical engineering","score_opus":0.025408751287786933,"score_gpt":0.22791511092285455,"score_spread":0.2025063596350676,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300417637","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":[0.6797554,0.0005517469,0.310591,0.0019700476,0.0004868791,0.00038858553,0.00008901539,0.00059781375,0.005569488],"genre_scores_gemma":[0.9855877,0.00031398132,0.011272667,0.000064395186,0.000040195442,0.000053140102,0.0015134824,0.00008241717,0.0010719843],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.995048,0.0029026435,0.0005525945,0.00065618206,0.00041407516,0.00042652854],"domain_scores_gemma":[0.99677813,0.00073167845,0.00020992022,0.0011642402,0.00083886436,0.00027713893],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.004268204,0.00039620072,0.00036517467,0.00031435516,0.00024107225,0.00021906804,0.0006008914,0.00038852144,0.000029202232],"category_scores_gemma":[0.0013897369,0.00046403307,0.00014895563,0.0006870789,0.000063783424,0.00014616692,0.0003814541,0.00070574164,0.000055082364],"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":[0.00006597868,0.00071833574,0.048842482,0.00052814843,0.00048248775,0.000012169499,0.01105654,0.7755354,0.027420735,0.07259106,0.0030094527,0.059737228],"study_design_scores_gemma":[0.0012063916,0.0000021354815,0.5581423,0.0060175136,0.00013387174,0.000016573918,0.00002959717,0.27445832,0.12402321,0.02446674,0.009112384,0.0023909626],"about_ca_topic_score_codex":0.0042232974,"about_ca_topic_score_gemma":0.016687252,"teacher_disagreement_score":0.5092998,"about_ca_system_score_codex":0.00044697482,"about_ca_system_score_gemma":0.00021202542,"threshold_uncertainty_score":0.99978113},"labels":[],"label_agreement":null},{"id":"W4300436850","doi":"10.1109/icc45855.2022.9839249","title":"Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home","year":2022,"lang":"en","type":"article","venue":"ICC 2022 - IEEE International Conference on Communications","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":64,"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 Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Computer science; Generative model; Divergence (linguistics); Data modeling; Smart grid; Synthetic data; Key (lock); Data mining; Artificial intelligence; Generative adversarial network; Data quality; Generative grammar; Grid; Machine learning; Deep learning; Mathematics; Engineering","score_opus":0.1467061214693945,"score_gpt":0.32309803676808996,"score_spread":0.17639191529869547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300436850","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":[0.030361464,0.0014730787,0.7318896,0.021518467,0.033346932,0.0030093307,0.015498189,0.0011205412,0.1617824],"genre_scores_gemma":[0.9659127,0.0001996333,0.02361563,0.0002814074,0.00062863436,0.00070349796,0.00819012,0.000037881313,0.0004305227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855214,0.00016922099,0.00040358942,0.000313949,0.00034047663,0.00022063521],"domain_scores_gemma":[0.9983271,0.00032013594,0.00009690731,0.0010926389,0.00011786845,0.000045383073],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056209636,0.00016366449,0.00015750069,0.0001763164,0.00044561835,0.00010133642,0.0018324525,0.000052499257,0.00086045696],"category_scores_gemma":[0.000072686926,0.0001991883,0.00005179255,0.00023695074,0.000047013968,0.0002953817,0.00042381397,0.00042327607,0.00001564489],"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":[0.000026528682,0.00009963982,0.00013570594,0.0000034425639,0.000089166766,0.0000011651902,0.00037220452,0.81771183,0.00093879155,0.16477896,0.01398114,0.0018614401],"study_design_scores_gemma":[0.00043946202,0.00003925959,0.00016622925,0.000017115275,0.000012949393,0.000005103057,0.00009068064,0.9568059,0.000036594065,0.003969857,0.03821649,0.00020035329],"about_ca_topic_score_codex":0.0000719457,"about_ca_topic_score_gemma":0.00092284154,"teacher_disagreement_score":0.9355512,"about_ca_system_score_codex":0.00028616557,"about_ca_system_score_gemma":0.00013350364,"threshold_uncertainty_score":0.94214004},"labels":[],"label_agreement":null},{"id":"W4302350288","doi":"10.1109/iwssip55020.2022.9854390","title":"Comprehensive Electric load forecasting using ensemble machine learning methods","year":2022,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"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":"European Regional Development Fund","keywords":"Computer science; Electricity; Electrical load; Electric power system; Population; Load profile; Grid; Load management; Electric power; Ensemble learning; Machine learning; Artificial intelligence; Power (physics); Engineering; Electrical engineering","score_opus":0.048193872664637864,"score_gpt":0.27805185392273996,"score_spread":0.22985798125810208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302350288","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":[0.59637266,0.005500473,0.3255902,0.000015087623,0.0011271772,0.00016821647,0.0000045918578,0.0013752875,0.06984628],"genre_scores_gemma":[0.91331977,0.000023703638,0.08569002,0.00007395861,0.00010783424,0.000015459966,0.000013154169,0.00008088008,0.00067519146],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986024,0.00017504542,0.00027417231,0.00021950003,0.00024712086,0.0004817667],"domain_scores_gemma":[0.9994141,0.00026649417,0.000052089264,0.00014062943,0.00004778262,0.00007892663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039743164,0.00021226793,0.00024295409,0.0001656611,0.00053532305,0.000038687856,0.00016111793,0.00004034566,0.0004887429],"category_scores_gemma":[0.000050510826,0.00023080502,0.00009315651,0.0006725517,0.000009678159,0.00010355468,0.00015952653,0.0006350563,0.0000058089227],"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":[0.0000061753635,0.0000073693705,0.00027427846,0.000028710465,0.00004059071,0.000019119238,0.00028654095,0.8403227,0.11040087,0.0001728055,0.000077392055,0.048363466],"study_design_scores_gemma":[0.0002114332,0.000056379442,0.000013477223,0.000008725835,0.000017686381,0.00020504968,0.00016665633,0.9326503,0.016141502,0.00011121233,0.05014828,0.00026927138],"about_ca_topic_score_codex":0.0001871191,"about_ca_topic_score_gemma":0.000009945763,"teacher_disagreement_score":0.3169471,"about_ca_system_score_codex":0.00027811306,"about_ca_system_score_gemma":0.00003589059,"threshold_uncertainty_score":0.94119555},"labels":[],"label_agreement":null},{"id":"W4304630580","doi":"10.3389/fenrg.2022.1038819","title":"A modified deep residual network for short-term load forecasting","year":2022,"lang":"en","type":"article","venue":"Frontiers in Energy Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"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":"Residual; Generalization; Computer science; Term (time); Artificial intelligence; Deep learning; Artificial neural network; Position (finance); Electric power system; Data mining; Machine learning; Reliability engineering; Power (physics); Algorithm; Engineering; Mathematics","score_opus":0.05880915961555307,"score_gpt":0.2835155722730671,"score_spread":0.22470641265751407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304630580","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":[0.33906582,0.0240814,0.50415355,0.00035951275,0.010965523,0.0011832246,0.00013868717,0.0010402553,0.119012],"genre_scores_gemma":[0.98025316,0.00017534528,0.016535956,0.000047252877,0.00063609076,0.00090451224,0.00011044173,0.0001189116,0.0012183323],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99712384,0.00025634054,0.00035343828,0.00037335197,0.00071297464,0.0011800311],"domain_scores_gemma":[0.9991443,0.0003156615,0.000021121186,0.00030589334,0.000087634995,0.00012539787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023627633,0.00019607818,0.00028334622,0.00037504785,0.00049695553,0.000068646936,0.00050214265,0.000096782576,0.00003295746],"category_scores_gemma":[0.00011466742,0.00022827306,0.00008735879,0.0008917799,0.00006997478,0.00013033414,0.0002871386,0.0007356362,5.1754307e-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":[0.00011212876,0.000023657183,0.0024561062,0.00003746539,0.000044562283,0.00004441331,0.0004942966,0.9185323,0.00008222305,0.003338844,0.034855537,0.039978474],"study_design_scores_gemma":[0.0005296251,0.00013081226,0.00013370112,0.000044054115,0.0000053177264,0.000014860662,0.00042328401,0.9255928,0.00021042918,0.0052310126,0.0673902,0.0002939456],"about_ca_topic_score_codex":0.00010692355,"about_ca_topic_score_gemma":0.00041696258,"teacher_disagreement_score":0.6411873,"about_ca_system_score_codex":0.0005851531,"about_ca_system_score_gemma":0.00012061592,"threshold_uncertainty_score":0.93087053},"labels":[],"label_agreement":null},{"id":"W4307444508","doi":"10.32920/21408579.v1","title":"Performance comparison of single and ensemble CNN, LSTM and traditional ANN models for short-term electricity load forecasting","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Boosting (machine learning); Artificial intelligence; Convolutional neural network; Machine learning; Ensemble forecasting; Artificial neural network; Term (time); Deep learning; Consistency (knowledge bases); Ensemble learning; Gradient boosting; Random forest","score_opus":0.09620230674950049,"score_gpt":0.2523171165336219,"score_spread":0.1561148097841214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307444508","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":[0.9482118,0.0015787674,0.033066127,0.000007438779,0.00030787976,0.00028561903,0.00010795661,0.000146313,0.01628813],"genre_scores_gemma":[0.99236023,0.00014229062,0.006988379,0.000008733105,0.00012097842,0.00007606951,0.00016924264,0.00004905026,0.00008503983],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986212,0.0000147077935,0.0004643118,0.00034237464,0.00024117045,0.00031618334],"domain_scores_gemma":[0.99940425,0.00019803194,0.00008877336,0.00015820585,0.00006973597,0.000081027974],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023438118,0.0002930369,0.00048499517,0.00012326615,0.0001532091,0.000047449277,0.00013351133,0.00016800225,0.000029588988],"category_scores_gemma":[0.000017843653,0.00031932868,0.00008245679,0.000089224224,0.00004172076,0.00015051405,0.00017147354,0.00041501052,9.2584756e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049558872,0.000079959325,0.004364293,0.0017402857,0.000119410455,0.0000014179869,0.0010150014,0.94705164,0.0042092605,0.00064312917,0.0004820597,0.040243987],"study_design_scores_gemma":[0.00022233337,0.00017395601,0.0003748713,0.00015908648,0.00004759706,0.000016461128,0.000048875907,0.9901291,0.007314922,0.0010066113,0.00016947079,0.00033670015],"about_ca_topic_score_codex":0.000020024649,"about_ca_topic_score_gemma":0.000029764233,"teacher_disagreement_score":0.04414845,"about_ca_system_score_codex":0.00011656914,"about_ca_system_score_gemma":0.000046550693,"threshold_uncertainty_score":0.99992585},"labels":[],"label_agreement":null},{"id":"W4307445408","doi":"10.32920/21408579","title":"Performance comparison of single and ensemble CNN, LSTM and traditional ANN models for short-term electricity load forecasting","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Boosting (machine learning); Artificial intelligence; Convolutional neural network; Machine learning; Ensemble forecasting; Artificial neural network; Term (time); Deep learning; Consistency (knowledge bases); Gradient boosting; Ensemble learning; Random forest","score_opus":0.09620230674950049,"score_gpt":0.2523171165336219,"score_spread":0.1561148097841214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4307445408","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":[0.9482118,0.0015787674,0.033066127,0.000007438779,0.00030787976,0.00028561903,0.00010795661,0.000146313,0.01628813],"genre_scores_gemma":[0.99236023,0.00014229062,0.006988379,0.000008733105,0.00012097842,0.00007606951,0.00016924264,0.00004905026,0.00008503983],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986212,0.0000147077935,0.0004643118,0.00034237464,0.00024117045,0.00031618334],"domain_scores_gemma":[0.99940425,0.00019803194,0.00008877336,0.00015820585,0.00006973597,0.000081027974],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023438118,0.0002930369,0.00048499517,0.00012326615,0.0001532091,0.000047449277,0.00013351133,0.00016800225,0.000029588988],"category_scores_gemma":[0.000017843653,0.00031932868,0.00008245679,0.000089224224,0.00004172076,0.00015051405,0.00017147354,0.00041501052,9.2584756e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000049558872,0.000079959325,0.004364293,0.0017402857,0.000119410455,0.0000014179869,0.0010150014,0.94705164,0.0042092605,0.00064312917,0.0004820597,0.040243987],"study_design_scores_gemma":[0.00022233337,0.00017395601,0.0003748713,0.00015908648,0.00004759706,0.000016461128,0.000048875907,0.9901291,0.007314922,0.0010066113,0.00016947079,0.00033670015],"about_ca_topic_score_codex":0.000020024649,"about_ca_topic_score_gemma":0.000029764233,"teacher_disagreement_score":0.04414845,"about_ca_system_score_codex":0.00011656914,"about_ca_system_score_gemma":0.000046550693,"threshold_uncertainty_score":0.99992585},"labels":[],"label_agreement":null},{"id":"W4308090769","doi":"10.1109/ccece49351.2022.9918402","title":"A Proposed Adaptive Filter for Harmonics Mitigation based on Adaptive Neuro Fuzzy Inference System Model for Hybrid Wind Solar Energy System","year":2022,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Harmonics; Adaptive neuro fuzzy inference system; Photovoltaic system; Renewable energy; Computer science; Wind power; Wind speed; Filter (signal processing); Distributed generation; Control theory (sociology); Control engineering; Fuzzy logic; Engineering; Fuzzy control system; Artificial intelligence; Meteorology; Electrical engineering","score_opus":0.025233165509441904,"score_gpt":0.20560575403252185,"score_spread":0.18037258852307994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308090769","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":[0.0075236117,0.00003266533,0.98591286,0.00003957354,0.0005855107,0.00083024666,0.0006421459,0.00065655744,0.0037768052],"genre_scores_gemma":[0.9851882,6.2622775e-7,0.013433914,0.00014921407,0.000106247615,0.00059822464,0.00017717801,0.00010119945,0.00024520152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845695,0.00005614764,0.0003805949,0.0003936972,0.00028469632,0.0004278817],"domain_scores_gemma":[0.9989654,0.00042704004,0.00010425129,0.0002662349,0.00013342034,0.00010367098],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023497925,0.00030717062,0.00030778418,0.00015131652,0.0003237021,0.000050203736,0.00022004097,0.00006090172,0.0000065325094],"category_scores_gemma":[0.00003131058,0.00030730155,0.00017183376,0.0001427019,0.000016491464,0.00014830142,0.00004631283,0.00017822388,0.0000018900084],"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":[0.00022138545,0.0000239668,0.0000024934407,0.00028104658,0.000045770714,0.00000503714,0.00014750703,0.9483231,0.0008035541,0.048593055,0.0008711834,0.0006819262],"study_design_scores_gemma":[0.0009761017,0.00052567624,0.0000016859385,0.00016536281,0.000046084773,0.000007866995,0.00040995484,0.9852476,0.011401769,0.00019352206,0.0006738816,0.00035047298],"about_ca_topic_score_codex":0.000037532416,"about_ca_topic_score_gemma":0.00001906293,"teacher_disagreement_score":0.9776646,"about_ca_system_score_codex":0.00042856473,"about_ca_system_score_gemma":0.000111912006,"threshold_uncertainty_score":0.9999379},"labels":[],"label_agreement":null},{"id":"W4308090797","doi":"10.1109/ccece49351.2022.9918237","title":"Investigating the Impact of Increasing Renewable Energy Penetration Levels on the Accuracy of Net Load Forecasting","year":2022,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Renewable energy; Photovoltaic system; Penetration (warfare); Environmental economics; Electricity; Wind power; Electric power system; Renewable resource; Environmental science; Computer science; Power (physics); Engineering; Economics; Electrical engineering; Operations research","score_opus":0.044962404985268156,"score_gpt":0.24653158207892084,"score_spread":0.20156917709365268,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308090797","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":[0.95685476,0.00014884208,0.0011552243,0.000037708407,0.000116074036,0.000078568504,0.000017896857,0.00006765885,0.04152329],"genre_scores_gemma":[0.9992178,0.000005154305,0.0004633493,0.000058408215,0.00007458446,0.000018266657,0.000009822185,0.000028480636,0.00012409913],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987796,0.00016472978,0.00038672917,0.00011736012,0.0003165083,0.00023509836],"domain_scores_gemma":[0.99802893,0.0014215067,0.00019056012,0.00026728254,0.00005471634,0.000037000522],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007225951,0.00015402104,0.00016801193,0.00005939768,0.00029715942,0.00002966028,0.00028267756,0.000031089086,0.00026894404],"category_scores_gemma":[0.0006042748,0.00009252296,0.00011241791,0.0004294188,0.000048651462,0.00012336728,0.000102457474,0.00019350057,3.400683e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000067021874,0.000009310045,0.0013016621,0.0000134736665,0.000044477423,4.111648e-7,0.0006105663,0.94148797,0.05131316,0.0012196127,0.0009500097,0.003042617],"study_design_scores_gemma":[0.00022592262,0.00019566693,0.0025136052,0.00011387514,0.000018006545,0.000032716525,0.0008685543,0.90064096,0.09308356,0.0016721272,0.00043074097,0.00020426577],"about_ca_topic_score_codex":0.011657909,"about_ca_topic_score_gemma":0.00036995191,"teacher_disagreement_score":0.0423631,"about_ca_system_score_codex":0.000122001766,"about_ca_system_score_gemma":0.000103106104,"threshold_uncertainty_score":0.99492353},"labels":[],"label_agreement":null},{"id":"W4308217826","doi":"10.1109/powercon53406.2022.9929886","title":"An Imputation Reinforcement Learning Agent For Power Transformers Load Study","year":2022,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Polytechnique Montréal","funders":"","keywords":"Imputation (statistics); Reinforcement learning; Computer science; Transformer; Artificial intelligence; Machine learning; Reinforcement; Data mining; Reliability engineering; Voltage; Engineering; Missing data; Electrical engineering","score_opus":0.011948636289113364,"score_gpt":0.2393246158971541,"score_spread":0.22737597960804073,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4308217826","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":[0.770789,0.000042614054,0.20410576,0.0000155717,0.0005669803,0.0004981265,0.0000026517566,0.00046101853,0.02351827],"genre_scores_gemma":[0.99870986,0.00000230137,0.00032167925,0.000041338222,0.000025204206,0.00015532192,0.000035839537,0.000027276848,0.00068118516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930376,0.000017932447,0.00017158594,0.0001160735,0.00019478844,0.0001958607],"domain_scores_gemma":[0.99984074,0.000020392185,0.000015677819,0.000064287364,0.000018383871,0.000040515537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026003557,0.000095378025,0.00008220195,0.000052055035,0.00022451216,0.000023441711,0.00007016064,0.00001358795,0.00049850385],"category_scores_gemma":[0.0000041512326,0.00009832031,0.000045030603,0.00009643282,0.0000033618376,0.00011332642,0.0000105584995,0.00011305244,0.0000030763156],"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":[0.000016044414,0.000032656775,0.00021177882,0.000009445755,0.00003267042,0.0000015534504,0.0049912827,0.9827974,0.0008669398,0.00012911182,0.00016925468,0.010741904],"study_design_scores_gemma":[0.0011817822,0.0019066333,0.00023755939,0.000004045504,0.000025601972,0.000003684867,0.014551319,0.9380013,0.0011320717,0.000025020267,0.042637076,0.0002939357],"about_ca_topic_score_codex":0.00003237522,"about_ca_topic_score_gemma":0.000022047485,"teacher_disagreement_score":0.22792085,"about_ca_system_score_codex":0.00014252999,"about_ca_system_score_gemma":0.000014996063,"threshold_uncertainty_score":0.5458268},"labels":[],"label_agreement":null},{"id":"W4309345632","doi":"10.1109/smc53654.2022.9945528","title":"Improving imbalanced dataset classification using Conditional Classifier-Generator (cCGen)","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Energy Load and Power Forecasting","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 British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Classifier (UML); Machine learning; Artificial intelligence; Generator (circuit theory); Sampling (signal processing); Oversampling; Synthetic data; Data mining; Bandwidth (computing)","score_opus":0.07074704000648549,"score_gpt":0.2766460503492664,"score_spread":0.20589901034278094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309345632","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":[0.9152786,0.0010114363,0.018143661,0.00030215265,0.014546851,0.0007321936,0.015775286,0.0004762666,0.033733524],"genre_scores_gemma":[0.99465495,0.00009005053,0.00016944848,0.00013559678,0.000504756,0.000105307634,0.003385037,0.00004260902,0.00091223203],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981974,0.00008959376,0.00045867552,0.00038828427,0.00059474574,0.00027131205],"domain_scores_gemma":[0.9993097,0.000059442184,0.00017454484,0.00024237405,0.00010532719,0.00010860955],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026563072,0.0002474253,0.00022435814,0.00018517244,0.00028423642,0.00022703642,0.00034834218,0.00007966725,0.00049148395],"category_scores_gemma":[0.000015613163,0.0002791302,0.000046476627,0.00012542246,0.00005583421,0.00016784802,0.00009260025,0.00036310137,0.000023191484],"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":[0.00014490003,0.00020997952,0.002387924,0.00029401667,0.0005203267,0.00009737317,0.000631531,0.2721542,0.32293177,0.35711288,0.03902973,0.0044853725],"study_design_scores_gemma":[0.0004987098,0.00007171712,0.00037966322,0.000051886505,0.000026465295,0.00008128239,0.0006556045,0.9413856,0.0010196391,0.00023950575,0.05520105,0.00038883608],"about_ca_topic_score_codex":0.00013468476,"about_ca_topic_score_gemma":0.00003684067,"teacher_disagreement_score":0.6692314,"about_ca_system_score_codex":0.00025170518,"about_ca_system_score_gemma":0.00007080984,"threshold_uncertainty_score":0.9999661},"labels":[],"label_agreement":null},{"id":"W4309640158","doi":"10.1109/iemcon56893.2022.9946553","title":"Improving Power Load Forecasting using FIS","year":2022,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 British Columbia","funders":"","keywords":"Renewable energy; Pace; Computer science; Wind power; Electricity; Electric potential energy; Environmental economics; Electrical load; Automotive engineering; Energy (signal processing); Engineering; Electrical engineering; Economics; Statistics; Mathematics","score_opus":0.020577121755168782,"score_gpt":0.199689850205701,"score_spread":0.17911272845053222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309640158","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":[0.85382867,0.0005766253,0.01766967,0.0000134206375,0.0015310697,0.000085493,0.000011774031,0.00080640614,0.1254769],"genre_scores_gemma":[0.9931679,0.0000014744674,0.0060402513,0.00007623448,0.00009064884,0.0000106446405,0.000004135575,0.000051657567,0.0005570797],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990472,0.00001587971,0.0001957104,0.00015633483,0.00023152056,0.0003533376],"domain_scores_gemma":[0.99969256,0.00004298927,0.000029279738,0.00015282117,0.000020410116,0.00006191563],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00021003539,0.00013914892,0.00011932172,0.00007282321,0.00030195556,0.000037494065,0.0001440219,0.000028046796,0.0013779072],"category_scores_gemma":[0.000026203918,0.00015088715,0.00006458812,0.00024028742,0.000010368518,0.00013429421,0.00015326109,0.00023673548,0.0000069253697],"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":[0.000005970174,0.000014472414,0.0006778151,0.000039271235,0.000036188463,0.000050113966,0.0007380392,0.9357665,0.026576897,0.0004891276,0.0010532746,0.03455232],"study_design_scores_gemma":[0.00018234215,0.00003094759,0.00001654001,0.000010364466,0.000008825728,0.00010908913,0.0003305307,0.9777156,0.0029785926,0.000053723237,0.0182939,0.00026951678],"about_ca_topic_score_codex":0.00014021675,"about_ca_topic_score_gemma":0.000012973799,"teacher_disagreement_score":0.13933924,"about_ca_system_score_codex":0.00020930475,"about_ca_system_score_gemma":0.000031963526,"threshold_uncertainty_score":0.99953496},"labels":[],"label_agreement":null},{"id":"W4309684540","doi":"10.1109/iemcon56893.2022.9946597","title":"Harmonics Prediction and Mitigation using Adaptive Neuro Fuzzy Inference System Model based on Hybrid of Wind Solar Driven by DFIG","year":2022,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Harmonics; Adaptive neuro fuzzy inference system; Photovoltaic system; Control theory (sociology); Computer science; Wind power; Hybrid system; Renewable energy; Wind speed; Filter (signal processing); Artificial neural network; Fuzzy logic; Fuzzy control system; Engineering; Artificial intelligence; Machine learning; Meteorology; Electrical engineering","score_opus":0.01642893006928331,"score_gpt":0.1959192425507789,"score_spread":0.17949031248149558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309684540","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":[0.8422998,0.00003709017,0.15480846,0.000007521457,0.00015270378,0.000093334536,0.00016736114,0.00014651041,0.0022872048],"genre_scores_gemma":[0.9980389,0.0000034061075,0.0018348646,0.000024278665,0.000015256799,0.0000060568364,0.000041760308,0.000021042915,0.000014424594],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999371,0.000032271488,0.00017340484,0.00013513435,0.00017044072,0.000117767006],"domain_scores_gemma":[0.99973166,0.00006260298,0.000045483932,0.00009578342,0.000024202012,0.000040247898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008457745,0.00010170369,0.00011080968,0.00006913463,0.00010786583,0.000013593893,0.000053438092,0.00002343454,0.0000065647096],"category_scores_gemma":[0.000010239189,0.00011243027,0.000024603896,0.00008855097,0.00001335901,0.0001058841,0.000028233697,0.00014726118,2.5114724e-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":[0.0000099777235,0.000008894723,0.00032063745,0.000045553017,0.00000937825,0.0000014328982,0.00009003624,0.9859941,0.012726079,0.00025561627,0.00009886213,0.00043945826],"study_design_scores_gemma":[0.00020887531,0.00008842756,0.0000508814,0.00005747848,0.000016023625,0.0000043225814,0.00009316178,0.9827129,0.016626984,0.000025394005,0.000026744188,0.00008877904],"about_ca_topic_score_codex":0.000026245521,"about_ca_topic_score_gemma":0.0000012998677,"teacher_disagreement_score":0.1557391,"about_ca_system_score_codex":0.000089152905,"about_ca_system_score_gemma":0.00002320424,"threshold_uncertainty_score":0.45847735},"labels":[],"label_agreement":null},{"id":"W4310529625","doi":"10.1109/smartgridcomm52983.2022.9961008","title":"Vulnerability Assessment of Machine Learning Based Short-Term Residential Load Forecast against Cyber Attacks on Smart Meters","year":2022,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Carleton University","funders":"","keywords":"Computer science; Nonlinear autoregressive exogenous model; Support vector machine; Machine learning; Random forest; Artificial intelligence; Decision tree; Vulnerability (computing); Term (time); Artificial neural network; Smart grid; Vulnerability assessment; Long short term memory; Recurrent neural network; Deep learning; Data mining; Real-time computing; Computer security; Engineering","score_opus":0.020172225640781423,"score_gpt":0.26324126739547743,"score_spread":0.243069041754696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310529625","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":[0.9223519,0.000043331063,0.005772087,0.000046312383,0.0005845778,0.00015561974,0.00003132276,0.00029720992,0.070717625],"genre_scores_gemma":[0.99809957,0.0000047951435,0.0010740174,0.00008859527,0.000056057,0.0000467753,0.00012245125,0.000052983465,0.00045478408],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982175,0.00016484332,0.0004300357,0.00029564134,0.0005539389,0.0003380233],"domain_scores_gemma":[0.99930894,0.00018810661,0.000055063425,0.00031426625,0.00004117681,0.00009246943],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007258188,0.00023597745,0.00029471563,0.00013919656,0.00023406754,0.00003118742,0.00021166666,0.0000519574,0.0011644555],"category_scores_gemma":[0.00003708871,0.000235045,0.00017430408,0.00022725062,0.00003407227,0.00009064626,0.00011593299,0.0006255143,0.0000028352147],"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":[0.000030189054,0.000070785034,0.039006814,0.000061617895,0.00006843817,0.000016799466,0.00010491591,0.9482647,0.0061433995,0.00010079031,0.00043493375,0.0056966045],"study_design_scores_gemma":[0.0006622167,0.00029672298,0.011232309,0.000044051438,0.000034372075,0.0000037967832,0.00007025302,0.9669961,0.013088069,0.000011398728,0.007161684,0.0003990647],"about_ca_topic_score_codex":0.00015013464,"about_ca_topic_score_gemma":0.00023734158,"teacher_disagreement_score":0.07574763,"about_ca_system_score_codex":0.00033312538,"about_ca_system_score_gemma":0.000057112386,"threshold_uncertainty_score":0.9997486},"labels":[],"label_agreement":null},{"id":"W4310738127","doi":"10.18280/ijsdp.170707","title":"Investigations on Impact of Feature Normalization Techniques for Prediction of Hydro-Climatology Data Using Neural Network Backpropagation with Three Layer Hidden","year":2022,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":9,"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":"Normalization (sociology); Backpropagation; Artificial neural network; Artificial intelligence; Feature (linguistics); Computer science; Layer (electronics); Pattern recognition (psychology); Data mining; Machine learning","score_opus":0.032932967840929854,"score_gpt":0.268062605332319,"score_spread":0.23512963749138915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310738127","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":[0.97583985,0.00017317911,0.023498073,0.000028760953,0.00017398663,0.000107135274,0.000044001816,0.000014683824,0.00012033583],"genre_scores_gemma":[0.9809193,0.0000063699295,0.018626843,0.000009181558,0.00011622045,0.0000053185427,0.0002914301,0.000012495896,0.00001282837],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927217,0.000017649028,0.0003043385,0.000070793416,0.00021552325,0.00011955146],"domain_scores_gemma":[0.99927604,0.00006619767,0.0002826058,0.000053728858,0.00029493516,0.000026486261],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032739202,0.0000838901,0.00013486326,0.00024603365,0.00010667823,0.000019413037,0.00016728988,0.000032401724,0.0000058699693],"category_scores_gemma":[0.00003649261,0.00007303457,0.000022647506,0.00014863667,0.000017589482,0.00033305783,0.00006418707,0.00012921664,1.0071724e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002069712,0.000014706001,0.08425478,0.000066060966,0.00022061086,0.00003338993,0.0011422846,0.9110351,0.0005288916,0.000507368,0.0005140476,0.0014757687],"study_design_scores_gemma":[0.0013515885,0.0007179945,0.038023416,0.00072639144,0.00009680579,0.0005633788,0.0034095654,0.94353205,0.0073383576,0.001040415,0.0029159908,0.0002840458],"about_ca_topic_score_codex":0.0000100547395,"about_ca_topic_score_gemma":0.0000021164165,"teacher_disagreement_score":0.046231363,"about_ca_system_score_codex":0.00012792127,"about_ca_system_score_gemma":0.00012727892,"threshold_uncertainty_score":0.29782632},"labels":[],"label_agreement":null},{"id":"W4311681329","doi":"10.22215/etd/2022-15219","title":"Machine Learning and Optimization Model Development for Northern Community Energy Planning","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Energy Load and Power Forecasting","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":"Carleton University","funders":"","keywords":"Wind power; Mean absolute percentage error; Key (lock); Electricity; Energy planning; Energy (signal processing); Wind speed; Linear programming; Computer science; Engineering; Meteorology; Renewable energy; Artificial neural network; Artificial intelligence; Geography; Algorithm; Electrical engineering; Mathematics; Statistics","score_opus":0.016978327715427045,"score_gpt":0.2358754781864169,"score_spread":0.21889715047098987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311681329","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":[0.067995235,0.0034881698,0.86578935,0.0000031169157,0.00058896834,0.00018452728,0.00002793679,0.00083451695,0.061088156],"genre_scores_gemma":[0.7617673,0.00052517396,0.14404649,0.000055497396,0.00012274226,0.00050210557,0.04367265,0.0004768873,0.04883114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993697,0.000027091613,0.00021850024,0.000114977,0.000100720885,0.00016901908],"domain_scores_gemma":[0.9997212,0.00007734645,0.000056818517,0.00007100589,0.000032071166,0.00004154898],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013988646,0.00020876425,0.00018255081,0.00011318835,0.00053493615,0.000035528166,0.00009120618,0.00011155052,0.00005959838],"category_scores_gemma":[0.00001671448,0.0002298088,0.000032311087,0.000063445674,0.0000036734875,0.00005514376,0.00002727158,0.00040918562,1.6385728e-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":[0.00001453402,0.0000065906606,0.000099011326,0.00016201814,0.000047258323,4.046631e-7,0.0028465756,0.98382527,0.000038817692,0.00013864253,0.000017922357,0.012802954],"study_design_scores_gemma":[0.00015813959,0.000018413217,0.0000073611027,0.000060511433,0.000017159908,0.0000012559138,0.001084814,0.99060696,0.00023673444,0.000035279427,0.007510042,0.0002633106],"about_ca_topic_score_codex":0.0001501126,"about_ca_topic_score_gemma":0.0019418468,"teacher_disagreement_score":0.72174287,"about_ca_system_score_codex":0.000068542475,"about_ca_system_score_gemma":0.000033159522,"threshold_uncertainty_score":0.9371331},"labels":[],"label_agreement":null},{"id":"W4312178129","doi":"10.18280/ria.360505","title":"Diffusion Convolutional Recurrent Neural Network-Based Load Forecasting During COVID-19 Pandemic Situation","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"Mean absolute percentage error; Mean squared error; Term (time); Normalization (sociology); Computer science; Coronavirus disease 2019 (COVID-19); Recurrent neural network; Artificial neural network; Statistics; Artificial intelligence; Econometrics; Mathematics","score_opus":0.06182576053777536,"score_gpt":0.2605008735406535,"score_spread":0.19867511300287813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312178129","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":[0.9136329,0.0010185827,0.08174614,0.000122035795,0.0016570512,0.00023896099,0.00002532387,0.0005326629,0.0010262869],"genre_scores_gemma":[0.9985916,0.000038006645,0.00037258927,0.000106476,0.0003210615,0.000090729074,0.00009780299,0.000039669005,0.00034208418],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982071,0.00009111285,0.00049892004,0.00034247668,0.00034134492,0.00051905453],"domain_scores_gemma":[0.99913824,0.00026965345,0.000106909545,0.0002484661,0.000050957173,0.00018578241],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005572886,0.00021710547,0.00019410471,0.000105713465,0.0009284814,0.00004201394,0.00024734504,0.000059570553,0.0008246914],"category_scores_gemma":[0.00016185106,0.00025409157,0.00012222446,0.00051213027,0.0000483674,0.00010777015,0.00012654767,0.0004398044,0.000032368618],"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":[0.000027806627,0.00003359151,0.0066754892,0.000094088435,0.000009366643,0.000017658978,0.000661116,0.9799442,0.0038472165,0.0002510786,0.00023576731,0.008202614],"study_design_scores_gemma":[0.00011519139,0.000052774125,0.0001691451,0.000051047424,0.000011917243,0.000092552,0.0002544745,0.992325,0.0011310186,0.00032810486,0.005181919,0.0002868574],"about_ca_topic_score_codex":0.00003283647,"about_ca_topic_score_gemma":0.000041880452,"teacher_disagreement_score":0.08495863,"about_ca_system_score_codex":0.0007597292,"about_ca_system_score_gemma":0.00009533497,"threshold_uncertainty_score":0.9999911},"labels":[],"label_agreement":null},{"id":"W4312307060","doi":"10.1109/ithings-greencom-cpscom-smartdata-cybermatics55523.2022.00020","title":"On the Benefits of Transfer Learning and Reinforcement Learning for Electric Short-term Load Forecasting","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing &amp; Communications (GreenCom) and IEEE Cyber, Physical &amp; Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)","topic":"Energy Load and Power Forecasting","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":"McGill University","funders":"","keywords":"Computer science; Reinforcement learning; Leverage (statistics); Artificial intelligence; Machine learning; Probabilistic forecasting; Time series; Transfer of learning; Term (time); Transformer; Engineering; Probabilistic logic","score_opus":0.08114227040189179,"score_gpt":0.3029475724956981,"score_spread":0.2218053020938063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312307060","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":[0.9819482,0.00038831722,0.012468437,0.00042308183,0.0021446513,0.0007313391,0.00039095455,0.00016719184,0.0013378441],"genre_scores_gemma":[0.9953425,0.0006973249,0.0018140853,0.0003703696,0.00042735093,0.00003780385,0.00087583764,0.000100292506,0.00033445557],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9948571,0.0005921879,0.0017646544,0.00090541673,0.0011284001,0.0007522638],"domain_scores_gemma":[0.99294066,0.0044932165,0.00088247075,0.000980406,0.000442506,0.00026074966],"candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0021159835,0.0008855016,0.0012440542,0.00047176005,0.0021080868,0.00058085594,0.0020133578,0.00023266468,0.000019052082],"category_scores_gemma":[0.00031768373,0.00083544553,0.0002114385,0.00044151655,0.0006947,0.00066901284,0.0009416956,0.0022894663,0.0000031699024],"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":[0.002402029,0.003367398,0.018041814,0.007371609,0.010936174,0.000020786536,0.19017448,0.22390546,0.030389871,0.06480663,0.044196986,0.40438676],"study_design_scores_gemma":[0.0015364625,0.0005888992,0.0004392868,0.0017823885,0.00031395452,0.000060525042,0.00080905017,0.98141897,0.00062222814,0.000797707,0.010533649,0.0010968884],"about_ca_topic_score_codex":0.0006872846,"about_ca_topic_score_gemma":0.00042541232,"teacher_disagreement_score":0.7575135,"about_ca_system_score_codex":0.00013305458,"about_ca_system_score_gemma":0.00010075451,"threshold_uncertainty_score":0.9994096},"labels":[],"label_agreement":null},{"id":"W4312731389","doi":"10.1109/icses55317.2022.9914113","title":"Hybrid Solar Energy Forecasting with Supervised Deep Learning in IoT Environment","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES)","topic":"Energy Load and Power Forecasting","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":"Horizon College and Seminary","funders":"","keywords":"Renewable energy; Smart grid; Computer science; Wind power; Context (archaeology); Photovoltaic system; Reliability engineering; Wind power forecasting; Distributed generation; Demand forecasting; Grid; Intermittent energy source; Solar power; Electric power system; Engineering; Power (physics); Operations research; Electrical engineering","score_opus":0.029584624279833164,"score_gpt":0.22798796324813242,"score_spread":0.19840333896829926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312731389","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":[0.74225724,0.0021035078,0.22315116,0.0003523017,0.0007436505,0.0005743276,0.000029170566,0.00040927494,0.030379359],"genre_scores_gemma":[0.99835,0.00042368038,0.00038609706,0.000119379765,0.000053976866,0.00009886004,0.0002634026,0.00004044949,0.00026412107],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99774843,0.00037310054,0.00065743347,0.00035581965,0.00051629316,0.00034891794],"domain_scores_gemma":[0.99895513,0.000300637,0.00021350523,0.0002686273,0.0001879786,0.000074113224],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00062955555,0.0002826522,0.00030554144,0.00044410446,0.0004107368,0.00013607623,0.00057899166,0.000051411807,0.00026617144],"category_scores_gemma":[0.00006442175,0.00028784416,0.00004131003,0.00059031724,0.00006549626,0.000076231794,0.00034971998,0.0010321956,0.000005505475],"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":[0.00014927509,0.00027042118,0.031164156,0.00003158766,0.00029565193,0.000026879296,0.0014678395,0.6031815,0.0007976879,0.23810902,0.0002712375,0.12423479],"study_design_scores_gemma":[0.0003763747,0.0002791986,0.00060428114,0.00009867138,0.000005335241,0.00006695191,0.0005143027,0.98270667,0.00049742917,0.0002497177,0.014272502,0.0003285722],"about_ca_topic_score_codex":0.00023656213,"about_ca_topic_score_gemma":0.000026751628,"teacher_disagreement_score":0.3795252,"about_ca_system_score_codex":0.00054386095,"about_ca_system_score_gemma":0.000045452307,"threshold_uncertainty_score":0.9999574},"labels":[],"label_agreement":null},{"id":"W4313244205","doi":"10.3390/su15010231","title":"An Optimized Machine Learning Approach for Forecasting Thermal Energy Demand of Buildings","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Harmony search; Artificial neural network; Computer science; Benchmark (surveying); Artificial intelligence; Machine learning; Metaheuristic; Multilayer perceptron; Backtracking; Perceptron; Mathematical optimization; Data mining; Algorithm; Mathematics","score_opus":0.010108826486445286,"score_gpt":0.21714473347782887,"score_spread":0.20703590699138358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313244205","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":[0.7478548,0.00036665623,0.2496715,0.000014773569,0.00011209501,0.00025211647,0.000024840248,0.00027167983,0.0014315355],"genre_scores_gemma":[0.9827531,0.000002545812,0.016710514,0.000009659874,0.00006750321,0.00020223788,0.00010244582,0.000053041764,0.00009893796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986679,0.00013701087,0.00033424396,0.0002762332,0.00016962395,0.00041498168],"domain_scores_gemma":[0.9992803,0.00015561425,0.000083500374,0.00024089553,0.00016065718,0.0000790351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009768613,0.00018222173,0.00028800263,0.000100176054,0.00034848935,0.000020521571,0.00024496386,0.00005279201,0.000050894338],"category_scores_gemma":[0.00022158136,0.00019125073,0.0001304734,0.0002450624,0.000051966636,0.00014379744,0.00010785777,0.00024959678,1.8363307e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011426184,0.00007654167,0.0032261605,0.00029646367,0.000030022222,0.0000019088575,0.0011853194,0.980285,0.0006858885,0.0013918951,0.000009826409,0.012696745],"study_design_scores_gemma":[0.0006589535,0.00021431009,0.00008890853,0.000003523218,0.000020728558,0.0000075977814,0.0008327941,0.99226296,0.0021938647,0.00073086045,0.0027729615,0.0002125382],"about_ca_topic_score_codex":0.00016752948,"about_ca_topic_score_gemma":0.0000035916537,"teacher_disagreement_score":0.23489831,"about_ca_system_score_codex":0.00028938163,"about_ca_system_score_gemma":0.000063190746,"threshold_uncertainty_score":0.77989787},"labels":[],"label_agreement":null},{"id":"W4313361472","doi":"10.3390/buildings13010080","title":"An Effective Metaheuristic Approach for Building Energy Optimization Problems","year":2022,"lang":"en","type":"article","venue":"Buildings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Metaheuristic; Mathematical optimization; Computer science; Global optimization; Optimization problem; Test functions for optimization; Algorithm; Multi-swarm optimization; Mathematics","score_opus":0.007925408829379282,"score_gpt":0.20504578427064998,"score_spread":0.1971203754412707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313361472","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":[0.018148366,0.00040304835,0.97817224,0.0000058831847,0.00035368605,0.0002783899,0.000023943154,0.00050173356,0.002112688],"genre_scores_gemma":[0.8478726,0.000009418129,0.15073968,0.000040725263,0.00015935724,0.00091210526,0.00012130216,0.00008123414,0.00006356553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901414,0.000042841533,0.00019215628,0.00027893373,0.00015848778,0.0003134305],"domain_scores_gemma":[0.999592,0.00008421291,0.000049271774,0.00016644441,0.000032166616,0.0000758988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029788254,0.00017847454,0.00018722969,0.00014557221,0.0003190302,0.000056620444,0.00021834574,0.00004476173,0.000033089473],"category_scores_gemma":[0.000025247029,0.00019917815,0.000077172335,0.00029772302,0.000015038581,0.0001792236,0.000049900365,0.00013344863,1.9636875e-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":[0.000009803806,0.00003208217,0.000027425935,0.00006400287,0.000040885432,7.994058e-7,0.00020689693,0.9758947,0.007777283,0.00798328,0.00027533554,0.007687539],"study_design_scores_gemma":[0.0003194734,0.00013629235,0.000007187663,0.000009359148,0.00003587317,0.000016068698,0.00004556288,0.9755901,0.004312864,0.00037811403,0.018892482,0.0002566398],"about_ca_topic_score_codex":0.000033136315,"about_ca_topic_score_gemma":8.303066e-7,"teacher_disagreement_score":0.82972425,"about_ca_system_score_codex":0.00011657137,"about_ca_system_score_gemma":0.000007782386,"threshold_uncertainty_score":0.8122249},"labels":[],"label_agreement":null},{"id":"W4313391037","doi":"10.4038/slemaj.v25i1.34","title":"Analysis of Artificial Intelligence based Methods For Sensorless Disaggregation of the Residential Electric Power Signals","year":2022,"lang":"en","type":"article","venue":"SLEMA Journal","topic":"Energy Load and Power Forecasting","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 Waterloo","funders":"","keywords":"Sri lanka; Energy (signal processing); Power (physics); Electric power; Engineering; Political science; History; Statistics; Mathematics; South asia; Ancient history; Physics","score_opus":0.024751758200891837,"score_gpt":0.30416009195379745,"score_spread":0.27940833375290564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313391037","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":[0.38937756,0.0002213989,0.6096799,0.000038939386,0.00050099444,0.00006422844,0.000018284045,0.000009988475,0.000088749905],"genre_scores_gemma":[0.99459535,0.0000046126456,0.005301351,0.000010604112,0.000049406703,0.0000063941393,0.0000038126584,0.00001291219,0.000015567179],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885696,0.00021384729,0.00046840703,0.00007585209,0.00023153597,0.00015341723],"domain_scores_gemma":[0.99921006,0.0003117443,0.00023583634,0.00012401672,0.000087698594,0.000030620482],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010704645,0.0000779023,0.00020931345,0.0003384035,0.00016790983,0.000018771429,0.0001999564,0.000025196026,0.00036692427],"category_scores_gemma":[0.00012878387,0.00006358257,0.00031910205,0.0011704133,0.000018107656,0.000047178604,0.000024847306,0.0001933834,1.1251738e-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":[0.000027827122,0.000019880628,0.0003208194,0.0000136093095,0.00030183213,6.217018e-7,0.00027810675,0.88380206,0.09111743,0.0004371176,0.000029781162,0.023650952],"study_design_scores_gemma":[0.000053817166,0.00005457733,0.00052882935,0.000016407226,0.00035573213,0.0000054937827,0.0001594976,0.7150946,0.28236786,0.0011651615,0.00012385348,0.00007415814],"about_ca_topic_score_codex":0.0000062626573,"about_ca_topic_score_gemma":0.0000086847995,"teacher_disagreement_score":0.60521775,"about_ca_system_score_codex":0.0000507485,"about_ca_system_score_gemma":0.000039185463,"threshold_uncertainty_score":0.40175638},"labels":[],"label_agreement":null},{"id":"W4313523814","doi":"10.1142/9789811259142_0013","title":"A Neural Network Approach to Understanding Implied Volatility Movements","year":2023,"lang":"en","type":"book-chapter","venue":"World Scientific lecture notes in finance","topic":"Energy Load and Power Forecasting","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":"University of Toronto","funders":"","keywords":"Volatility (finance); Artificial neural network; Computer science; Artificial intelligence; Economics; Financial economics","score_opus":0.046156100983460756,"score_gpt":0.22541646488831857,"score_spread":0.1792603639048578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313523814","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0021930484,0.0021771549,0.13576418,0.00012700696,0.015972383,0.0011848833,0.0001658623,0.0011828125,0.84123266],"genre_scores_gemma":[0.8130569,0.000016804303,0.003211113,0.00014329844,0.0005691425,0.00004738643,0.0001884764,0.00022262348,0.18254426],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974183,0.000013799079,0.00051498594,0.0008468556,0.00041908756,0.00078698253],"domain_scores_gemma":[0.99895597,0.00017639434,0.00009654755,0.000649515,0.000026094567,0.000095466305],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00053467776,0.0005014823,0.00051651296,0.0005243537,0.00026904145,0.00021852803,0.000408849,0.00023016028,0.000040119194],"category_scores_gemma":[0.000040412404,0.00052378734,0.00014288214,0.0009309321,0.00011764701,0.000084072744,0.00016308099,0.0008101019,0.000050123577],"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":[0.000011257116,0.000007106571,0.00009562031,0.00011764279,0.00002442562,0.000017356899,0.00042330092,0.9581224,0.000044138134,0.03288495,0.0036747144,0.0045771394],"study_design_scores_gemma":[0.0005455355,0.000031857937,0.00030918446,0.0018222558,0.000037166847,0.000007703262,0.000005407566,0.5871826,0.00018200137,0.21602447,0.19185075,0.002001077],"about_ca_topic_score_codex":0.000007752713,"about_ca_topic_score_gemma":0.0017170513,"teacher_disagreement_score":0.81086385,"about_ca_system_score_codex":0.00050181564,"about_ca_system_score_gemma":0.000040763316,"threshold_uncertainty_score":0.99972135},"labels":[],"label_agreement":null},{"id":"W4313562562","doi":"10.1109/epec56903.2022.10000071","title":"Application of Reinforcement Learning to Wind Farm Active Power Control Design","year":2022,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Reinforcement learning; Wind power; Computer science; Grid; Control (management); Electric power system; Production (economics); Automatic frequency control; Fault (geology); Stability (learning theory); Power (physics); Control engineering; Simulation; Artificial intelligence; Engineering; Machine learning; Telecommunications; Electrical engineering","score_opus":0.008455280721606543,"score_gpt":0.20274334115702242,"score_spread":0.19428806043541588,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313562562","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":[0.02981128,0.000022190614,0.9318744,0.000022709484,0.000101198835,0.00022129233,0.000001908442,0.00013765144,0.037807357],"genre_scores_gemma":[0.9986059,7.035635e-7,0.0008194175,0.00006563717,0.0000142975405,0.000033924603,0.0000054518323,0.000014823881,0.00043981086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952924,0.000021021944,0.00013075436,0.000079643854,0.000121936006,0.00011741005],"domain_scores_gemma":[0.99978614,0.000051774234,0.000026247291,0.00008307133,0.000017360604,0.000035417288],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013235968,0.00006657645,0.000088059904,0.00005869491,0.000066634406,0.0000047518593,0.00007509196,0.000014629589,0.00024739318],"category_scores_gemma":[0.000011053183,0.00006986757,0.000024945197,0.00013262698,0.0000041496965,0.000027269783,0.00002793214,0.00010729289,0.0000087545095],"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":[0.000023598346,0.000004777349,0.000057314166,0.000003779738,0.00002333882,3.28978e-7,0.00072597765,0.97052366,0.019918535,0.0010415873,0.000063612206,0.007613471],"study_design_scores_gemma":[0.00059880776,0.00032406457,0.0002955695,0.000007111552,0.000013935481,0.00000304321,0.0012663049,0.9202011,0.044602107,0.000050414594,0.032418262,0.00021927386],"about_ca_topic_score_codex":0.000026774374,"about_ca_topic_score_gemma":0.0000015517431,"teacher_disagreement_score":0.96879464,"about_ca_system_score_codex":0.000061315935,"about_ca_system_score_gemma":0.0000070859523,"threshold_uncertainty_score":0.2849117},"labels":[],"label_agreement":null},{"id":"W4313562578","doi":"10.1109/epec56903.2022.10000164","title":"Optimized Hybrid Neural Network for Wind Speed Forecasting","year":2022,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Wind speed; Convolutional neural network; Mean squared error; Artificial intelligence; Wind power; Support vector machine; Artificial neural network; Bayesian optimization; Feature extraction; Feature (linguistics); Deep learning; Pattern recognition (psychology); Random forest; Fuzzy logic; Machine learning; Mathematics; Engineering; Statistics","score_opus":0.02462193454317747,"score_gpt":0.2106837153825046,"score_spread":0.18606178083932715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313562578","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":[0.8597027,0.00076012796,0.046581537,0.00016796173,0.0086549,0.0007875674,0.00009566789,0.0018906979,0.08135886],"genre_scores_gemma":[0.97335565,0.0000023994846,0.023801876,0.00017125817,0.0006792725,0.000019757688,0.00008447105,0.000072471834,0.0018128447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999055,0.000017873173,0.00021610218,0.00015490437,0.00011777767,0.00043833445],"domain_scores_gemma":[0.9996035,0.00015898087,0.000028491024,0.00012813315,0.000014856505,0.00006605287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021973677,0.000145482,0.00017313281,0.000042433978,0.0003030999,0.000034244742,0.00015301153,0.000017148554,0.0005015108],"category_scores_gemma":[0.000020118227,0.00015237607,0.00010715781,0.00014881697,0.000009595276,0.00007982287,0.000078657315,0.00016764444,0.0000026612527],"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":[0.000023579316,0.000004615459,0.00007262212,0.00001425288,0.000024832454,0.000008420911,0.00004632741,0.98015213,0.00010265392,0.00022690954,0.014938521,0.00438516],"study_design_scores_gemma":[0.00061231985,0.000051916097,0.000009172017,0.000005587636,0.000012351263,0.000053404758,0.000045292705,0.96531534,0.00034706618,0.00023485582,0.033116262,0.0001964544],"about_ca_topic_score_codex":0.000009657311,"about_ca_topic_score_gemma":0.0000020494408,"teacher_disagreement_score":0.11365297,"about_ca_system_score_codex":0.000044325534,"about_ca_system_score_gemma":0.000008223805,"threshold_uncertainty_score":0.62137157},"labels":[],"label_agreement":null},{"id":"W4313635619","doi":"10.37119/jpss2022.v20i1.511","title":"Forecasting of Immigrants in Canada using Forecasting models","year":2022,"lang":"en","type":"article","venue":"Journal of Probability and Statistical Science","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Regina","funders":"","keywords":"Autoregressive integrated moving average; Mean absolute percentage error; Akaike information criterion; Mean squared error; Statistics; Bayesian information criterion; Econometrics; Autoregressive model; Moving average; Mean absolute error; Bayesian probability; Mathematics; Autoregressive–moving-average model; Box–Jenkins; Time series","score_opus":0.05201014812807485,"score_gpt":0.23030207008379794,"score_spread":0.17829192195572308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313635619","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":[0.9718153,0.00012314697,0.027505113,0.000008551877,0.00020200506,0.000048101927,0.000031035375,0.0000026797688,0.00026410326],"genre_scores_gemma":[0.96621233,0.0000032588239,0.03375719,0.0000073392243,0.000013361558,9.4202704e-7,2.6852265e-7,0.0000047066205,6.2508764e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99855214,0.000043008207,0.00053976435,0.00011359386,0.0004780719,0.0002734206],"domain_scores_gemma":[0.9993208,0.0002945727,0.00012766555,0.000061564344,0.00008053301,0.00011488292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014869258,0.000078523575,0.00021848547,0.00009767698,0.0001528925,0.00001630059,0.00018391684,0.000011435186,0.000019247196],"category_scores_gemma":[0.0003702171,0.00007157165,0.000019371748,0.00044377297,0.00014974296,0.00025409405,0.00008719789,0.00026615965,7.262857e-9],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015748301,0.000011245626,0.0057099713,0.00007443861,0.0000034124964,0.000035712477,0.00038806692,0.97977936,0.0006191771,0.0024738014,0.0000039724855,0.010885063],"study_design_scores_gemma":[0.0001494899,0.00005913337,0.0012713668,0.000056123165,0.000005435071,0.00016703295,0.00032336442,0.9838885,0.00019141672,0.013805053,0.000008142298,0.0000749192],"about_ca_topic_score_codex":0.10373775,"about_ca_topic_score_gemma":0.11161022,"teacher_disagreement_score":0.011331252,"about_ca_system_score_codex":0.0006267974,"about_ca_system_score_gemma":0.00085657294,"threshold_uncertainty_score":0.90460056},"labels":[],"label_agreement":null},{"id":"W4313816313","doi":"10.3389/fenrg.2022.1073271","title":"Shaping energy cost management in process industries through clustering and soft sensors","year":2023,"lang":"en","type":"article","venue":"Frontiers in Energy Research","topic":"Energy Load and Power Forecasting","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":"Basic and Applied Basic Research Foundation of Guangdong Province; Fundamental Research Funds for the Central Universities; South China University of Technology; Horizon 2020 Framework Programme; National Natural Science Foundation of China","keywords":"Cluster analysis; Benchmark (surveying); Computer science; Autoregressive model; Ordinary least squares; Autoregressive–moving-average model; Artificial neural network; Nonlinear system; Process (computing); Support vector machine; Feature (linguistics); Mathematical optimization; Data mining; Artificial intelligence; Econometrics; Machine learning; Mathematics","score_opus":0.06884618615263419,"score_gpt":0.31575039165950075,"score_spread":0.24690420550686656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313816313","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":[0.7910892,0.009606861,0.06376079,0.0006089397,0.0036395302,0.0006422765,0.00002562944,0.0015537268,0.12907305],"genre_scores_gemma":[0.9928986,0.0039875186,0.001040526,0.000028769378,0.00012277624,0.00020366997,0.000028389108,0.00007586728,0.0016139045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979054,0.000098207536,0.00030349634,0.0003659353,0.00041128797,0.00091568375],"domain_scores_gemma":[0.99955815,0.00009618919,0.000017937227,0.0002013081,0.000037222693,0.00008919634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006145683,0.00019717493,0.00025390004,0.0009844907,0.00012682713,0.0000893269,0.00026134882,0.00017510467,0.000008527398],"category_scores_gemma":[0.00004990886,0.00021838682,0.000022044873,0.002273283,0.00012905785,0.00027108437,0.00022957656,0.00044759543,0.0000025524846],"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":[0.000035507393,0.000016871949,0.006795391,0.00023149393,0.00005762658,0.00032092413,0.0020299156,0.7896062,0.000054157103,0.0018974371,0.006614267,0.19234021],"study_design_scores_gemma":[0.0008889856,0.00003356232,0.0010633707,0.0005939072,0.0000045054903,0.0000099519975,0.007674241,0.89230245,0.0025668386,0.003816925,0.09056593,0.00047932853],"about_ca_topic_score_codex":0.00037638505,"about_ca_topic_score_gemma":0.0005359717,"teacher_disagreement_score":0.20180938,"about_ca_system_score_codex":0.00018248001,"about_ca_system_score_gemma":0.000025753896,"threshold_uncertainty_score":0.89055556},"labels":[],"label_agreement":null},{"id":"W4316804852","doi":"10.3390/math11030499","title":"A Novel and Robust Wind Speed Prediction Method Based on Spatial Features of Wind Farm Cluster","year":2023,"lang":"en","type":"article","venue":"Mathematics","topic":"Energy Load and Power Forecasting","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":"Simon Fraser University","funders":"National Natural Science Foundation of China","keywords":"Wind speed; Robustness (evolution); Computer science; Wind power; Spatial analysis; Data set; Set (abstract data type); Artificial neural network; Data mining; Artificial intelligence; Machine learning; Meteorology; Statistics; Engineering; Geography; Mathematics","score_opus":0.023460330634818526,"score_gpt":0.23828259468458335,"score_spread":0.21482226404976482,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316804852","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":[0.5172843,0.000042429867,0.45995685,0.00010268676,0.000995824,0.00035235536,0.00014893351,0.0006404824,0.020476168],"genre_scores_gemma":[0.89179754,0.00000783507,0.10714291,0.000046856272,0.00023350178,0.0000018595711,0.000043890395,0.000079138,0.0006464459],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994126,0.000009797327,0.00018834497,0.000096197444,0.00015426986,0.00013877558],"domain_scores_gemma":[0.99956554,0.00020072548,0.000039959166,0.00013941602,0.00001770423,0.00003663092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022441919,0.00011829686,0.00016644782,0.00011358018,0.000030572613,0.00001906516,0.00005479278,0.00008004182,0.000013131159],"category_scores_gemma":[0.000060416154,0.00010504861,0.000036551362,0.00015716189,0.000015624071,0.000025962512,0.000020632155,0.000107669606,0.0000041632734],"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":[0.000007691208,0.000033456083,0.00008143196,0.0004219782,0.000028933435,0.0000017566389,0.000948672,0.987081,0.008200478,0.00026415536,0.00036620066,0.0025642777],"study_design_scores_gemma":[0.0004316882,0.000044483088,0.0010922521,0.00019984998,0.000026683143,0.0000078538105,0.000077112825,0.99189276,0.005774716,0.0001586112,0.00019789225,0.00009611519],"about_ca_topic_score_codex":0.000009236973,"about_ca_topic_score_gemma":0.000013438378,"teacher_disagreement_score":0.3745133,"about_ca_system_score_codex":0.000013648039,"about_ca_system_score_gemma":0.0000058239207,"threshold_uncertainty_score":0.42837578},"labels":[],"label_agreement":null},{"id":"W4317373184","doi":"10.1016/j.renene.2023.01.068","title":"Nexus between renewable energy certificates and electricity prices in India: Evidence from wavelet coherence analysis","year":2023,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canada Excellence Research Chairs, Government of Canada","keywords":"Electricity market; Bivariate analysis; Economics; Electricity; Renewable energy; Nexus (standard); Scale (ratio); Econometrics; Monetary economics; Geography; Engineering","score_opus":0.023915787183922392,"score_gpt":0.2263638503169187,"score_spread":0.2024480631329963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317373184","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":[0.93014187,0.0121488925,0.044655066,0.00009990772,0.0004340225,0.0001013207,0.00009607363,0.0014702459,0.010852617],"genre_scores_gemma":[0.9949419,0.0026808092,0.0006006121,0.00003184023,0.00022234526,0.00005282247,0.00024753204,0.000056032033,0.0011661384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99767905,0.00007505563,0.00054258906,0.0006163301,0.00031819576,0.0007687608],"domain_scores_gemma":[0.99838954,0.0008086157,0.00011051471,0.0004416811,0.000043834676,0.00020578377],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003057566,0.0003636578,0.00063472934,0.0008445833,0.00013213817,0.00014056901,0.00038760857,0.00025616237,0.000066350636],"category_scores_gemma":[0.00011065031,0.00037331894,0.00010956027,0.0045754407,0.000051620253,0.0003832376,0.00012813478,0.00015843626,0.0000068807262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000009318467,0.00001164195,0.03509072,0.000028715835,0.00032866537,0.000036562746,0.00020768403,0.9466176,0.011827362,0.00004887019,0.0012242747,0.0045686075],"study_design_scores_gemma":[0.0004559267,0.0000870626,0.07605618,0.00034576395,0.00039630535,0.0000033666197,0.0001293703,0.6599187,0.24637766,0.0044010817,0.0106908465,0.0011377378],"about_ca_topic_score_codex":0.16379167,"about_ca_topic_score_gemma":0.02685272,"teacher_disagreement_score":0.28669888,"about_ca_system_score_codex":0.00011849743,"about_ca_system_score_gemma":0.000049922142,"threshold_uncertainty_score":0.99987185},"labels":[],"label_agreement":null},{"id":"W4318055228","doi":"10.3390/en16031295","title":"An Enhancement Method Based on Long Short-Term Memory Neural Network for Short-Term Natural Gas Consumption Forecasting","year":2023,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","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":"University of Regina","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; China Scholarship Council","keywords":"Artificial neural network; Computer science; Robustness (evolution); Convolutional neural network; Artificial intelligence; Term (time); Support vector machine; Deep learning; Key (lock); Machine learning; Data mining","score_opus":0.03940362906696582,"score_gpt":0.29223568158276736,"score_spread":0.25283205251580154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318055228","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":[0.97713196,0.00032085736,0.01801381,0.000021047985,0.002643984,0.00023454968,0.000016899645,0.0010297261,0.00058717956],"genre_scores_gemma":[0.9874863,0.00004264541,0.010427697,0.00006314288,0.0010441229,0.0001752751,0.00048635068,0.00010277084,0.0001717085],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981704,0.000068912035,0.0003801569,0.00039323376,0.00025353677,0.0007337792],"domain_scores_gemma":[0.9990271,0.00044032288,0.00004112854,0.000332427,0.00004364088,0.000115430186],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00046202444,0.00035732196,0.00031279365,0.00017736026,0.0002634978,0.000117054224,0.00022911483,0.00011797072,0.000038034428],"category_scores_gemma":[0.0000313733,0.00035148073,0.00015525223,0.00025211988,0.000036175785,0.0002536699,0.00004061131,0.00023608896,0.0000073168817],"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":[0.0000445014,0.000012897498,0.003537161,0.00013250354,0.000040044844,0.000026446914,0.00018097565,0.910132,0.00971201,0.0000943032,0.0004883324,0.07559884],"study_design_scores_gemma":[0.00025654383,0.00012817967,0.0040799077,0.00020563063,0.000038640064,0.000008115029,0.00004004187,0.95906514,0.035550177,0.000035109697,0.00018528618,0.0004072104],"about_ca_topic_score_codex":0.0000062029108,"about_ca_topic_score_gemma":0.00009814585,"teacher_disagreement_score":0.07519163,"about_ca_system_score_codex":0.00008162061,"about_ca_system_score_gemma":0.000014666529,"threshold_uncertainty_score":0.9998937},"labels":[],"label_agreement":null},{"id":"W4318147647","doi":"10.1109/bigdata55660.2022.10020940","title":"Using CNN-LSTM Model for Weather Forecasting","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Big Data (Big Data)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Deep learning; Generalization; Artificial neural network; Weather forecasting; Artificial intelligence; Feature (linguistics); Data modeling; Big data; Machine learning; Numerical weather prediction; Data mining; Meteorology; Database; Geography","score_opus":0.5383621885771043,"score_gpt":0.3526381344140766,"score_spread":0.18572405416302767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318147647","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":[0.04003178,0.000208154,0.82017624,0.00043189208,0.021773564,0.0006026444,0.08723974,0.0005130515,0.029022906],"genre_scores_gemma":[0.9674579,0.00006227559,0.0067838742,0.00024574212,0.0016165188,0.00007740056,0.02251542,0.000097602926,0.0011432706],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978414,0.000036805242,0.00040832802,0.0007032328,0.0006116279,0.0003985861],"domain_scores_gemma":[0.9981785,0.00012505143,0.00012368978,0.0013948287,0.00008475692,0.00009318603],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000578081,0.00027557695,0.00022720023,0.00021397113,0.0003169326,0.00016815698,0.0030113093,0.0000605683,0.0003552554],"category_scores_gemma":[0.00012529887,0.0003104261,0.000050934417,0.00017365115,0.000036440633,0.0005457267,0.0013804537,0.00042200563,0.000014181713],"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":[0.00013909584,0.00012561328,0.000121080455,0.000069227244,0.0003243291,0.00003612194,0.0003305797,0.75963384,0.0099571,0.008180215,0.04182647,0.17925633],"study_design_scores_gemma":[0.00043347327,0.000031443302,0.0000030269568,0.00005995285,0.000027579008,0.000028623013,0.00011272511,0.94431514,0.00025048852,0.0007650549,0.053644486,0.00032800107],"about_ca_topic_score_codex":0.00007464205,"about_ca_topic_score_gemma":0.00017383033,"teacher_disagreement_score":0.9274261,"about_ca_system_score_codex":0.00016968949,"about_ca_system_score_gemma":0.00013828042,"threshold_uncertainty_score":0.9999348},"labels":[],"label_agreement":null},{"id":"W4318464159","doi":"10.3390/en16031352","title":"A Deep Learning Approach for Exploring the Design Space for the Decarbonization of the Canadian Electricity System","year":2023,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of British Columbia; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Electricity; Computer science; Energy system; Carbon tax; Space (punctuation); Key (lock); Artificial intelligence; Wind power; Climate change; Energy (signal processing); Environmental economics; Machine learning; Economics; Engineering; Mathematics","score_opus":0.03644981966901884,"score_gpt":0.19745091306876406,"score_spread":0.16100109339974522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318464159","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":[0.15815468,0.001486284,0.8344562,0.00021792745,0.0012951095,0.0010144408,0.000006819058,0.0006274135,0.0027411128],"genre_scores_gemma":[0.9976576,0.00003909001,0.0014837915,0.000006104567,0.00015039509,0.00043929613,0.0000062311965,0.0000355208,0.00018197708],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993862,0.000043190514,0.00012012344,0.00008795943,0.000096219504,0.00026629303],"domain_scores_gemma":[0.9991597,0.00058160623,0.000036005407,0.0001548333,0.000043815988,0.000024061299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004483187,0.00009510502,0.00009303747,0.00006068679,0.000553947,0.000046910875,0.00022670114,0.000037051355,3.1789446e-7],"category_scores_gemma":[0.00016251871,0.000051984443,0.00006843121,0.00048341282,0.000022423677,0.000051964602,0.000018155934,0.00009504572,3.2710042e-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":[0.000002858882,6.1451783e-7,0.000101794205,0.00006610439,0.000031889038,7.7143575e-8,0.0011159419,0.98760766,0.0003453792,0.008481044,0.00013114473,0.0021154801],"study_design_scores_gemma":[0.000076250784,0.000010912205,0.00026659135,0.000027196773,0.000025478701,0.0000012869606,0.0010864825,0.98330575,0.012207704,0.000039280843,0.002882423,0.00007065186],"about_ca_topic_score_codex":0.003042569,"about_ca_topic_score_gemma":0.0073096557,"teacher_disagreement_score":0.83950293,"about_ca_system_score_codex":0.00008515328,"about_ca_system_score_gemma":0.00003959886,"threshold_uncertainty_score":0.45994744},"labels":[],"label_agreement":null},{"id":"W4318766781","doi":"10.1016/j.renene.2023.01.108","title":"Ensemble robust local mean decomposition integrated with random forest for short-term significant wave height forecasting","year":2023,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Random forest; Radio frequency; Autocorrelation; Extreme learning machine; Term (time); Statistics; Mathematics; Linear regression; Computer science; Artificial intelligence; Artificial neural network; Physics","score_opus":0.026881355896111413,"score_gpt":0.21343348370222529,"score_spread":0.18655212780611388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318766781","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":[0.12048406,0.00014109703,0.87094873,0.000012621412,0.00059796585,0.00019462462,0.000030815445,0.000979359,0.0066107446],"genre_scores_gemma":[0.99244577,0.00007622207,0.0048077735,0.000021327252,0.00041695498,0.00021143098,0.0008895695,0.00015358791,0.0009773496],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982016,0.000035087418,0.00040401274,0.00038759105,0.0002213922,0.0007503094],"domain_scores_gemma":[0.9990554,0.00028402975,0.000054082368,0.0002639409,0.00015703512,0.00018553554],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023368424,0.00037531412,0.000409582,0.0002456057,0.00023204202,0.00009521043,0.00015857763,0.00016403646,0.000023259548],"category_scores_gemma":[0.00002861514,0.000317587,0.00013361461,0.0006356087,0.000053547898,0.00019696655,0.000036350277,0.00012462774,0.0000038270455],"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":[0.00017193731,0.000017542745,0.00013480977,0.00008641937,0.00011212854,0.000060246053,0.00010186833,0.97714204,0.0089139,0.00030092444,0.0019242817,0.011033885],"study_design_scores_gemma":[0.0012193203,0.00016459989,0.000024149194,0.00028382885,0.00005161741,0.000035200395,0.0001507308,0.8961833,0.09543871,0.00030227326,0.005714993,0.00043126554],"about_ca_topic_score_codex":0.00088781957,"about_ca_topic_score_gemma":0.0069411937,"teacher_disagreement_score":0.8719617,"about_ca_system_score_codex":0.00013789549,"about_ca_system_score_gemma":0.000051062678,"threshold_uncertainty_score":0.99992764},"labels":[],"label_agreement":null},{"id":"W4319264889","doi":"10.1049/gtd2.12763","title":"A deep LSTM‐CNN based on self‐attention mechanism with input data reduction for short‐term load forecasting","year":2023,"lang":"en","type":"article","venue":"IET Generation Transmission & Distribution","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":55,"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":"National Natural Science Foundation of China","keywords":"Computer science; Term (time); Convolution (computer science); Reduction (mathematics); Artificial intelligence; Data mining; Process (computing); Feature (linguistics); Pattern recognition (psychology); Machine learning; Artificial neural network; Mathematics","score_opus":0.04298138297657694,"score_gpt":0.24672175712396616,"score_spread":0.2037403741473892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319264889","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":[0.09772465,0.000044330467,0.8994342,0.00017778791,0.000673454,0.000495088,0.0002886157,0.0010106853,0.00015118072],"genre_scores_gemma":[0.96570385,0.00005589448,0.006503624,0.000020567708,0.00054333295,0.00015237615,0.026897004,0.000065771266,0.000057564575],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99815285,0.000052381423,0.00041417536,0.0005275002,0.0004456937,0.0004074015],"domain_scores_gemma":[0.9991531,0.000048856527,0.00006618395,0.0004277068,0.00016273318,0.00014142031],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005409744,0.0002988089,0.0002022152,0.00012565014,0.00042150097,0.00011674777,0.00019009602,0.00019386187,0.000023927221],"category_scores_gemma":[0.00003136837,0.00027753843,0.00008489145,0.00053343823,0.000017150911,0.0004293318,0.000015003593,0.0001762389,0.000010124261],"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":[0.00017330813,0.000112962116,0.00004932205,0.00028473945,0.00007222976,0.000008590927,0.00024760136,0.71341085,0.17659508,0.00072866963,0.004878829,0.103437826],"study_design_scores_gemma":[0.0007747022,0.00019513335,0.00011973394,0.00017723923,0.00008064425,0.000012309999,0.000030998544,0.9452293,0.048773456,0.000091047885,0.00419239,0.0003230718],"about_ca_topic_score_codex":0.00000610017,"about_ca_topic_score_gemma":0.000017758646,"teacher_disagreement_score":0.89293057,"about_ca_system_score_codex":0.00023262159,"about_ca_system_score_gemma":0.000075197,"threshold_uncertainty_score":0.9999677},"labels":[],"label_agreement":null},{"id":"W4319917416","doi":"10.2139/ssrn.4353949","title":"A Proposed Hybrid Model of Ann and Anfis for Harmonics Forecasting of Wind and Solar Hybrid Model","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Adaptive neuro fuzzy inference system; Harmonics; Artificial neural network; Meteorology; Econometrics; Computer science; Environmental science; Economics; Engineering; Geography; Artificial intelligence; Fuzzy logic; Electrical engineering","score_opus":0.021840224245511426,"score_gpt":0.21596985931901977,"score_spread":0.19412963507350833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319917416","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":[0.8883815,0.0018183833,0.10934827,0.000036314952,0.00005493055,0.000131768,0.0000462458,0.00005429322,0.00012834105],"genre_scores_gemma":[0.99490255,0.0023439617,0.0024870285,0.000006266103,0.000060372415,0.0000031627155,0.000009416787,0.000057217432,0.00012999371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981191,0.000013040133,0.00039040824,0.00015963361,0.00015920743,0.0011586023],"domain_scores_gemma":[0.9995341,0.00006555834,0.00013064196,0.000102488906,0.000085198655,0.000082033665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089394604,0.0001820414,0.000298712,0.00017631454,0.00012084447,0.000024456072,0.00012070926,0.000047800077,6.260844e-7],"category_scores_gemma":[0.000060651495,0.0001763087,0.00008582252,0.00012498553,0.00004900108,0.00019342953,0.000041666786,0.00056935643,1.7452184e-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":[0.000057732686,0.000015600994,0.00016940678,0.00024051203,0.00018441373,0.0000027988792,0.0004252312,0.9575279,0.0144236665,0.0034561548,0.00006605761,0.023430549],"study_design_scores_gemma":[0.0006884709,0.0001701571,0.000005548819,0.0000884925,0.000049897437,0.00020332141,0.00015449808,0.92936736,0.012559918,0.05653305,0.000020079571,0.00015920351],"about_ca_topic_score_codex":0.0000057395137,"about_ca_topic_score_gemma":0.000021729727,"teacher_disagreement_score":0.10686124,"about_ca_system_score_codex":0.00009163457,"about_ca_system_score_gemma":0.00035238994,"threshold_uncertainty_score":0.71896607},"labels":[],"label_agreement":null},{"id":"W4320525629","doi":"10.1016/j.segan.2023.101006","title":"Efficient deep generative model for short-term household load forecasting using non-intrusive load monitoring","year":2023,"lang":"en","type":"article","venue":"Sustainable Energy Grids and Networks","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Leverage (statistics); Key (lock); Demand response; Term (time); Generative model; Load profile; Artificial intelligence; Machine learning; Reliability engineering; Data mining; Generative grammar; Engineering; Electricity; Computer security","score_opus":0.02532885864955739,"score_gpt":0.23150631704181465,"score_spread":0.20617745839225726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320525629","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":[0.49450177,0.0018417418,0.50171447,0.0000058326323,0.00075796887,0.00018380453,0.000008241277,0.00033412836,0.0006520175],"genre_scores_gemma":[0.9944283,0.00038337737,0.0023458898,0.00002006667,0.0016053172,0.00014267977,0.000027431808,0.00015864252,0.00088833447],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974237,0.00001843994,0.00041181894,0.0004884178,0.0002801983,0.0013774411],"domain_scores_gemma":[0.99904436,0.00015484252,0.00006358053,0.00024981768,0.00026052378,0.00022690598],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00041996266,0.0004529872,0.00041169074,0.0001787584,0.0006766689,0.0001782092,0.00017760463,0.00025382824,0.0000019233603],"category_scores_gemma":[0.00003899676,0.00046168148,0.00013861424,0.00061053043,0.00006275433,0.00015816883,0.00019191923,0.00026014604,2.7860182e-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":[0.000030701824,0.000010945562,0.0002942641,0.0001401722,0.00007985432,0.00008323843,0.0008424958,0.9893545,0.0006158592,0.0009506807,0.00019334425,0.007403957],"study_design_scores_gemma":[0.00043641712,0.000049104045,0.000034674296,0.00015546299,0.000054478303,0.000015593063,0.000872003,0.9954895,0.0017074143,0.00030348214,0.00034506657,0.00053680566],"about_ca_topic_score_codex":0.00010351803,"about_ca_topic_score_gemma":0.000039105114,"teacher_disagreement_score":0.49992648,"about_ca_system_score_codex":0.0005395965,"about_ca_system_score_gemma":0.0001244856,"threshold_uncertainty_score":0.9997835},"labels":[],"label_agreement":null},{"id":"W4321502838","doi":"10.1016/j.heliyon.2023.e13903","title":"A proposed novel adaptive DC technique for non-stationary data removal","year":2023,"lang":"en","type":"article","venue":"Heliyon","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Series (stratigraphy); Computer science; Stationary process; Time series; Transformation (genetics); Logarithm; Algorithm; Mathematics; Mathematical optimization; Applied mathematics; Machine learning","score_opus":0.05041494983080546,"score_gpt":0.26865510872654697,"score_spread":0.2182401588957415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321502838","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":[0.020578973,0.0005184698,0.9546882,0.00020697637,0.0012103465,0.0013690402,0.0011146764,0.0021087534,0.018204587],"genre_scores_gemma":[0.5391617,0.00078785757,0.4478331,0.0002036553,0.0018399595,0.0006066156,0.0043695997,0.00036245276,0.0048350957],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936223,0.000004459744,0.000138794,0.00018388753,0.00009645733,0.00021416172],"domain_scores_gemma":[0.9995459,0.00008057335,0.000019129653,0.0002841703,0.00003213988,0.000038122813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017435972,0.000104350554,0.000097975164,0.00009123386,0.00005971928,0.000014292262,0.00019606811,0.00006800179,0.000008872142],"category_scores_gemma":[0.00004097426,0.00010503662,0.00002668257,0.0002506484,0.000012763415,0.00014418615,0.00007101506,0.000089948655,0.000038615555],"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":[0.00010273667,0.000061675935,0.00006088186,0.0016291243,0.00012884711,0.000084862855,0.0008844707,0.042236574,0.91084385,0.0036386375,0.01761753,0.022710819],"study_design_scores_gemma":[0.0009659474,0.00016205941,0.00047427032,0.0009810763,0.000030293952,0.000073936564,0.00021267771,0.68706596,0.1466076,0.000429474,0.16239873,0.0005979784],"about_ca_topic_score_codex":0.0000038108044,"about_ca_topic_score_gemma":0.000012185795,"teacher_disagreement_score":0.7642363,"about_ca_system_score_codex":0.00002513448,"about_ca_system_score_gemma":0.000025697229,"threshold_uncertainty_score":0.4283269},"labels":[],"label_agreement":null},{"id":"W4322632036","doi":"10.1016/j.apenergy.2023.120888","title":"Development of an integrated BLSVM-MFA method for analyzing renewable power-generation potential under climate change: A case study of Xiamen","year":2023,"lang":"en","type":"article","venue":"Applied Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Renewable energy; Climate change; Electricity generation; Environmental science; Photovoltaic system; Wind power; Fossil fuel; Environmental economics; Environmental engineering; Engineering; Power (physics); Waste management; Economics; Electrical engineering; Ecology","score_opus":0.040246880724230216,"score_gpt":0.27996772291528377,"score_spread":0.23972084219105355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322632036","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":[0.8404643,0.000027540735,0.15858282,0.000002434135,0.00020843137,0.00017168841,0.0000135692535,0.00019686688,0.00033233213],"genre_scores_gemma":[0.95104617,0.000012980111,0.048349787,0.000009694059,0.00009284904,0.00022530636,0.0001834487,0.00005759356,0.00002218832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987765,0.000030752166,0.00048109877,0.000246758,0.00014759862,0.00031729852],"domain_scores_gemma":[0.9995133,0.000039490445,0.00010470302,0.00021301856,0.000062239196,0.000067222594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040441292,0.00019471746,0.00030696095,0.0002695119,0.00011528907,0.000019660387,0.00010944634,0.00009345342,0.000011023464],"category_scores_gemma":[0.0000043492896,0.00019460525,0.000044923578,0.00056575163,0.0000102436225,0.00008538877,0.000050601462,0.000054961656,7.219284e-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":[0.000029346964,0.00011192343,0.000007739602,0.00004798418,0.00017034961,0.000026494428,0.0038283048,0.78687865,0.16340946,0.0015748825,0.00003437288,0.04388049],"study_design_scores_gemma":[0.0017298779,0.00027680898,0.000041152514,0.000049339873,0.00012221548,0.00004091485,0.013091511,0.6514381,0.33085045,0.00015087416,0.0016815357,0.000527217],"about_ca_topic_score_codex":0.0008757017,"about_ca_topic_score_gemma":0.0035551514,"teacher_disagreement_score":0.167441,"about_ca_system_score_codex":0.00004490056,"about_ca_system_score_gemma":0.00002524648,"threshold_uncertainty_score":0.7935772},"labels":[],"label_agreement":null},{"id":"W4322832358","doi":"10.5281/zenodo.7694291","title":"Risk-adjustable stochastic schedule based on Sobol augmented Latin hypercube sampling considering correlation of wind power uncertainties","year":2021,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Latin hypercube sampling; Sobol sequence; Schedule; Computer science; Correlation; Sampling (signal processing); Power (physics); Mathematical optimization; Monte Carlo method; Mathematics; Statistics; Physics","score_opus":0.030400712708185854,"score_gpt":0.22145453568535436,"score_spread":0.1910538229771685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322832358","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":[0.5883061,0.00032716626,0.31331688,0.00011658742,0.0005767716,0.000394588,0.00033000615,0.0016628936,0.09496902],"genre_scores_gemma":[0.99710834,0.000013785047,0.0015283745,0.000026531123,0.00004469078,3.0346243e-8,0.0005075408,0.0006127338,0.00015797073],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987903,0.00013028139,0.00027641474,0.00024117088,0.00027333526,0.0002884883],"domain_scores_gemma":[0.99910593,0.000101250465,0.00008283742,0.00028649805,0.00033054972,0.0000929527],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00031787038,0.0001478148,0.00016766254,0.00018089706,0.00069764117,0.00019344712,0.00020341064,0.000067834255,0.004156899],"category_scores_gemma":[0.0008294727,0.00017013511,0.00005051795,0.0004384982,0.00006443123,0.00014538388,0.00016444235,0.00029398123,0.0003292188],"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":[0.00002922378,0.0000404322,0.000029126879,0.000076622055,0.000044436118,0.0000060605043,0.0006266869,0.976922,0.013233892,0.0009953005,0.001490516,0.0065057315],"study_design_scores_gemma":[0.0013721815,0.00024882975,0.0009277109,0.00047492643,0.000057322934,0.000041506457,0.0015038177,0.8869538,0.014543977,0.00016943956,0.093221314,0.00048521932],"about_ca_topic_score_codex":0.000009742791,"about_ca_topic_score_gemma":6.61229e-7,"teacher_disagreement_score":0.40880224,"about_ca_system_score_codex":0.00011118975,"about_ca_system_score_gemma":0.000007554651,"threshold_uncertainty_score":0.99675345},"labels":[],"label_agreement":null},{"id":"W4323338358","doi":"10.1109/access.2023.3253047","title":"Harmonics Forecasting of Wind and Solar Hybrid Model Driven by DFIG and PMSG Using ANN and ANFIS","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Harmonics; Wind power; Total harmonic distortion; Adaptive neuro fuzzy inference system; Computer science; Waveform; Electronic engineering; Power electronics; Control theory (sociology); Electrical engineering; Voltage; Engineering; Fuzzy logic; Artificial intelligence; Fuzzy control system","score_opus":0.054061301614264604,"score_gpt":0.26173043892642084,"score_spread":0.20766913731215625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323338358","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":[0.99414057,0.000821405,0.0043728175,0.000012683648,0.00014104652,0.00006717519,0.00006391005,0.00010633068,0.0002740419],"genre_scores_gemma":[0.9986345,0.0003809994,0.0008349393,0.000017377375,0.000050695828,0.0000019407535,0.000010612076,0.000041754407,0.000027157013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99922305,0.000010199132,0.00020298343,0.00019822893,0.0001020721,0.00026345058],"domain_scores_gemma":[0.99965876,0.00007099479,0.000049925107,0.00010589236,0.000026043901,0.00008837892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012592277,0.00015704439,0.0002090383,0.00010271983,0.000104867315,0.00010208759,0.00010488634,0.000059183454,0.0000018476914],"category_scores_gemma":[0.000020074169,0.00016578146,0.000020066254,0.00017088022,0.000054416258,0.0004295775,0.0001001913,0.00012506735,3.0026152e-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":[0.000011209346,0.000009974728,0.017136188,0.00052101334,0.00008715429,0.000029781737,0.0010341397,0.9151603,0.04671333,0.000019129637,0.0011900458,0.018087717],"study_design_scores_gemma":[0.00024335917,0.000013374435,0.0003538324,0.000116252166,0.000024516672,0.00002637994,0.000029658253,0.97638464,0.022311952,0.0002093174,0.000110367844,0.00017632643],"about_ca_topic_score_codex":0.00004508297,"about_ca_topic_score_gemma":0.000008884148,"teacher_disagreement_score":0.06122435,"about_ca_system_score_codex":0.000012128314,"about_ca_system_score_gemma":0.000010079704,"threshold_uncertainty_score":0.6760372},"labels":[],"label_agreement":null},{"id":"W4323343629","doi":"10.2139/ssrn.4076339","title":"Seasonal Adjustment of Daily Data With CAMPLET","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Center for Interuniversity Research and Analysis on Organizations","funders":"","keywords":"Geography; Climatology; Environmental science; Geology","score_opus":0.015038933035841369,"score_gpt":0.2251690122208468,"score_spread":0.21013007918500543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323343629","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":[0.9670778,0.007899206,0.017488671,0.00020923994,0.0006946829,0.00010595288,0.00008621912,0.00038788674,0.0060503576],"genre_scores_gemma":[0.9962657,0.002553802,0.00023371982,0.00001084079,0.00029105006,0.000001773571,0.00007836817,0.00003511337,0.0005296432],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828315,0.000015621847,0.00016653503,0.00011206993,0.00022412016,0.0011984903],"domain_scores_gemma":[0.999598,0.0000332869,0.000043856693,0.0002392628,0.000026302045,0.000059292564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005835723,0.0001107859,0.000131348,0.000082853636,0.000054497938,0.000014072414,0.00034161913,0.00003304789,0.000026860656],"category_scores_gemma":[0.000013283605,0.00008862915,0.000029032375,0.00027423436,0.000019670057,0.00015091187,0.00005206914,0.00066771527,0.000017934175],"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":[0.0002399912,0.00013258903,0.011884297,0.00025620882,0.0031939864,0.00010879787,0.0015104972,0.31155357,0.007325612,0.1522884,0.019260682,0.49224535],"study_design_scores_gemma":[0.011827829,0.0038400344,0.023959387,0.0013974691,0.0008878385,0.008180445,0.013976256,0.6578633,0.0047520795,0.08845658,0.18112032,0.0037384431],"about_ca_topic_score_codex":0.000025244817,"about_ca_topic_score_gemma":0.0004752176,"teacher_disagreement_score":0.4885069,"about_ca_system_score_codex":0.00015632524,"about_ca_system_score_gemma":0.0004236453,"threshold_uncertainty_score":0.3614192},"labels":[],"label_agreement":null},{"id":"W4323895136","doi":"10.1109/spec55080.2022.10058414","title":"Development of Short-Term Wind Power Forecasting Methods","year":2022,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Wind power; Wind speed; Wind power forecasting; Meteorology; Term (time); Computer science; Electric power system; Power (physics); Environmental science; Econometrics; Engineering; Mathematics; Geography","score_opus":0.04251540522029358,"score_gpt":0.2773074084547209,"score_spread":0.23479200323442734,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323895136","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":[0.8423242,0.0001865386,0.04071308,0.0000036471583,0.0006143025,0.000059941918,0.0000031482116,0.00020377156,0.11589138],"genre_scores_gemma":[0.766604,8.329679e-7,0.23309486,0.000010543301,0.000016737435,0.000008706724,0.0000066529233,0.000023634793,0.00023403845],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99921113,0.000024964584,0.00029351545,0.00011070167,0.0001475654,0.00021210077],"domain_scores_gemma":[0.99974316,0.000059558348,0.00002116823,0.00011717508,0.000013699287,0.00004525213],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00040230827,0.000109097586,0.00014355675,0.00008452383,0.0001243739,0.000008078976,0.00013719751,0.00002308412,0.0012137577],"category_scores_gemma":[0.00001072044,0.000110295194,0.000045491775,0.00017700696,0.000009657978,0.000051068106,0.00012820291,0.000138142,0.0000017737267],"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":[0.000014778315,0.00006964899,0.003983294,0.00014409855,0.00024682374,0.000025233829,0.0119068455,0.294539,0.15417832,0.0020970893,0.0007702243,0.5320246],"study_design_scores_gemma":[0.00067641493,0.00014997969,0.0041160914,0.00010704606,0.000044477758,0.00016154033,0.0028925831,0.2515955,0.43190822,0.00023469274,0.30668274,0.0014307167],"about_ca_topic_score_codex":0.0000015434234,"about_ca_topic_score_gemma":0.000003872183,"teacher_disagreement_score":0.53059393,"about_ca_system_score_codex":0.000055128894,"about_ca_system_score_gemma":0.000022213933,"threshold_uncertainty_score":0.9996993},"labels":[],"label_agreement":null},{"id":"W4323923663","doi":"10.46660/ijeeg.v12i4.78","title":"Using XGBoost Model with Feature Selection Techniques for Wind Speed Forecasting","year":2023,"lang":"en","type":"article","venue":"International Journal of Economic and Environmental Geology","topic":"Energy Load and Power Forecasting","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":"Simon Fraser University","funders":"","keywords":"Renewable energy; Wind power; Fossil fuel; Global warming; Government (linguistics); Environmental economics; Natural resource economics; Environmental science; Climate change; Business; Computer science; Engineering; Economics; Ecology; Waste management","score_opus":0.02208260773160919,"score_gpt":0.22787072774915534,"score_spread":0.20578812001754615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323923663","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":[0.9895492,0.000048497426,0.009465497,0.00007529948,0.00034640782,0.000041447376,0.000015234801,0.000022453329,0.0004359262],"genre_scores_gemma":[0.9924031,0.00008108137,0.0069959704,0.000027512238,0.00028308062,5.667962e-7,0.00001604201,0.000016305457,0.00017634715],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996297,0.000003826213,0.00015217296,0.000067811125,0.000038672802,0.00010785297],"domain_scores_gemma":[0.999831,0.000031166404,0.00008071489,0.000020324216,0.0000061968894,0.000030594565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000082988314,0.00007772104,0.00010507175,0.00011549588,0.0000328144,0.000014025814,0.000067627756,0.00005366411,0.000014441594],"category_scores_gemma":[0.0000028071854,0.00007181262,0.00003389235,0.00001242352,0.00003535798,0.0001424281,0.000020617688,0.00008655125,0.0000011547602],"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":[0.000056441277,0.000004337973,0.008024254,0.0000045754286,0.00010799772,0.0000079236115,0.000085135674,0.9802889,0.007046295,0.00010233495,0.00023135338,0.0040404014],"study_design_scores_gemma":[0.0003396812,0.00007954672,0.00039631623,0.000022664108,0.000013817755,0.0007143548,0.00007471483,0.9936177,0.002798341,0.0005475711,0.0013100666,0.00008523871],"about_ca_topic_score_codex":0.000003445922,"about_ca_topic_score_gemma":0.0000064086935,"teacher_disagreement_score":0.013328737,"about_ca_system_score_codex":0.00010808148,"about_ca_system_score_gemma":0.000008817968,"threshold_uncertainty_score":0.29284337},"labels":[],"label_agreement":null},{"id":"W4324122550","doi":"10.23977/acss.2023.070112","title":"A Time Series Data Prediction Model Based on Adaptive Weighted LSTM","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":3,"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":"Computer science; Principal component analysis; Robustness (evolution); Time series; Residual; Artificial intelligence; Data mining; Model selection; Series (stratigraphy); Nonlinear system; Machine learning; Pattern recognition (psychology); Algorithm","score_opus":0.023911376383093052,"score_gpt":0.23198745703139778,"score_spread":0.20807608064830474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4324122550","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":[0.02828289,0.0038584555,0.96151805,0.000030040017,0.0017669383,0.00029110594,0.00033427434,0.0009052821,0.0030129573],"genre_scores_gemma":[0.99640894,0.000373179,0.0025974067,0.000023946915,0.00029724848,0.000025964608,0.00015964062,0.000027847016,0.000085842854],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999198,0.000034124827,0.00021824382,0.0002360674,0.00012987769,0.00018364831],"domain_scores_gemma":[0.99956363,0.00011470931,0.000028436814,0.00023623694,0.00001581368,0.00004119731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020667813,0.00013291257,0.00018602349,0.00011703481,0.000046582114,0.00004015566,0.00014632614,0.00004979132,0.0000022604434],"category_scores_gemma":[0.0000028233699,0.000119358585,0.0000145235845,0.00021526481,0.000020822472,0.0004498898,0.000051219107,0.00008690086,0.0000119343695],"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":[0.000011293959,0.0000054824723,0.00009293306,0.00007092524,0.000008961561,0.0000090877165,0.00010743369,0.9901339,0.00007218732,0.0001878784,0.0006060861,0.008693858],"study_design_scores_gemma":[0.00019885071,0.000077341596,0.000031129795,0.0003960207,0.000003171012,0.000003502138,0.000013476382,0.99627304,0.000038851216,0.00016451644,0.0026772756,0.00012283861],"about_ca_topic_score_codex":0.000004147564,"about_ca_topic_score_gemma":0.000005132085,"teacher_disagreement_score":0.96812606,"about_ca_system_score_codex":0.000015871718,"about_ca_system_score_gemma":0.00000781157,"threshold_uncertainty_score":0.4867302},"labels":[],"label_agreement":null},{"id":"W4362563105","doi":"10.2139/ssrn.4409757","title":"Electrical Demand Dynamics in Institutional Building Clusters: A Cloud-Oriented Data-Mining Framework","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Energy demand; Dashboard; Computer science; Supply and demand; Electric potential energy; Cloud computing; Efficient energy use; Cluster (spacecraft); Demand response; On demand; Energy (signal processing); Environmental economics; Data science; Engineering; Electrical engineering","score_opus":0.020077302673624915,"score_gpt":0.2647450001543839,"score_spread":0.24466769748075898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362563105","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":[0.25827315,0.0064915237,0.72867405,0.00021811709,0.004986933,0.00019736012,0.00006696142,0.00049407966,0.00059782853],"genre_scores_gemma":[0.9801227,0.007783419,0.008885763,0.00004740249,0.002347883,0.000021367854,0.00048106257,0.0001778225,0.00013258556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9946691,0.00011132084,0.00087312434,0.0006175118,0.0005218136,0.0032071401],"domain_scores_gemma":[0.998671,0.00027888073,0.00020661182,0.00060009136,0.000079893216,0.00016355047],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0025195198,0.0005189316,0.00057381013,0.0006747008,0.00024512885,0.00018334408,0.0011738751,0.00070279994,0.00000801183],"category_scores_gemma":[0.0005686081,0.000560717,0.00016354714,0.0007292844,0.000058946895,0.00027694748,0.0007995374,0.011261889,0.000012751563],"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":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006272052,0.000053895546,0.00395861,0.0001331771,0.0006768709,0.00014955271,0.00034873464,0.79876536,0.000017532479,0.17564447,0.00024974658,0.019939318],"study_design_scores_gemma":[0.00049037044,0.00006248736,0.00012493962,0.0011839095,0.00007757017,0.0005391854,0.0004168039,0.8958112,0.000008337341,0.09989992,0.0007516716,0.0006335956],"about_ca_topic_score_codex":0.00009535893,"about_ca_topic_score_gemma":0.003200241,"teacher_disagreement_score":0.72184956,"about_ca_system_score_codex":0.0057198317,"about_ca_system_score_gemma":0.0021569496,"threshold_uncertainty_score":0.99968445},"labels":[],"label_agreement":null},{"id":"W4362576618","doi":"10.22215/etd/2022-15450","title":"Analysis of Open-Source Data in Wind Power Prediction Modeling","year":2022,"lang":"en","type":"dissertation","venue":"","topic":"Energy Load and Power Forecasting","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":"Wind power; Weather Research and Forecasting Model; Terrain; Meteorology; Software deployment; Wind power forecasting; Numerical weather prediction; Wind speed; Environmental science; Power (physics); Computer science; Electric power system; Engineering; Geography; Cartography","score_opus":0.025916644319442666,"score_gpt":0.27273156076876776,"score_spread":0.2468149164493251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362576618","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":[0.84402305,0.0007341339,0.003641415,0.0000023090158,0.0008476412,0.00016220732,0.0003960266,0.0001568332,0.15003641],"genre_scores_gemma":[0.9753117,0.00008044813,0.00024022526,0.000004819502,0.000019912128,0.000007436937,0.02133314,0.0000488929,0.0029534202],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891394,0.000019655903,0.00043265283,0.00028317078,0.00020890778,0.00014168168],"domain_scores_gemma":[0.99928707,0.000029728197,0.0000625083,0.0005707011,0.000022258073,0.000027720576],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027903845,0.000157146,0.00037115125,0.0006535062,0.000033459186,0.00004097291,0.0007510261,0.000120554025,0.0013853072],"category_scores_gemma":[0.000022182432,0.00017389299,0.00006232597,0.0010276508,0.000002670561,0.00026417003,0.00016394709,0.0002779098,9.741449e-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":[0.000011796208,0.000014965153,0.0012837076,0.000045398458,0.0004882798,0.0000012507021,0.0008215305,0.9957741,0.000100349265,0.000049466773,0.00010494675,0.0013042194],"study_design_scores_gemma":[0.000106235384,0.0000088474,0.00080739247,0.00005512365,0.00034927257,2.3680971e-7,0.001221237,0.99547124,0.00003863566,0.000006896122,0.0017851888,0.0001496996],"about_ca_topic_score_codex":0.0012977186,"about_ca_topic_score_gemma":0.0039720973,"teacher_disagreement_score":0.14708298,"about_ca_system_score_codex":0.000050730567,"about_ca_system_score_gemma":0.000026436583,"threshold_uncertainty_score":0.9995276},"labels":[],"label_agreement":null},{"id":"W4362653742","doi":"10.1109/mce.2023.3264884","title":"AI-Based Electricity Grid Management for Sustainability, Reliability, and Security","year":2023,"lang":"en","type":"article","venue":"IEEE Consumer Electronics Magazine","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Electricity; Computer science; Environmental economics; Sustainability; Smart grid; Electricity market; Grid; Anomaly detection; Reliability (semiconductor); Greenhouse gas; Supply and demand; Economics; Engineering; Microeconomics; Artificial intelligence","score_opus":0.007461354058742113,"score_gpt":0.2386713398907587,"score_spread":0.23120998583201657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4362653742","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":[0.9633409,0.0043870737,0.018453242,0.0021560055,0.0015560754,0.0021938635,0.00012916124,0.003181857,0.0046018274],"genre_scores_gemma":[0.99798274,0.00062137,0.0003349894,0.00017676114,0.00010900754,0.00022013012,0.00008629935,0.00007585388,0.00039287008],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806553,0.0000396745,0.00033210323,0.00043148035,0.00019180238,0.00093941466],"domain_scores_gemma":[0.9990326,0.0002549852,0.000037243633,0.0003856226,0.00016167045,0.0001278531],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061084057,0.0002933464,0.00029419505,0.00023528167,0.00014427549,0.00006389788,0.00019114319,0.00012159748,0.0000119961305],"category_scores_gemma":[0.00007710727,0.0003157691,0.00010371307,0.0007252259,0.00007488769,0.000104796985,0.00004732718,0.00033249508,0.00002514675],"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":[0.00089440844,0.0007556668,0.023107795,0.016596688,0.0015085876,0.00021789502,0.0007701742,0.38144183,0.0116168,0.05554411,0.33871052,0.16883552],"study_design_scores_gemma":[0.002082434,0.00028725434,0.00082596426,0.00005404281,0.00016222744,0.0000117883865,0.000015456453,0.58503234,0.012744572,0.051359914,0.34656766,0.0008563599],"about_ca_topic_score_codex":0.000007579302,"about_ca_topic_score_gemma":0.00009294794,"teacher_disagreement_score":0.2035905,"about_ca_system_score_codex":0.00029918837,"about_ca_system_score_gemma":0.000091567046,"threshold_uncertainty_score":0.9999294},"labels":[],"label_agreement":null},{"id":"W4366397785","doi":"10.2139/ssrn.4411230","title":"Minimum Variance Hedge Ratios using Short-Lived Arbitrage Model and Artificial Neural Network","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Arbitrage; Variance (accounting); Econometrics; Hedge; Economics; Artificial intelligence; Mathematics; Financial economics; Statistics; Computer science; Ecology","score_opus":0.027119589820383518,"score_gpt":0.24120446304936616,"score_spread":0.21408487322898265,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366397785","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":[0.94188666,0.0014822024,0.05521409,0.00005968548,0.0005693397,0.00006308872,0.0000020436264,0.00020915212,0.0005137091],"genre_scores_gemma":[0.9974838,0.0008058518,0.00043204453,0.00003155956,0.0010246128,0.0000028469403,0.000005691728,0.00005392106,0.00015968493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974392,0.00003376461,0.00029715803,0.00016373707,0.00014916765,0.001916944],"domain_scores_gemma":[0.9996919,0.000042126732,0.000038453647,0.00010436993,0.00002458337,0.000098564175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065446354,0.00019008416,0.00018993899,0.00008893168,0.00028088092,0.000094608105,0.00012739873,0.000087903514,0.0000050530325],"category_scores_gemma":[0.00001625218,0.00019387653,0.00006997102,0.000304393,0.000027062853,0.00021070351,0.000029227007,0.0013287937,0.00000536713],"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":[0.00001376597,0.00000443376,0.00016443913,0.000008224265,0.00006333724,0.000013548483,0.00016076895,0.9774443,0.008017398,0.009106906,0.00007023513,0.004932674],"study_design_scores_gemma":[0.00013553735,0.00003752644,0.00006790375,0.000029069133,0.000027048556,0.00023385666,0.00012919508,0.96115065,0.0001554876,0.03777021,0.00005855842,0.00020495272],"about_ca_topic_score_codex":0.000006128222,"about_ca_topic_score_gemma":0.00020412079,"teacher_disagreement_score":0.055597097,"about_ca_system_score_codex":0.00018838237,"about_ca_system_score_gemma":0.00026975942,"threshold_uncertainty_score":0.79060555},"labels":[],"label_agreement":null},{"id":"W4366418160","doi":"10.3390/jrfm16040246","title":"Coupling the Empirical Wavelet and the Neural Network Methods in Order to Forecast Electricity Price","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"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":"Autoregressive conditional heteroskedasticity; Econometrics; Autoregressive model; Volatility (finance); Electricity; Computer science; Electricity price forecasting; Robustness (evolution); Wavelet; Artificial neural network; Wavelet transform; Economics; Electricity price; Artificial intelligence; Engineering","score_opus":0.025041072257891223,"score_gpt":0.27868362203104347,"score_spread":0.25364254977315226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366418160","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":[0.79699737,0.001806705,0.19910881,0.00055024005,0.0006682591,0.00019869149,0.0000011845311,0.00002865852,0.0006401059],"genre_scores_gemma":[0.98564523,0.0037835087,0.009781162,0.0002367598,0.00048740572,0.00000909831,4.1141453e-7,0.000016402342,0.00003998875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923456,0.000056309404,0.00027859284,0.00007625096,0.00011462686,0.00023963337],"domain_scores_gemma":[0.9994633,0.0003237359,0.000065071115,0.00006947485,0.000034946024,0.00004344991],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017546971,0.00009649542,0.00018627895,0.000103474405,0.00014012797,0.000048110345,0.00011334508,0.000032159678,0.0000014290067],"category_scores_gemma":[0.00018340284,0.00005435858,0.00003363636,0.0011205558,0.000027164966,0.000049351955,0.00008466947,0.00030769463,6.634841e-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":[0.00010743132,0.000006277404,0.0041384697,0.000025310374,0.000024080418,0.000034758952,0.0014598393,0.5980621,0.000003564635,0.0015135156,0.002585932,0.3920387],"study_design_scores_gemma":[0.0012060049,0.00008766831,0.1391158,0.00008519673,0.000070440765,0.000030944004,0.00022839154,0.7755581,0.000011983784,0.00263169,0.08080484,0.0001689345],"about_ca_topic_score_codex":0.000012732316,"about_ca_topic_score_gemma":0.00003725569,"teacher_disagreement_score":0.39186975,"about_ca_system_score_codex":0.000018266437,"about_ca_system_score_gemma":0.0000067673686,"threshold_uncertainty_score":0.22166786},"labels":[],"label_agreement":null},{"id":"W4366764945","doi":"10.1016/j.enconman.2023.117063","title":"AI-coherent data-driven forecasting model for a combined cycle power plant","year":2023,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Context (archaeology); Broyden–Fletcher–Goldfarb–Shanno algorithm; Electricity generation; Industrial engineering; Machine learning; Data mining; Artificial intelligence; Power (physics); Engineering","score_opus":0.03314082118030349,"score_gpt":0.2246267724534142,"score_spread":0.19148595127311072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366764945","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":[0.18926781,0.000778779,0.7054106,0.0022621434,0.0059527247,0.0017780418,0.0008951118,0.005874307,0.0877805],"genre_scores_gemma":[0.9936369,0.0004825819,0.0018463071,0.00042731056,0.000047178797,0.00005890607,0.0007344575,0.00004718686,0.0027191422],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991049,0.000008040841,0.00017404203,0.0002738113,0.00012913073,0.00031004963],"domain_scores_gemma":[0.9995587,0.000038820206,0.000026731943,0.00027065192,0.000013746589,0.00009135822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012260988,0.00016226157,0.00014918398,0.00014203951,0.00012773472,0.000043122527,0.00020648511,0.000048126443,0.000023499055],"category_scores_gemma":[0.0000033764802,0.00015935846,0.00003901156,0.00013811856,0.000016689437,0.00014068538,0.00031797105,0.000052721694,0.000009175891],"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":[0.00006328653,0.000030928037,0.000039881226,0.00037310328,0.00024627132,0.00004901003,0.00038758098,0.77192646,0.00014874262,0.042132117,0.16275442,0.021848219],"study_design_scores_gemma":[0.000736646,0.000028007184,0.000016035077,0.00005878546,0.000026424676,0.0000014222808,0.000098587436,0.86120945,0.00009075136,0.0003080733,0.13725449,0.00017130317],"about_ca_topic_score_codex":0.000013179965,"about_ca_topic_score_gemma":0.000025728346,"teacher_disagreement_score":0.8043691,"about_ca_system_score_codex":0.00002541501,"about_ca_system_score_gemma":0.0000046867385,"threshold_uncertainty_score":0.64984494},"labels":[],"label_agreement":null},{"id":"W4366977989","doi":"10.1117/12.2657701","title":"Efficient community electricity load forecasting with transformer and federated learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 British Columbia","funders":"","keywords":"Computer science; Electricity; Transformer; Deep learning; Data modeling; Artificial intelligence; Computation; Process (computing); Machine learning; Big data; Federated learning; Data mining; Database; Engineering","score_opus":0.019131292374382564,"score_gpt":0.20344253589141292,"score_spread":0.18431124351703035,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366977989","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":[0.9477196,0.000055099983,0.007655419,0.000013288001,0.000042427884,0.000062563355,8.905632e-7,0.001060735,0.043389976],"genre_scores_gemma":[0.9993012,0.000017010518,0.0002475894,0.000014270697,0.000019739684,0.0000068662735,0.000010055758,0.000032234442,0.00035103937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992645,0.000041011885,0.00013021482,0.00009316597,0.00013309196,0.0003380565],"domain_scores_gemma":[0.99965686,0.00016132544,0.000013335541,0.00006293719,0.000036139536,0.000069397574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033418983,0.00014181745,0.0001332944,0.00007862632,0.0004529422,0.00005993922,0.000050922088,0.000050929277,0.000019366926],"category_scores_gemma":[0.000033183103,0.00011495319,0.00002004118,0.0005571939,0.000024950426,0.000040764222,0.0000116050705,0.00046703828,0.000011300508],"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":[0.000020559073,0.000014530664,0.0024812815,0.00010693409,0.00005255526,0.000015802343,0.0021879023,0.95610267,0.0056314156,0.0001280906,0.00010569652,0.033152547],"study_design_scores_gemma":[0.0003431526,0.00009561031,0.0009874452,0.00005721004,0.00000964251,0.000034147768,0.0004835142,0.9904078,0.0067166844,0.000014131709,0.0006523957,0.00019823553],"about_ca_topic_score_codex":0.00016630186,"about_ca_topic_score_gemma":0.00034470053,"teacher_disagreement_score":0.051581595,"about_ca_system_score_codex":0.000039208568,"about_ca_system_score_gemma":0.000015245555,"threshold_uncertainty_score":0.4687655},"labels":[],"label_agreement":null},{"id":"W4367596703","doi":"10.58445/rars.187","title":"Predicting the Number of Sunspots Per Month and Per Quarter Using ARIMA Models","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Quarter (Canadian coin); Autoregressive integrated moving average; Sunspot; Sunspot number; Econometrics; Statistics; Meteorology; Mathematics; Geography; Time series; Physics; Solar cycle","score_opus":0.03604178903496466,"score_gpt":0.24689435038406296,"score_spread":0.2108525613490983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367596703","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":[0.9592139,0.00026687677,0.015806636,0.000039504477,0.0008088238,0.00013058985,0.000029633868,0.00034539122,0.02335865],"genre_scores_gemma":[0.99390715,0.00006750945,0.0051598987,0.00001571188,0.00023779062,0.000013125298,0.000015484658,0.0000869674,0.00049636915],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99887735,0.000026381025,0.00035243534,0.0002681455,0.00019369923,0.00028197464],"domain_scores_gemma":[0.99936795,0.00011332638,0.00006704685,0.00034978363,0.000047137084,0.000054742188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022480807,0.00027332798,0.0003008994,0.000059877573,0.000075558455,0.00006793968,0.00018082962,0.00023231503,0.000092577095],"category_scores_gemma":[0.000009688197,0.00020197219,0.000107650274,0.000058343463,0.00004692319,0.00010950808,0.00027214357,0.000501176,0.000008118272],"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":[0.0000036301562,0.0000060649,0.0130091645,0.00033730804,0.000112147245,0.000002719851,0.0032959837,0.98166215,0.00024141853,0.0007660102,0.0001797425,0.0003836413],"study_design_scores_gemma":[0.00009819793,0.0000037459322,0.0011710579,0.00023960204,0.000057547186,0.000013543559,0.00045507733,0.9953456,0.00019613463,0.002106484,0.000079284495,0.00023375964],"about_ca_topic_score_codex":0.0010573468,"about_ca_topic_score_gemma":0.00008075546,"teacher_disagreement_score":0.03469325,"about_ca_system_score_codex":0.00003183341,"about_ca_system_score_gemma":0.000023633289,"threshold_uncertainty_score":0.8236187},"labels":[],"label_agreement":null},{"id":"W4367678847","doi":"10.1016/j.scs.2023.104623","title":"Energy demand forecasting in seven sectors by an optimization model based on machine learning algorithms","year":2023,"lang":"en","type":"article","venue":"Sustainable Cities and Society","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Autoregressive integrated moving average; Particle swarm optimization; Computer science; Algorithm; Demand forecasting; Electricity; Artificial intelligence; Machine learning; Mathematical optimization; Operations research; Time series; Engineering; Mathematics","score_opus":0.00881264846630763,"score_gpt":0.19496843597151978,"score_spread":0.18615578750521214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367678847","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":[0.4987997,0.0006306259,0.49305484,0.000064906984,0.0001406514,0.00015561758,0.00003601269,0.0009663977,0.006151229],"genre_scores_gemma":[0.9936498,0.00030918946,0.002212132,0.00008199084,0.000055482134,0.000024804602,0.00024792677,0.0000572151,0.0033614754],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905795,0.00002918014,0.00015607724,0.00018549153,0.00011882156,0.00045245126],"domain_scores_gemma":[0.9997224,0.000072494884,0.0000250113,0.00007607377,0.000033349457,0.000070642156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028603117,0.00016543722,0.00015368874,0.000079592035,0.00026183948,0.00007684182,0.000058704743,0.00011064303,0.000010334409],"category_scores_gemma":[0.00001779686,0.00017361478,0.000051107,0.00033536748,0.000028102357,0.00020868797,0.000031317984,0.0001860632,1.3127794e-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":[0.0000036777922,0.000006822058,0.0004085696,0.00012920529,0.000009140763,0.0000073225856,0.0026170674,0.9944466,0.000010606799,0.00026634915,0.0004270998,0.0016675908],"study_design_scores_gemma":[0.0003358851,0.000048777823,0.000013361663,0.00003596534,0.000004627991,8.933185e-7,0.007501512,0.9907697,0.00008384597,0.00022820584,0.00077720114,0.00020005967],"about_ca_topic_score_codex":0.00028632095,"about_ca_topic_score_gemma":0.00001680912,"teacher_disagreement_score":0.49485007,"about_ca_system_score_codex":0.0001041228,"about_ca_system_score_gemma":0.000024389234,"threshold_uncertainty_score":0.7079805},"labels":[],"label_agreement":null},{"id":"W4376880129","doi":"10.1016/j.energy.2023.127852","title":"Spatiotemporal analysis of bidimensional wind speed forecasting: Development and thorough assessment of LSTM and ensemble graph neural networks on the Dutch database","year":2023,"lang":"en","type":"article","venue":"Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakes Environmental (Canada); University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Computer science; Benchmarking; Wind power; Wind speed; Intermittency; Benchmark (surveying); Time horizon; Meteorology; Data mining; Engineering; Mathematics; Mathematical optimization; Geography","score_opus":0.03646505924199806,"score_gpt":0.24664055983455474,"score_spread":0.21017550059255669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376880129","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":[0.99704826,0.0001659109,0.0016807499,0.00003418179,0.000184608,0.00003350328,0.000012825571,0.000043261596,0.0007967038],"genre_scores_gemma":[0.9985567,0.000060639704,0.0010829373,0.000026822237,0.00003607442,0.0000019449876,0.00014998825,0.000017859007,0.00006708704],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991145,0.000038115104,0.0002882563,0.00016862502,0.00020008833,0.00019038704],"domain_scores_gemma":[0.9994236,0.00025350502,0.00007765606,0.00016321955,0.000028928967,0.000053038686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000284071,0.00014401312,0.00024539614,0.0002401809,0.00007550693,0.000013397089,0.00007095028,0.000047994814,0.000016631677],"category_scores_gemma":[0.000016666498,0.00010927702,0.000050969018,0.00078584364,0.000044433767,0.0000543759,0.00007705014,0.00009047207,1.0946255e-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":[0.000010465303,0.000014838416,0.0073893326,0.000021777067,0.00042637344,0.000010125394,0.00022697983,0.97846067,0.0015902288,0.0040920503,0.00026157696,0.0074955667],"study_design_scores_gemma":[0.0001420239,0.00003172409,0.018464345,0.000048562644,0.00008790028,0.000002155715,0.000062465595,0.97517264,0.005347927,0.00003886305,0.0004778039,0.00012360341],"about_ca_topic_score_codex":0.00011007718,"about_ca_topic_score_gemma":0.00015850176,"teacher_disagreement_score":0.011075013,"about_ca_system_score_codex":0.000011384399,"about_ca_system_score_gemma":0.000015137962,"threshold_uncertainty_score":0.44561875},"labels":[],"label_agreement":null},{"id":"W4378227107","doi":"10.2139/ssrn.4454118","title":"A Multi-Task Deep Learning Model for Inflation Forecasting: Dynamic Phillips Curve Neural Network","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; University of Guelph","funders":"","keywords":"Artificial neural network; Phillips curve; Inflation (cosmology); Artificial intelligence; Deep learning; Task (project management); Computer science; Econometrics; Machine learning; Economics; Keynesian economics; Monetary policy","score_opus":0.019144542460952454,"score_gpt":0.23486932787441214,"score_spread":0.21572478541345969,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378227107","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":[0.35714605,0.0018467966,0.6395794,0.000054823573,0.0005422585,0.00015276692,0.0000028643296,0.00053517823,0.00013987166],"genre_scores_gemma":[0.9954264,0.0010706892,0.002049494,0.000013777355,0.0005130284,0.000024967245,0.000061367165,0.000107087195,0.0007331491],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99672985,0.000038010177,0.00036414922,0.00018598443,0.0001655569,0.0025164702],"domain_scores_gemma":[0.9995095,0.00012351072,0.000112289614,0.00009773166,0.00006397092,0.000092979164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011208272,0.00023308747,0.00020779655,0.0001613836,0.00042460219,0.00007470548,0.00018260076,0.00011721548,0.0000027768297],"category_scores_gemma":[0.00010645376,0.00023724954,0.00016474044,0.000396157,0.000016355822,0.00026323693,0.000030126821,0.0016277217,0.000010790006],"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":[0.000016925713,0.0000044236926,0.0005687045,0.000016946615,0.00008068738,0.0000020555158,0.00032320197,0.9539087,0.00035844723,0.0009380169,0.000024963985,0.043756936],"study_design_scores_gemma":[0.00063584215,0.000091979324,0.00012757018,0.000040427643,0.000029958039,0.00012179306,0.00016519931,0.9825896,0.0000073347105,0.015589913,0.00034606853,0.0002543261],"about_ca_topic_score_codex":0.0000041730136,"about_ca_topic_score_gemma":0.0007459072,"teacher_disagreement_score":0.6382804,"about_ca_system_score_codex":0.00044692008,"about_ca_system_score_gemma":0.0001505924,"threshold_uncertainty_score":0.96747553},"labels":[],"label_agreement":null},{"id":"W4379207950","doi":"10.1016/j.energy.2023.128022","title":"A clustering-based feature enhancement method for short-term natural gas consumption forecasting","year":2023,"lang":"en","type":"article","venue":"Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"China Scholarship Council","keywords":"Cluster analysis; Pattern recognition (psychology); Entropy (arrow of time); Feature (linguistics); Artificial intelligence; Computer science; Gaussian; Correlation; Mixture model; Data mining; Statistics; Mathematics","score_opus":0.031236908395051496,"score_gpt":0.27461141295106506,"score_spread":0.24337450455601356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379207950","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":[0.07137115,0.0006256161,0.92241305,0.00011013722,0.0025775407,0.00015533024,0.00002511341,0.0011541947,0.0015678897],"genre_scores_gemma":[0.96220726,0.00004651146,0.03526199,0.00010946597,0.00046621042,0.0001925615,0.00036608707,0.000084388215,0.0012655036],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998932,0.000023397419,0.00020247434,0.00024174126,0.0001433031,0.0004571049],"domain_scores_gemma":[0.99948555,0.00020442177,0.000030065234,0.00017121813,0.000033817792,0.000074902964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020045822,0.00021929166,0.00019747621,0.00015144418,0.00012382578,0.000047947513,0.00012622795,0.00010860071,0.000029005221],"category_scores_gemma":[0.000029678838,0.00021868982,0.0001175053,0.00021611656,0.000013953537,0.00008208366,0.000036504814,0.00013077255,0.0000074002965],"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":[0.000054019092,0.000014893572,0.00021911268,0.00036309785,0.00010757754,0.000028343498,0.00023145336,0.7208828,0.054925486,0.00061942625,0.0059040436,0.21664977],"study_design_scores_gemma":[0.00031971373,0.00003855626,0.00007586735,0.0001286957,0.000018060638,0.0000093695635,0.000010874082,0.9304954,0.047353122,0.000053605785,0.021245161,0.00025160064],"about_ca_topic_score_codex":0.0000103018965,"about_ca_topic_score_gemma":0.0002695627,"teacher_disagreement_score":0.8908361,"about_ca_system_score_codex":0.000072967516,"about_ca_system_score_gemma":0.000013026356,"threshold_uncertainty_score":0.89179116},"labels":[],"label_agreement":null},{"id":"W4379229730","doi":"10.23977/jeeem.2023.060303","title":"Short-term load prediction based on Pearson-optimized CNN-LSTM hybrid neural network","year":2023,"lang":"en","type":"article","venue":"Journal of Electrotechnology Electrical Engineering and Management","topic":"Energy Load and Power Forecasting","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":"Computer science; Artificial neural network; Electric power system; Artificial intelligence; Smart grid; Pearson product-moment correlation coefficient; Power grid; Power (physics); Inefficiency; Term (time); Stability (learning theory); Convolutional neural network; Machine learning; Statistics; Engineering; Mathematics","score_opus":0.005616281758078104,"score_gpt":0.1860280322208132,"score_spread":0.1804117504627351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379229730","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":[0.7491395,0.0038213267,0.23697792,0.0011351148,0.0023135697,0.0006602255,0.0000053917074,0.004026204,0.0019207558],"genre_scores_gemma":[0.99585843,0.0016796982,0.0020403133,0.00004416298,0.00023568944,0.000022375127,0.000004532575,0.000052371433,0.00006242391],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984823,0.000017501763,0.0004073161,0.00018231638,0.00027914078,0.00063145143],"domain_scores_gemma":[0.999559,0.00008166697,0.000055756514,0.00016306773,0.00003725244,0.00010328951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036580747,0.00024284088,0.0003304453,0.0006099577,0.000073358984,0.000033351625,0.00019488699,0.000119375436,0.0000041535404],"category_scores_gemma":[0.000042964137,0.000231262,0.000111073685,0.0008360535,0.000019472576,0.00007530056,0.000033780663,0.0007680834,0.000003767605],"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":[0.000057087764,0.000019039488,0.0000485528,0.000057099758,0.00014377611,0.00024658255,0.000004954273,0.9654753,0.0015569603,0.00062564365,0.0026410483,0.02912391],"study_design_scores_gemma":[0.00067914906,0.00053619675,0.000692015,0.000106446656,0.00006786646,0.00013802013,0.0000019006716,0.9918045,0.0016820924,0.00010171357,0.003991004,0.00019911067],"about_ca_topic_score_codex":4.5252708e-7,"about_ca_topic_score_gemma":1.7046501e-7,"teacher_disagreement_score":0.24671893,"about_ca_system_score_codex":0.00016875721,"about_ca_system_score_gemma":0.00001166789,"threshold_uncertainty_score":0.9430591},"labels":[],"label_agreement":null},{"id":"W4379231384","doi":"10.56958/jesi.2020.5.4.5","title":"Wind resource assessment system based on time-scale-dependent roughness","year":2020,"lang":"en","type":"article","venue":"Journal of Engineering Sciences and Innovation","topic":"Energy Load and Power Forecasting","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":"Saint Mary's University","funders":"","keywords":"Wind speed; Wind power; Context (archaeology); Scale (ratio); Environmental science; Meteorology; Wind resource assessment; Computer science; Resource (disambiguation); Wind direction; Geography; Engineering; Cartography","score_opus":0.014303011054477769,"score_gpt":0.22250704788648293,"score_spread":0.20820403683200517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379231384","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":[0.8963102,0.00009727862,0.09627525,0.00044766313,0.00050451286,0.00005445036,0.0000026449445,0.00011036059,0.006197643],"genre_scores_gemma":[0.9956243,0.0000025259797,0.0040367353,0.00008133097,0.00023625616,4.2253467e-7,9.830442e-7,0.000010074408,0.0000073750293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991951,0.000008005111,0.00031514332,0.00008064841,0.00028259523,0.00011850243],"domain_scores_gemma":[0.9997195,0.000041464027,0.000091670154,0.000039358252,0.000059291124,0.000048743303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048044507,0.00009098835,0.00013323825,0.00024127876,0.000055542314,0.00007637887,0.000104983286,0.00003429194,0.000005347597],"category_scores_gemma":[0.000029056526,0.00007534474,0.00002108459,0.0008232709,0.000014718299,0.00019719607,0.000009126725,0.00016309113,9.684292e-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":[0.0000035461546,0.0000037223701,0.00024135273,0.00006335728,0.0000058708906,0.000003937032,0.00006407131,0.9855561,0.011470554,0.0012465131,0.00011205929,0.0012289097],"study_design_scores_gemma":[0.00019103874,0.00019410146,0.0008370866,0.00021769489,0.0000057211378,0.00001557177,0.00006778804,0.99359936,0.002334277,0.0000023679104,0.0024427497,0.00009224383],"about_ca_topic_score_codex":4.1144833e-7,"about_ca_topic_score_gemma":3.5515953e-8,"teacher_disagreement_score":0.0993141,"about_ca_system_score_codex":0.000045588666,"about_ca_system_score_gemma":0.000022090273,"threshold_uncertainty_score":0.30724692},"labels":[],"label_agreement":null},{"id":"W4380029001","doi":"10.54254/2755-2721/3/20230348","title":"Comparative analysis of renewable energy","year":2023,"lang":"en","type":"article","venue":"Applied and Computational Engineering","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Renewable energy; Wind power; Environmental economics; Climate change; Energy transition; Natural resource economics; Natural resource; Process (computing); Environmental resource management; Business; Environmental planning; Environmental science; Computer science; Engineering; Economics; Political science; Ecology","score_opus":0.01022193923041924,"score_gpt":0.20273463567647046,"score_spread":0.1925126964460512,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380029001","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":[0.5770288,0.00037465218,0.39471972,0.000009376993,0.00020192197,0.000051681745,0.000032793967,0.0009113096,0.026669722],"genre_scores_gemma":[0.99792874,0.00001626745,0.0018656229,0.0000063333096,0.0000238769,0.000008519215,0.000104064726,0.000008673523,0.00003789026],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999572,0.0000017153558,0.00013736173,0.000086594664,0.00008847133,0.00011389378],"domain_scores_gemma":[0.99977535,0.00011436158,0.000014566095,0.00004551555,0.000013987598,0.000036199235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000041165447,0.00008430014,0.00019192723,0.00030423678,0.000023682845,0.000009478953,0.0000401159,0.000026342288,0.0000083025525],"category_scores_gemma":[0.0000014328796,0.0000919717,0.000035081986,0.00096763956,0.000010993116,0.000027592589,0.000016688162,0.00003358953,0.0000018726554],"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":[0.0000011044417,0.0000020566547,0.000041617626,0.000015815041,0.00030848238,6.309333e-7,0.00019949445,0.9826136,0.0020815614,0.014110354,0.00012987865,0.0004953997],"study_design_scores_gemma":[0.00007860906,0.0000037174195,0.0034053042,0.0000072767675,0.000054467284,5.2935803e-7,0.00003544685,0.9936558,0.00133318,0.0006028647,0.00072861894,0.00009415853],"about_ca_topic_score_codex":0.000018893359,"about_ca_topic_score_gemma":0.000005413099,"teacher_disagreement_score":0.42089993,"about_ca_system_score_codex":0.0000075215526,"about_ca_system_score_gemma":0.0000038687454,"threshold_uncertainty_score":0.3750497},"labels":[],"label_agreement":null},{"id":"W4380356255","doi":"10.1016/j.jprocont.2023.103009","title":"Self-tuning kernel Gaussian method for predictive control systems","year":2023,"lang":"en","type":"article","venue":"Journal of Process Control","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Control theory (sociology); Kernel (algebra); Controller (irrigation); Model predictive control; Computer science; Gaussian; Gaussian process; Process (computing); Mathematical optimization; Control engineering; Engineering; Control (management); Artificial intelligence; Mathematics","score_opus":0.010371220751323698,"score_gpt":0.2572107231875239,"score_spread":0.24683950243620023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380356255","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":[0.0051983106,0.0021185977,0.98838276,0.0001631801,0.0017084136,0.00039650162,0.00005713552,0.00040580472,0.0015693072],"genre_scores_gemma":[0.9958538,0.00003796584,0.0027611947,0.000045192075,0.0010899574,0.00006512849,0.0000023469895,0.00005930284,0.000085136904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985728,0.000058334637,0.00059357885,0.000117898024,0.00027388745,0.00038349905],"domain_scores_gemma":[0.9986159,0.00052549056,0.0002905861,0.00009162177,0.0003196955,0.00015669975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009805327,0.00018963832,0.00050584285,0.00024334279,0.0000942872,0.00008023522,0.00022428826,0.00011217333,0.0000040343302],"category_scores_gemma":[0.00019369677,0.00015710125,0.00017450488,0.00026092312,0.000010712291,0.00029476974,0.000005944143,0.000278671,0.0000043561085],"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":[0.00016834392,0.000018430772,0.00040925617,0.0006008109,0.0006870551,0.0000374366,0.0011358645,0.9911791,0.0022839736,0.00038120418,0.0008874579,0.0022110767],"study_design_scores_gemma":[0.003979639,0.00021368742,0.00010183875,0.0002658652,0.00020220209,0.00008648952,0.00040755727,0.98781073,0.0003377425,0.00029282583,0.006135014,0.00016642977],"about_ca_topic_score_codex":0.0000019550803,"about_ca_topic_score_gemma":0.0000011131045,"teacher_disagreement_score":0.9906555,"about_ca_system_score_codex":0.00006884936,"about_ca_system_score_gemma":0.000073795185,"threshold_uncertainty_score":0.6406403},"labels":[],"label_agreement":null},{"id":"W4380793245","doi":"10.18280/ejee.250201","title":"Optimal Energy Tracking in a Solar Power System Utilizing Synthetic Neural Network","year":2023,"lang":"en","type":"article","venue":"European Journal of Electrical Engineering","topic":"Energy Load and Power Forecasting","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":"Artificial neural network; Tracking (education); Computer science; Power (physics); Solar energy; Environmental science; Artificial intelligence; Engineering; Electrical engineering; Physics; Psychology","score_opus":0.0102656047171594,"score_gpt":0.18847145571508345,"score_spread":0.17820585099792405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4380793245","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":[0.9068457,0.0041925013,0.07888192,0.000032916927,0.002455881,0.000066893284,0.000001512848,0.0010601089,0.006462554],"genre_scores_gemma":[0.9980688,0.00007643041,0.0011587811,0.000010716831,0.00054107234,0.0000010487674,0.0000011242298,0.00012742798,0.000014610802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804074,0.00011010223,0.00072786387,0.000149128,0.00025491914,0.00071726163],"domain_scores_gemma":[0.9993628,0.0001927376,0.000093131755,0.00013143528,0.00004034535,0.00017951542],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009459719,0.00024885192,0.00036661184,0.00049224624,0.000049994742,0.000072268645,0.00029586785,0.0000477761,0.0000065238096],"category_scores_gemma":[0.00012073448,0.00024603817,0.00016772716,0.0012630416,0.000009241208,0.000196414,0.000041078907,0.0006434388,0.000012279327],"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":[0.000011367174,0.000006168886,0.00006772187,0.00003636475,0.000038622486,0.0015416043,0.00013346753,0.98619765,0.0034022727,0.00046159732,0.00016678845,0.0079363715],"study_design_scores_gemma":[0.00035361637,0.00012260224,0.0011518147,0.0005433607,0.000016630966,0.00057105126,0.000041464198,0.9927002,0.0008220633,0.0000019245354,0.0033998548,0.0002753778],"about_ca_topic_score_codex":0.000001186019,"about_ca_topic_score_gemma":3.9593306e-7,"teacher_disagreement_score":0.091223076,"about_ca_system_score_codex":0.00013029456,"about_ca_system_score_gemma":0.000013797093,"threshold_uncertainty_score":0.99999917},"labels":[],"label_agreement":null},{"id":"W4381186353","doi":"10.11159/cdsr23.207","title":"A Radial Basis Function Neural Network Approach to Filtering Stochastic Wind Speed Data","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Conference of Control, Dynamic systems, and Robotics","topic":"Energy Load and Power Forecasting","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 Calgary","funders":"","keywords":"Radial basis function; Computer science; Artificial neural network; Basis (linear algebra); Wind speed; Radial basis function network; Function (biology); Artificial intelligence; Mathematics; Meteorology; Physics","score_opus":0.03795606731835914,"score_gpt":0.23158936702228863,"score_spread":0.1936332997039295,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381186353","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":[0.8492938,0.0006169687,0.12306866,0.00073645153,0.012585914,0.0011726025,0.00041120805,0.00042301408,0.011691326],"genre_scores_gemma":[0.99900293,0.000019922742,0.00043403613,0.000014327372,0.00027766338,0.0000031585907,0.000028703098,0.00002130767,0.00019793623],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891794,0.000006341932,0.00038298115,0.00019506853,0.00029676533,0.00020089932],"domain_scores_gemma":[0.9993713,0.00007663308,0.00015068467,0.0001272828,0.00021822986,0.000055891116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032187023,0.00014937116,0.000275488,0.00011078374,0.000058319038,0.000091415364,0.00060396135,0.000061951745,0.0000023424252],"category_scores_gemma":[0.000109588625,0.0001221203,0.000042026,0.00019414346,0.000043494536,0.00020092887,0.00019634866,0.000119144264,0.0000011892312],"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":[0.000045078126,0.0000073087767,0.0006818436,0.00016507939,0.0001404193,1.3804267e-7,0.00012087064,0.98853284,0.0040451344,0.0054782457,0.00037291355,0.00041015533],"study_design_scores_gemma":[0.000370604,0.000029795125,0.0014273821,0.0003181774,0.000057667137,0.0000132883315,0.000205873,0.9972262,0.000010479566,0.00016032984,0.0000646669,0.00011552883],"about_ca_topic_score_codex":0.000030550415,"about_ca_topic_score_gemma":0.0000054168004,"teacher_disagreement_score":0.14970909,"about_ca_system_score_codex":0.000029093404,"about_ca_system_score_gemma":0.000018265875,"threshold_uncertainty_score":0.4979921},"labels":[],"label_agreement":null},{"id":"W4381192962","doi":"10.32920/23541828.v1","title":"Fault Detection and Diagnosis for Central Heating System Using Equipment Emulators and Vibration Monitoring Techniques","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University; Sciencetech (Canada)","funders":"","keywords":"HVAC; Leverage (statistics); Fault detection and isolation; Automation; Vibration; Engineering; Boiler (water heating); Condition monitoring; Reliability engineering; Computer science; Real-time computing; Control engineering; Artificial intelligence; Mechanical engineering; Electrical engineering","score_opus":0.03574703273348581,"score_gpt":0.2632345792151601,"score_spread":0.22748754648167427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381192962","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":[0.7866375,0.00036986682,0.20951855,0.000005083218,0.0015048524,0.0004592504,0.000013614543,0.0014053986,0.00008589276],"genre_scores_gemma":[0.97550917,0.00018961348,0.023372782,0.0000015097231,0.00056043925,0.00026810373,0.000011828155,0.00007705778,0.000009476385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989688,0.000015792444,0.000321792,0.00029819415,0.00010712005,0.00028833342],"domain_scores_gemma":[0.99961007,0.000101047546,0.000066271474,0.000114462855,0.000031356467,0.00007682366],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016946165,0.0002498809,0.00024475806,0.00015055867,0.00016359035,0.00018123073,0.000047626567,0.00024465154,5.517641e-7],"category_scores_gemma":[0.000023465032,0.00026449567,0.000051205636,0.00006883445,0.00001093485,0.00012258838,0.00014403777,0.00019547949,1.408598e-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":[0.00001544459,0.000011584316,0.029167218,0.009235906,0.00026859881,0.0000062415547,0.0016146996,0.8031919,0.04465535,0.00028495988,0.000013843375,0.111534245],"study_design_scores_gemma":[0.000085424945,0.000022835553,0.00076975033,0.0018496218,0.000049948507,0.0000059945205,0.00031649982,0.7894275,0.20707808,0.00006714472,0.000037302074,0.00028989423],"about_ca_topic_score_codex":0.0003182114,"about_ca_topic_score_gemma":0.00003626425,"teacher_disagreement_score":0.1888717,"about_ca_system_score_codex":0.00024833885,"about_ca_system_score_gemma":0.000009035961,"threshold_uncertainty_score":0.99998075},"labels":[],"label_agreement":null},{"id":"W4381192996","doi":"10.32920/23541828","title":"Fault Detection and Diagnosis for Central Heating System Using Equipment Emulators and Vibration Monitoring Techniques","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University; Sciencetech (Canada)","funders":"","keywords":"HVAC; Fault detection and isolation; Leverage (statistics); Automation; Engineering; Vibration; Boiler (water heating); Condition monitoring; Reliability engineering; Computer science; Real-time computing; Control engineering; Artificial intelligence; Mechanical engineering; Electrical engineering","score_opus":0.03574703273348581,"score_gpt":0.2632345792151601,"score_spread":0.22748754648167427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381192996","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":[0.7866375,0.00036986682,0.20951855,0.000005083218,0.0015048524,0.0004592504,0.000013614543,0.0014053986,0.00008589276],"genre_scores_gemma":[0.97550917,0.00018961348,0.023372782,0.0000015097231,0.00056043925,0.00026810373,0.000011828155,0.00007705778,0.000009476385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989688,0.000015792444,0.000321792,0.00029819415,0.00010712005,0.00028833342],"domain_scores_gemma":[0.99961007,0.000101047546,0.000066271474,0.000114462855,0.000031356467,0.00007682366],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016946165,0.0002498809,0.00024475806,0.00015055867,0.00016359035,0.00018123073,0.000047626567,0.00024465154,5.517641e-7],"category_scores_gemma":[0.000023465032,0.00026449567,0.000051205636,0.00006883445,0.00001093485,0.00012258838,0.00014403777,0.00019547949,1.408598e-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":[0.00001544459,0.000011584316,0.029167218,0.009235906,0.00026859881,0.0000062415547,0.0016146996,0.8031919,0.04465535,0.00028495988,0.000013843375,0.111534245],"study_design_scores_gemma":[0.000085424945,0.000022835553,0.00076975033,0.0018496218,0.000049948507,0.0000059945205,0.00031649982,0.7894275,0.20707808,0.00006714472,0.000037302074,0.00028989423],"about_ca_topic_score_codex":0.0003182114,"about_ca_topic_score_gemma":0.00003626425,"teacher_disagreement_score":0.1888717,"about_ca_system_score_codex":0.00024833885,"about_ca_system_score_gemma":0.000009035961,"threshold_uncertainty_score":0.99998075},"labels":[],"label_agreement":null},{"id":"W4381194074","doi":"10.11159/ehst23.120","title":"Deep Learning-Based Models for Wind and Solar Curtailment Forecasting","year":2023,"lang":"en","type":"article","venue":"Proceedings of the International Conference of Energy Harvesting, Storage, and Transfer","topic":"Energy Load and Power Forecasting","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":"Computer science; Meteorology; Solar wind; Environmental science; Artificial intelligence; Geography; Physics","score_opus":0.03519860510791901,"score_gpt":0.21741158653531914,"score_spread":0.18221298142740014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381194074","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":[0.9626363,0.00015042147,0.029388828,0.000112471615,0.00026264373,0.00010134842,0.000019775733,0.00011073747,0.007217495],"genre_scores_gemma":[0.99867254,0.00009595634,0.00072037114,0.000015628844,0.0000554499,0.000019262687,0.000010824961,0.000028515258,0.00038146335],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912274,0.0000038146645,0.00028284092,0.000185221,0.00021406938,0.00019133497],"domain_scores_gemma":[0.9994459,0.00010845959,0.00006487141,0.000039462906,0.00028612302,0.000055164273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022142523,0.0001633242,0.0001906527,0.00012834038,0.000100273886,0.00006562881,0.00022136269,0.000066081004,0.000007960795],"category_scores_gemma":[0.00009702652,0.00014031502,0.00006960801,0.00012646274,0.00009235905,0.00026126843,0.00003876872,0.00012087686,8.39252e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009040746,0.000037639566,0.0052919285,0.0008120174,0.00023204758,0.0000012463343,0.0016022881,0.81999207,0.034671944,0.12283268,0.00007899711,0.014356718],"study_design_scores_gemma":[0.00047315238,0.00007038491,0.00037084418,0.0003568052,0.000031170493,0.0000046400746,0.00015503679,0.9716271,0.022756571,0.0030052855,0.0009976233,0.00015137777],"about_ca_topic_score_codex":0.00005065065,"about_ca_topic_score_gemma":0.00003074303,"teacher_disagreement_score":0.15163502,"about_ca_system_score_codex":0.000014596493,"about_ca_system_score_gemma":0.00001932959,"threshold_uncertainty_score":0.572188},"labels":[],"label_agreement":null},{"id":"W4381389653","doi":"10.18280/ejee.250103","title":"Optimal Energy Tracking in a Solar Power System Utilizing Synthetic Neural Network","year":2023,"lang":"en","type":"article","venue":"European Journal of Electrical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Artificial neural network; Tracking (education); Power (physics); Computer science; Energy (signal processing); Solar energy; Photovoltaic system; Artificial intelligence; Engineering; Electrical engineering; Physics; Psychology","score_opus":0.0102656047171594,"score_gpt":0.18847145571508345,"score_spread":0.17820585099792405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381389653","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":[0.9068457,0.0041925013,0.07888192,0.000032916927,0.002455881,0.000066893284,0.000001512848,0.0010601089,0.006462554],"genre_scores_gemma":[0.9980688,0.00007643041,0.0011587811,0.000010716831,0.00054107234,0.0000010487674,0.0000011242298,0.00012742798,0.000014610802],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99804074,0.00011010223,0.00072786387,0.000149128,0.00025491914,0.00071726163],"domain_scores_gemma":[0.9993628,0.0001927376,0.000093131755,0.00013143528,0.00004034535,0.00017951542],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009459719,0.00024885192,0.00036661184,0.00049224624,0.000049994742,0.000072268645,0.00029586785,0.0000477761,0.0000065238096],"category_scores_gemma":[0.00012073448,0.00024603817,0.00016772716,0.0012630416,0.000009241208,0.000196414,0.000041078907,0.0006434388,0.000012279327],"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":[0.000011367174,0.000006168886,0.00006772187,0.00003636475,0.000038622486,0.0015416043,0.00013346753,0.98619765,0.0034022727,0.00046159732,0.00016678845,0.0079363715],"study_design_scores_gemma":[0.00035361637,0.00012260224,0.0011518147,0.0005433607,0.000016630966,0.00057105126,0.000041464198,0.9927002,0.0008220633,0.0000019245354,0.0033998548,0.0002753778],"about_ca_topic_score_codex":0.000001186019,"about_ca_topic_score_gemma":3.9593306e-7,"teacher_disagreement_score":0.091223076,"about_ca_system_score_codex":0.00013029456,"about_ca_system_score_gemma":0.000013797093,"threshold_uncertainty_score":0.99999917},"labels":[],"label_agreement":null},{"id":"W4383751125","doi":"10.1109/tetci.2023.3290050","title":"FSNet: A Hybrid Model for Seasonal Forecasting","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Energy Load and Power Forecasting","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":"Concordia University","funders":"","keywords":"Computer science; Autocorrelation; Partial autocorrelation function; Replicate; Fourier transform; Artificial neural network; Data mining; Artificial intelligence; Machine learning; Time series; Autoregressive integrated moving average; Statistics; Mathematics","score_opus":0.0655555301463283,"score_gpt":0.2926821400195836,"score_spread":0.22712660987325528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383751125","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":[0.03732823,0.000024473167,0.9608511,0.000111127716,0.0008083516,0.00012441962,0.00005261535,0.0003396932,0.0003600278],"genre_scores_gemma":[0.97723365,0.000029912753,0.022155462,0.000047832702,0.00009483766,0.00008409308,0.000022775122,0.000034842356,0.00029659038],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989838,0.000013807259,0.00030194354,0.00021257762,0.00018651043,0.0003013441],"domain_scores_gemma":[0.99938613,0.0003846717,0.000024338518,0.000092109345,0.00005707629,0.000055688964],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020635947,0.00015432673,0.00012953268,0.00026464608,0.00016494463,0.000028762262,0.00014330633,0.00004895046,0.000026741372],"category_scores_gemma":[0.000014524631,0.00018333625,0.000088485045,0.00040589535,0.000026800411,0.00011014741,0.0000015647954,0.0002621275,0.00001859396],"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":[0.0000073919855,0.00001592723,0.000011233504,0.000042740263,0.000016429749,0.0000049435735,0.0004026501,0.9368161,0.000016220109,0.001236465,0.00010902418,0.061320852],"study_design_scores_gemma":[0.000083965,0.000018299997,0.000013319886,0.00008696193,0.000005448297,0.0000067417313,0.000047207308,0.9816796,0.0016545469,0.016008757,0.00021480439,0.00018033141],"about_ca_topic_score_codex":0.0000053314393,"about_ca_topic_score_gemma":0.000040624265,"teacher_disagreement_score":0.9399054,"about_ca_system_score_codex":0.00007680557,"about_ca_system_score_gemma":0.000032048036,"threshold_uncertainty_score":0.7476235},"labels":[],"label_agreement":null},{"id":"W4383893960","doi":"10.20944/preprints202307.0662.v1","title":"The Impacts of COVID-19 and Roles of Artificial Intelligence on Energy Sector: An Analytical Review on the Pre-, Mid- and Post-Pandemic Perspectives","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"International Development Research Centre; Tenaga Nasional Berhad","keywords":"Pandemic; Efficient energy use; Energy (signal processing); Energy sector; Business; Coronavirus disease 2019 (COVID-19); Energy supply; Environmental economics; Risk analysis (engineering); Economics; Engineering; Medicine","score_opus":0.14552575713990268,"score_gpt":0.3620133692393608,"score_spread":0.2164876120994581,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4383893960","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":[0.99041986,0.007452189,0.00022204193,0.00060850324,0.00018984864,0.0002786907,0.000053688986,0.00013686129,0.0006383187],"genre_scores_gemma":[0.97106355,0.028600063,0.000012908023,0.00010264484,0.00009255493,0.00003575371,0.0000113501,0.000041072595,0.00004010534],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99819875,0.00022176809,0.00054777705,0.0004999354,0.00027504985,0.000256712],"domain_scores_gemma":[0.9976669,0.0010700844,0.00019591076,0.0008029626,0.00009418212,0.00016996516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011847621,0.00031116224,0.00045950207,0.000112214104,0.000111568246,0.000024528732,0.00043289465,0.00017915388,0.00005731548],"category_scores_gemma":[0.0015792327,0.00020390586,0.00012806266,0.00015693203,0.0002927365,0.000041629144,0.00046094332,0.0005737756,0.000005876471],"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":[0.0016318369,0.00071928784,0.16355163,0.022037523,0.0034656136,0.000047178808,0.06926083,0.3878463,0.046339713,0.22398753,0.0002873648,0.08082521],"study_design_scores_gemma":[0.0008099259,0.0017273161,0.34182137,0.037520804,0.00166956,0.00014913312,0.019519567,0.21499424,0.27406117,0.09611667,0.006814368,0.0047958703],"about_ca_topic_score_codex":0.00037789202,"about_ca_topic_score_gemma":0.00052073906,"teacher_disagreement_score":0.22772147,"about_ca_system_score_codex":0.00008403961,"about_ca_system_score_gemma":0.00010790991,"threshold_uncertainty_score":0.831504},"labels":[],"label_agreement":null},{"id":"W4384210856","doi":"10.20508/ijsmartgrid.v7i2.287.g274","title":"Three-Dimensional Fuzzy Logic Applied to DC Voltage Regulation in Active Power Filter of PV System","year":2023,"lang":"en","type":"article","venue":"International Journal of Smart grid","topic":"Energy Load and Power Forecasting","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":"Carbon Engineering (Canada)","funders":"","keywords":"Fuzzy logic; Total harmonic distortion; Control theory (sociology); Computer science; Control engineering; MATLAB; Electric power system; Fuzzy electronics; Voltage; Power (physics); Fuzzy control system; Electronic engineering; Engineering; Control (management); Neuro-fuzzy; Artificial intelligence; Electrical engineering","score_opus":0.014202882548473792,"score_gpt":0.22767412802883022,"score_spread":0.21347124548035643,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384210856","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":[0.98295563,0.000039085775,0.005314768,0.00021511131,0.004422092,0.00008823006,0.000043428583,0.00006286005,0.0068588075],"genre_scores_gemma":[0.99888384,0.0000027740211,0.0005537465,0.00004177748,0.0004442974,0.0000037700993,0.00001550061,0.00001818983,0.00003609618],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9988344,0.000009014352,0.00048082,0.00009017868,0.00044972074,0.0001358907],"domain_scores_gemma":[0.99942976,0.00009031132,0.00014900882,0.00007276889,0.00019951533,0.000058658545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027487878,0.0001088309,0.00019217141,0.00047853173,0.000015172953,0.00001671753,0.00021307616,0.000058191,0.00005654101],"category_scores_gemma":[0.000034228287,0.00009874465,0.00008073077,0.00023344993,0.000014086438,0.0001405863,0.000051105675,0.00016392178,0.00004249728],"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":[0.00039203133,0.000047011225,0.0028671825,0.00004734132,0.0002638716,0.00017614245,0.0005866374,0.90415806,0.07321847,0.008163905,0.0060919877,0.003987366],"study_design_scores_gemma":[0.0072739315,0.0008679124,0.59099865,0.0049690935,0.000104373044,0.0009866706,0.0012833563,0.14414404,0.21194711,0.013862127,0.021891093,0.0016716463],"about_ca_topic_score_codex":0.00001291899,"about_ca_topic_score_gemma":0.000035745616,"teacher_disagreement_score":0.760014,"about_ca_system_score_codex":0.00015535147,"about_ca_system_score_gemma":0.00002375745,"threshold_uncertainty_score":0.40266898},"labels":[],"label_agreement":null},{"id":"W4384434192","doi":"10.1007/978-3-031-36115-9_11","title":"Spectrum Analysis on Electricity Consumption Periods by Industry in Fujian Province","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes on data engineering and communications technologies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Electricity; Consumption (sociology); Quarter (Canadian coin); Agricultural economics; Electric power industry; Electricity generation; Energy consumption; Business; Geography; Engineering; Economics; Power (physics)","score_opus":0.030980900909453993,"score_gpt":0.24166830837960437,"score_spread":0.21068740747015038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384434192","genre_codex":"review","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":[0.11212525,0.27883536,0.20799802,0.04115796,0.0033695446,0.00858727,0.032007378,0.15140735,0.16451187],"genre_scores_gemma":[0.9715286,0.021449815,0.0022183429,0.000041909003,0.000034828805,0.000056182005,0.0032527682,0.00016716906,0.0012504049],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882996,0.000010172365,0.00030376748,0.00042073202,0.00014293387,0.00029242493],"domain_scores_gemma":[0.9965274,0.00043727361,0.000069552465,0.002922195,0.000011398632,0.00003221817],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.00013949123,0.00042046868,0.00042958444,0.0010106412,0.000106115775,0.000081375445,0.0014381155,0.0013078905,0.0000067356077],"category_scores_gemma":[0.00020212846,0.0004217149,0.000055115936,0.00043684256,0.00010359858,0.00007930029,0.0005044806,0.0033507869,0.000013993385],"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":[0.000026155632,0.00009553169,0.0012677413,0.0005757813,0.0017952181,0.000034668436,0.00016082978,0.7256374,0.0008956353,0.06175604,0.0021200709,0.20563494],"study_design_scores_gemma":[0.00053912314,0.00031946757,0.0008512086,0.002061312,0.0007869967,0.000019891753,0.000032741842,0.84042835,0.0044126776,0.004559106,0.14314711,0.002842027],"about_ca_topic_score_codex":0.00002511045,"about_ca_topic_score_gemma":0.0006150562,"teacher_disagreement_score":0.8594033,"about_ca_system_score_codex":0.00012673631,"about_ca_system_score_gemma":0.00001775365,"threshold_uncertainty_score":0.9999886},"labels":[],"label_agreement":null},{"id":"W4384929685","doi":"10.3390/forecast5030028","title":"A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes","year":2023,"lang":"en","type":"article","venue":"Forecasting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electricity price forecasting; Volatility (finance); Econometrics; Electricity market; Computer science; Electricity price; Electricity; Artificial neural network; Dimension (graph theory); Time horizon; Market price; Economics; Artificial intelligence; Microeconomics; Finance; Engineering","score_opus":0.08294379196822219,"score_gpt":0.2577073454779408,"score_spread":0.1747635535097186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4384929685","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":[0.41585287,0.0003303444,0.57404876,0.000026112273,0.0008519129,0.0007895441,0.00006854396,0.0026734404,0.0053584655],"genre_scores_gemma":[0.92974836,0.000036403155,0.068068445,0.00005991627,0.00053692085,0.00032796554,0.00009086222,0.00026079864,0.0008703218],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9963702,0.00004059696,0.00090099685,0.00063695846,0.0003512468,0.0017000046],"domain_scores_gemma":[0.9978872,0.001095681,0.00022301638,0.000351102,0.00017741477,0.00026560426],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012656099,0.0005976743,0.0006887285,0.0004924003,0.00059794803,0.00017443638,0.00036706353,0.00017089663,0.00001584341],"category_scores_gemma":[0.0017103839,0.00063531566,0.00031948645,0.00092163053,0.00005359551,0.0004721827,0.00016193333,0.00049289025,0.000035168014],"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":[0.00003914636,0.00003057537,0.0004676878,0.00056640874,0.00009805574,0.000060060476,0.0013729326,0.9672998,0.0045630997,0.0004398913,0.0015034616,0.02355885],"study_design_scores_gemma":[0.0014655713,0.000058052807,0.00003159273,0.00034589542,0.000048995258,0.00011113981,0.000085759224,0.98699147,0.008146961,0.0006617234,0.0013375113,0.00071535417],"about_ca_topic_score_codex":0.000020151085,"about_ca_topic_score_gemma":0.000037793343,"teacher_disagreement_score":0.5138955,"about_ca_system_score_codex":0.00019275269,"about_ca_system_score_gemma":0.00008747352,"threshold_uncertainty_score":0.9996098},"labels":[],"label_agreement":null},{"id":"W4385273673","doi":"10.35833/mpce.2022.000138","title":"Hierarchical Frequency-dependent Chance Constrained Unit Commitment for Bulk AC/DC Hybrid Power Systems with Wind Power Generation","year":2023,"lang":"en","type":"article","venue":"Journal of Modern Power Systems and Clean Energy","topic":"Energy Load and Power Forecasting","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":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Electric power system; Power system simulation; Wind power; Control theory (sociology); Upgrade; Frequency deviation; Power (physics); Computer science; Engineering; Automatic frequency control; Electrical engineering","score_opus":0.019725909539199598,"score_gpt":0.21813267736259695,"score_spread":0.19840676782339736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385273673","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":[0.76214355,0.005148173,0.22397363,0.00015353576,0.0046254587,0.00034456557,0.00014066033,0.00020813788,0.0032622889],"genre_scores_gemma":[0.9984412,0.00021212819,0.00017074397,0.00005053274,0.0003350494,0.00002430966,0.000029998266,0.00010195854,0.00063403294],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976697,0.000092703245,0.00087090227,0.00028712317,0.0005463425,0.00053323497],"domain_scores_gemma":[0.99872214,0.00011432501,0.00033460828,0.00027676616,0.00025077476,0.00030141795],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006284565,0.00038105482,0.00062570773,0.00028350056,0.00018704597,0.00022143926,0.00023432015,0.00015185402,0.0000144669675],"category_scores_gemma":[0.000018923307,0.0003029728,0.00013516362,0.00017338798,0.00007295902,0.00027746154,0.00003618009,0.00029405943,0.0000026342223],"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":[0.00026417093,0.00014826855,0.00042002465,0.00031976425,0.0013228857,0.0006197177,0.00215112,0.92057514,0.025273083,0.041030765,0.0047063413,0.0031686986],"study_design_scores_gemma":[0.009834531,0.00473759,0.0007953208,0.003275347,0.00033841337,0.0063850554,0.0036096736,0.86382306,0.009787976,0.0011423955,0.09355964,0.0027110137],"about_ca_topic_score_codex":0.000061497376,"about_ca_topic_score_gemma":0.000046662768,"teacher_disagreement_score":0.2362977,"about_ca_system_score_codex":0.000105674735,"about_ca_system_score_gemma":0.00008259493,"threshold_uncertainty_score":0.99994224},"labels":[],"label_agreement":null},{"id":"W4385597561","doi":"10.1016/j.ejor.2023.08.003","title":"Stochastic search for a parametric cost function approximation: Energy storage with rolling forecasts","year":2023,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Benchmark (surveying); Computer science; Mathematical optimization; Stochastic approximation; Variance (accounting); Function (biology); Parametric statistics; Convergence (economics); Variance reduction; Stochastic modelling; Stochastic optimization; Mathematics; Statistics; Key (lock)","score_opus":0.08872406472949086,"score_gpt":0.30476245359214615,"score_spread":0.2160383888626553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385597561","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":[0.15263306,0.00029630357,0.8419102,0.00017143709,0.000361597,0.00022519818,0.000015740296,0.00007601522,0.0043104407],"genre_scores_gemma":[0.9961775,0.00003846585,0.0022814192,0.000016180531,0.0008103708,0.000019102874,0.000049064096,0.00006690341,0.0005410121],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979073,0.00027159727,0.0003775031,0.0001416512,0.00090857985,0.00039341676],"domain_scores_gemma":[0.9981321,0.0006514798,0.000047788755,0.00011333858,0.0008919307,0.00016333196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003790164,0.00012259823,0.0001567029,0.0009117847,0.0003104998,0.00020414506,0.00022059066,0.00002650202,0.000043012562],"category_scores_gemma":[0.0002455958,0.000099708624,0.00006490399,0.0013598761,0.000052405052,0.0003247682,0.00003587808,0.00040478355,0.00003800067],"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":[0.00018251472,0.00001740637,0.000025185456,0.00003487321,0.00006490883,0.000052073523,0.00020636748,0.97285384,0.0007743076,0.0022239478,0.0021894102,0.021375144],"study_design_scores_gemma":[0.0011834153,0.0008756756,0.0006811597,0.00017932609,0.0000128095935,0.00011778494,0.00022888507,0.9862592,0.0008688026,0.000104854706,0.009320768,0.0001672853],"about_ca_topic_score_codex":0.0000018612659,"about_ca_topic_score_gemma":0.0000028305562,"teacher_disagreement_score":0.8435444,"about_ca_system_score_codex":0.00010807476,"about_ca_system_score_gemma":0.00013508598,"threshold_uncertainty_score":0.40659997},"labels":[],"label_agreement":null},{"id":"W4385694025","doi":"10.20944/preprints202308.0693.v1","title":"Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"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 Guelph","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Wind speed; Solar irradiance; Irradiance; Computer science; Random forest; Python (programming language); Support vector machine; Meteorology; Ensemble learning; Ensemble forecasting; Mean absolute percentage error; Algorithm; Simulation; Artificial intelligence; Artificial neural network; Physics","score_opus":0.0959507331151295,"score_gpt":0.3525151933803349,"score_spread":0.2565644602652054,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385694025","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":[0.6748258,0.0041334243,0.29664755,0.0002846485,0.008051989,0.0015285169,0.00026631873,0.0043594968,0.009902278],"genre_scores_gemma":[0.9705502,0.0016522504,0.021129454,0.000018409002,0.00028836966,0.000082312465,0.00027318325,0.0002513152,0.005754518],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979973,0.00011624203,0.00047644437,0.0007664763,0.00013316385,0.00051040173],"domain_scores_gemma":[0.9986586,0.00036343714,0.0001287112,0.00060643983,0.00006227973,0.00018051628],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009976752,0.00043372545,0.0005023701,0.00023106833,0.0002276567,0.000049753533,0.00032878862,0.00040602716,0.0000722811],"category_scores_gemma":[0.000435419,0.0005106743,0.00019016746,0.00016788804,0.000057973695,0.0001014419,0.00065240875,0.0013125647,0.0001040171],"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":[0.000018670306,0.000018833282,0.043754477,0.0008915695,0.00037674926,0.0000069681478,0.0011853662,0.9273449,0.017094107,0.00011039021,0.000037541206,0.009160416],"study_design_scores_gemma":[0.00035384577,0.000019881,0.024921304,0.0003991349,0.0001242346,0.00001744631,0.00004925888,0.94694304,0.004735747,0.002733808,0.019123001,0.00057929364],"about_ca_topic_score_codex":0.00011946998,"about_ca_topic_score_gemma":0.000054385786,"teacher_disagreement_score":0.29572442,"about_ca_system_score_codex":0.00013284595,"about_ca_system_score_gemma":0.000044982233,"threshold_uncertainty_score":0.99973446},"labels":[],"label_agreement":null},{"id":"W4385724715","doi":"10.1177/0309524x231188696","title":"A radial basis function neural network approach to filtering stochastic wind speed data","year":2023,"lang":"en","type":"article","venue":"Wind Engineering","topic":"Energy Load and Power Forecasting","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 Calgary","funders":"","keywords":"Control theory (sociology); Wind speed; Filter (signal processing); SIGNAL (programming language); Noise (video); Artificial neural network; Smoothing; Computer science; Radial basis function network; Turbine; Wind power; Basis (linear algebra); Radial basis function; Control engineering; Engineering; Artificial intelligence; Mathematics; Control (management)","score_opus":0.03161696006842215,"score_gpt":0.20663391264696834,"score_spread":0.1750169525785462,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385724715","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":[0.73703986,0.00037448335,0.24474202,0.000046617824,0.009346959,0.00041019454,0.000085376014,0.0042899265,0.0036645855],"genre_scores_gemma":[0.9944963,0.000005240089,0.002635991,0.000029247736,0.0022979295,0.000004440034,0.00026968628,0.00013648128,0.00012471163],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837035,0.00000986383,0.0002774858,0.00038698022,0.0002106336,0.0007446862],"domain_scores_gemma":[0.99910814,0.000078238954,0.000018671682,0.00056411064,0.000012910429,0.00021794248],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025220364,0.00030283027,0.00025846396,0.00023467315,0.00009577092,0.00009855606,0.0004039763,0.000103688224,0.000022119586],"category_scores_gemma":[0.000059992173,0.0003454889,0.000057418943,0.0009870528,0.000008483772,0.00030296377,0.0002264972,0.00028998964,0.00009806542],"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":[0.000008801524,0.0000043770146,0.000039563467,0.00007178959,0.000058828584,0.0000078297635,0.00018637965,0.99079216,0.002627774,0.000043686745,0.0029276554,0.003231144],"study_design_scores_gemma":[0.00021002891,0.000022610846,0.001673231,0.000085494336,0.000029201712,0.000018427118,0.00002838288,0.9917881,0.00005696501,0.0000057469324,0.0057063648,0.00037545522],"about_ca_topic_score_codex":0.000011991876,"about_ca_topic_score_gemma":0.0000022721235,"teacher_disagreement_score":0.25745642,"about_ca_system_score_codex":0.000054754153,"about_ca_system_score_gemma":0.0000087245535,"threshold_uncertainty_score":0.9998997},"labels":[],"label_agreement":null},{"id":"W4385860002","doi":"10.1007/978-3-031-34593-7_75","title":"Forecasting of Solar Installation Capacity in Canada","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Regina","funders":"","keywords":"Polynomial regression; Renewable energy; Regression analysis; Linear regression; Solar power; Electricity; Regression; Environmental science; Photovoltaic system; Solar energy; Econometrics; Statistics; Meteorology; Mathematics; Engineering; Geography; Power (physics)","score_opus":0.022970266341472312,"score_gpt":0.17216676381977522,"score_spread":0.1491964974783029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385860002","genre_codex":"other","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":[0.22797322,0.008499006,0.20008267,0.00010795914,0.016681297,0.0021626395,0.0007252901,0.0026086892,0.5411592],"genre_scores_gemma":[0.9985397,0.000084115236,0.0007718465,0.0000075928874,0.00016549144,0.000008021779,0.000046192454,0.00019151386,0.0001855116],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99849606,0.000005080809,0.0005798914,0.00024884642,0.0002583585,0.0004117795],"domain_scores_gemma":[0.99922156,0.00039371147,0.00008187142,0.0002186201,0.000026808459,0.000057444428],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015011645,0.00041493683,0.0005288709,0.00046277963,0.000015120838,0.00001113716,0.00016375963,0.00033397446,0.000036527428],"category_scores_gemma":[0.00017767785,0.00049447035,0.000075931406,0.00020837963,0.000011662336,0.00006917731,0.000046635636,0.00092035474,0.0000014611604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000017617605,0.0000010406244,0.00034023315,0.0005267198,0.00003015937,0.000052318155,0.00018571103,0.9934584,0.00028100048,0.00037170947,0.0000074086784,0.004743526],"study_design_scores_gemma":[0.00021611605,0.000011865022,0.00023847811,0.0024931147,0.000014207352,0.000012501174,0.0000024213082,0.9916851,0.0017315735,0.0012763357,0.0017018507,0.00061646715],"about_ca_topic_score_codex":0.093446724,"about_ca_topic_score_gemma":0.9552926,"teacher_disagreement_score":0.86184585,"about_ca_system_score_codex":0.00078296004,"about_ca_system_score_gemma":0.0001487225,"threshold_uncertainty_score":0.9997507},"labels":[],"label_agreement":null},{"id":"W4385989064","doi":"10.1016/j.eswa.2023.121207","title":"A new framework for electricity price forecasting via multi-head self-attention and CNN-based techniques in the competitive electricity market","year":2023,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":95,"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":"HORIZON EUROPE Framework Programme; Ministry of Science and Higher Education of the Russian Federation; Ministry of Education and Science of the Russian Federation","keywords":"Electricity price forecasting; Electricity market; Computer science; Bidding; Electricity; Smart grid; Process (computing); Demand response; Artificial intelligence; Econometrics; Mathematical optimization; Operations research; Microeconomics; Economics","score_opus":0.017941515463548406,"score_gpt":0.2563422122477567,"score_spread":0.2384006967842083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385989064","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":[0.0053161727,0.00051268016,0.990163,0.00009495595,0.000044393786,0.0018160273,0.000009378311,0.00086080265,0.0011825404],"genre_scores_gemma":[0.8983733,0.000046289093,0.09622021,0.00005992711,0.00023768193,0.0049049794,0.00004243518,0.00005172775,0.00006342517],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989173,0.00005500937,0.0002709661,0.00026054,0.00014761627,0.00034857343],"domain_scores_gemma":[0.99889135,0.00068896596,0.00008141356,0.00022461743,0.0000498474,0.000063816944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039407035,0.0001906611,0.00019993911,0.00018624186,0.00021877412,0.00007212213,0.00016780547,0.00011897836,0.0000016293797],"category_scores_gemma":[0.00003490732,0.00014987278,0.000038274287,0.0011755602,0.000015561282,0.00007808108,0.000011119679,0.00021274014,0.0000027380695],"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":[0.00094696967,0.002571495,0.093061835,0.008384665,0.001812526,0.00008692091,0.0434515,0.06859773,0.07330702,0.24771307,0.042670462,0.4173958],"study_design_scores_gemma":[0.00056265463,0.0001178637,0.0023429578,0.0004064836,0.000021721973,0.000029982037,0.00044047413,0.96284443,0.0019245064,0.00031370923,0.030596886,0.00039833842],"about_ca_topic_score_codex":0.00019984387,"about_ca_topic_score_gemma":0.000090191206,"teacher_disagreement_score":0.8942467,"about_ca_system_score_codex":0.00009761125,"about_ca_system_score_gemma":0.000036739377,"threshold_uncertainty_score":0.61116344},"labels":[],"label_agreement":null},{"id":"W4386078675","doi":"10.1016/j.rser.2023.113645","title":"Multivariate data decomposition based deep learning approach to forecast one-day ahead significant wave height for ocean energy generation","year":2023,"lang":"en","type":"article","venue":"Renewable and Sustainable Energy Reviews","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Multivariate statistics; Benchmarking; Meteorology; Mode (computer interface); Wave height; Significant wave height; Engineering; Climatology; Environmental science; Statistics; Wind wave; Computer science; Mathematics; Geography; Geology; Oceanography","score_opus":0.05717780358551601,"score_gpt":0.2630256515318115,"score_spread":0.20584784794629551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386078675","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":[0.0016998966,0.008273647,0.98343295,0.00008608051,0.00026499812,0.00052917266,0.000032789623,0.0004869555,0.0051934887],"genre_scores_gemma":[0.8688329,0.025188902,0.056994442,0.00077674066,0.0024973406,0.0013615995,0.019688603,0.0005410062,0.024118464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99723375,0.00021150796,0.0006447515,0.0007253871,0.0001959822,0.0009886171],"domain_scores_gemma":[0.99868935,0.00016722757,0.00011869106,0.0006218711,0.00012856195,0.0002743156],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0015199198,0.0004185651,0.00063301227,0.00035183821,0.00055553776,0.00020897217,0.00030082872,0.00017689918,0.000012340284],"category_scores_gemma":[0.00018117564,0.00039901905,0.00011767353,0.00085817755,0.000023519797,0.00043687318,0.00019098543,0.0001229811,0.000003089457],"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":[0.00002301566,0.000048361067,0.000011670721,0.00072797626,0.00006256116,0.000013097767,0.0001865564,0.89534503,0.006403054,0.004920875,0.008756057,0.08350172],"study_design_scores_gemma":[0.00029014546,0.000059725608,0.000002529378,0.000090101756,0.000041795738,0.0000029492246,0.00010392811,0.5809192,0.0033537426,0.00015296997,0.41467276,0.00031012326],"about_ca_topic_score_codex":0.00085686275,"about_ca_topic_score_gemma":0.00019865885,"teacher_disagreement_score":0.9264385,"about_ca_system_score_codex":0.00013890864,"about_ca_system_score_gemma":0.000054872955,"threshold_uncertainty_score":0.99984616},"labels":[],"label_agreement":null},{"id":"W4386089656","doi":"10.20944/preprints202308.1578.v1","title":"Trends on Wind Speed Forecasting: Umbrella Review of the Last 5 Years","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Energy Load and Power Forecasting","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 Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Wind power; Work (physics); Wind speed; Mathematics; Engineering; Physics; Meteorology; Mechanical engineering; Electrical engineering","score_opus":0.17588751110035825,"score_gpt":0.32245685140575575,"score_spread":0.1465693403053975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386089656","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":[0.8740405,0.002650695,0.000012257298,0.00023666548,0.005617905,0.0004278795,0.00009283765,0.00068411214,0.116237134],"genre_scores_gemma":[0.98988193,0.004037807,0.00006261303,0.00012349089,0.00039402637,0.000014991988,0.00005957078,0.00017231144,0.0052532842],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99779475,0.00009109771,0.00070197426,0.0005671866,0.00044810548,0.00039690392],"domain_scores_gemma":[0.9979739,0.00014382372,0.0002713571,0.0014483207,0.00006399498,0.0000986138],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00067324063,0.00041310105,0.00060564815,0.00019874825,0.000051716725,0.000013389326,0.000913289,0.0002855635,0.0004711432],"category_scores_gemma":[0.0003296257,0.000363188,0.0004826187,0.0004873608,0.00007184214,0.000032816653,0.0012073439,0.001112871,0.00047291885],"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":[0.000041310275,0.0001893334,0.1764923,0.04076162,0.0012013464,0.000099119716,0.0021895096,0.7329006,0.0036928612,0.00076516217,0.011733244,0.029933563],"study_design_scores_gemma":[0.0012267071,0.000085417516,0.6020525,0.18024412,0.00070971437,0.00006772715,0.000083325656,0.020004243,0.042057425,0.0018277172,0.14858183,0.003059284],"about_ca_topic_score_codex":0.00005048047,"about_ca_topic_score_gemma":0.000018838506,"teacher_disagreement_score":0.7128964,"about_ca_system_score_codex":0.00010082484,"about_ca_system_score_gemma":0.00005223477,"threshold_uncertainty_score":0.999882},"labels":[],"label_agreement":null},{"id":"W4386127602","doi":"10.11159/icert23.001","title":"Accelerating Renewable Energy by Artificial Intelligence","year":2023,"lang":"en","type":"article","venue":"Proceedings of the World Congress on New Technologies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"Renewable energy; Computer science; Energy (signal processing); Electrical engineering; Engineering; Physics","score_opus":0.02791375821555353,"score_gpt":0.23472835242887144,"score_spread":0.2068145942133179,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386127602","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":[0.6430658,0.0026403212,0.0006178278,0.010111054,0.007783659,0.0006944571,0.000047451515,0.033751752,0.30128768],"genre_scores_gemma":[0.9918256,0.00016316366,0.0005091714,0.000025642732,0.000056113386,0.00002614541,0.0000015444749,0.00003594931,0.0073567024],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99895775,0.0000016211582,0.00028991862,0.00020679901,0.00020019505,0.0003436945],"domain_scores_gemma":[0.9995957,0.00007866567,0.000102045764,0.00015850051,0.000038374015,0.000026715159],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000105303414,0.00019088526,0.00019500744,0.00027475774,0.0001246765,0.000084644205,0.00079914706,0.000103692466,0.000009751739],"category_scores_gemma":[0.00017819497,0.00014817875,0.00006671937,0.0014455197,0.0001110798,0.00014639071,0.0002363677,0.00022357964,0.0000075942644],"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":[0.000025520552,0.000025512105,0.00064522645,0.00012088241,0.0000851339,0.0000017693154,0.00011874895,0.030867303,0.17087741,0.095956616,0.29508466,0.40619123],"study_design_scores_gemma":[0.000034588444,0.00002248403,0.0000047388066,0.00023352225,0.000006570964,0.0000011999407,0.0003536138,0.013915648,0.9322576,0.023940168,0.029062156,0.00016772251],"about_ca_topic_score_codex":0.00006351988,"about_ca_topic_score_gemma":0.00011022926,"teacher_disagreement_score":0.7613802,"about_ca_system_score_codex":0.00003202091,"about_ca_system_score_gemma":0.000009776548,"threshold_uncertainty_score":0.6042554},"labels":[],"label_agreement":null},{"id":"W4386223800","doi":"10.3390/en16176208","title":"Smart Urban Wind Power Forecasting: Integrating Weibull Distribution, Recurrent Neural Networks, and Numerical Weather Prediction","year":2023,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Numerical weather prediction; Weibull distribution; Wind speed; Wind power forecasting; Meteorology; Wind power; Computer science; Mean squared error; Renewable energy; Probabilistic forecasting; Nowcasting; Environmental science; Electric power system; Power (physics); Artificial intelligence; Engineering; Statistics; Mathematics; Geography","score_opus":0.011436513616275828,"score_gpt":0.19926503525618763,"score_spread":0.18782852163991182,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386223800","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":[0.9870509,0.0011069468,0.006151479,0.00006639879,0.0021579063,0.00007627296,0.00005190459,0.0012461735,0.0020920343],"genre_scores_gemma":[0.99883187,0.0000707871,0.00010318759,0.000013372753,0.0004026928,0.00001442542,0.00026292336,0.000044108594,0.00025664314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99896926,0.00003017729,0.0002592397,0.00021793018,0.00013684176,0.0003865302],"domain_scores_gemma":[0.9996018,0.00010363056,0.000037449856,0.00013347386,0.00003049144,0.00009316553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001492746,0.00021315852,0.0001748318,0.00006365748,0.00016577747,0.000083342886,0.000085488246,0.000094804585,0.000027775242],"category_scores_gemma":[0.000070996386,0.0001935836,0.000056660545,0.0003785979,0.00004441214,0.00016901437,0.00006233679,0.00023912753,0.0000060335997],"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":[0.000010364513,0.000010205742,0.022674434,0.000024323928,0.00004420979,0.0000133939175,0.00061587733,0.9434091,0.00014692832,0.0011043746,0.02012907,0.011817739],"study_design_scores_gemma":[0.00016350506,0.0000623482,0.0072846515,0.00008607902,0.000014319367,0.000020055424,0.00018812375,0.95897335,0.00016815658,0.00004745829,0.032779712,0.0002122596],"about_ca_topic_score_codex":0.000016267326,"about_ca_topic_score_gemma":0.000006615183,"teacher_disagreement_score":0.015564252,"about_ca_system_score_codex":0.00004326514,"about_ca_system_score_gemma":0.0000060304637,"threshold_uncertainty_score":0.789411},"labels":[],"label_agreement":null},{"id":"W4386702709","doi":"10.1109/access.2023.3314742","title":"Harmonics Forecasting of Wind and Solar Hybrid Model Based on Deep Machine Learning","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Harmonics; Adaptive neuro fuzzy inference system; Photovoltaic system; Wind power; Renewable energy; Computer science; Power electronics; Artificial neural network; Electronic engineering; Engineering; Electrical engineering; Voltage; Fuzzy logic; Artificial intelligence; Fuzzy control system","score_opus":0.03708715314706886,"score_gpt":0.2471803181440033,"score_spread":0.21009316499693442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386702709","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":[0.9593187,0.00014030687,0.036970183,0.000016626364,0.00025572523,0.000059632064,0.00001546294,0.00031368923,0.0029096538],"genre_scores_gemma":[0.9991274,0.000047162383,0.0006021528,0.000032572865,0.000058629885,0.0000039930032,0.000021821572,0.000051801828,0.000054484644],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921876,0.000015281053,0.00019628688,0.00015944305,0.00014767474,0.00026257543],"domain_scores_gemma":[0.9995978,0.00014136748,0.000046937155,0.00012741433,0.0000241984,0.00006232343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019305285,0.00014731564,0.0001724972,0.00016018828,0.000092227136,0.000053574975,0.00016817088,0.000045298802,0.000009275236],"category_scores_gemma":[0.000053640026,0.00014927295,0.000041974366,0.00023736016,0.000019834037,0.00018411205,0.000041682288,0.00023796293,0.000003994163],"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":[0.000007905281,0.000005034451,0.0020198596,0.00010674413,0.000013061121,0.000014196188,0.00009804157,0.9839198,0.0011171883,0.000012871145,0.00006129852,0.012623985],"study_design_scores_gemma":[0.00025961304,0.000026018533,0.00013825092,0.00009802789,0.000011491033,0.0000033839042,0.0000067550036,0.9762472,0.022769827,0.00012910577,0.00016061144,0.00014972776],"about_ca_topic_score_codex":0.000018657436,"about_ca_topic_score_gemma":0.000011215116,"teacher_disagreement_score":0.03980866,"about_ca_system_score_codex":0.000016042517,"about_ca_system_score_gemma":0.000010647921,"threshold_uncertainty_score":0.60871744},"labels":[],"label_agreement":null},{"id":"W4386880720","doi":"10.1017/s1748499523000192","title":"An assessment of model risk in pricing wind derivatives","year":2023,"lang":"en","type":"article","venue":"Annals of Actuarial Science","topic":"Energy Load and Power Forecasting","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 Waterloo","funders":"","keywords":"Wind speed; Econometrics; Wind power; Skewness; Economics; Derivative (finance); Financial economics; Meteorology; Engineering; Physics","score_opus":0.061154789028590686,"score_gpt":0.36304139458034973,"score_spread":0.30188660555175906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386880720","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":[0.9923311,0.000009409432,0.003690644,0.000019290774,0.00012532635,0.000037865713,0.000005726815,0.000044932596,0.0037357376],"genre_scores_gemma":[0.99733955,0.00006280972,0.002551399,0.0000073325214,0.000026742113,9.858146e-7,0.0000012465364,0.0000060345687,0.0000039154165],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991947,0.000014486063,0.0002005585,0.0001246472,0.00023812079,0.00022750694],"domain_scores_gemma":[0.99963856,0.00006501669,0.000058720583,0.00013807329,0.00004989982,0.00004971284],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00085528486,0.000065276836,0.00012579597,0.0002127753,0.000046415214,0.000015446938,0.00024771504,0.000026498436,0.000004868114],"category_scores_gemma":[0.00010967743,0.000060492246,0.000022546612,0.00095180003,0.00012017182,0.00040778422,0.000037862825,0.00007752952,5.7037073e-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":[0.0000022285046,0.000011123693,0.0033143093,0.000010431817,0.000002830975,6.760872e-7,0.001261939,0.88292366,0.10724334,0.00079442665,0.000012229196,0.0044228],"study_design_scores_gemma":[0.00007574591,0.00004619548,0.057513192,0.0000435283,0.000001250585,1.649078e-7,0.00010153751,0.87072164,0.07031871,0.0011022523,0.000009757034,0.00006605784],"about_ca_topic_score_codex":0.000107842905,"about_ca_topic_score_gemma":0.000023846675,"teacher_disagreement_score":0.054198883,"about_ca_system_score_codex":0.000010210337,"about_ca_system_score_gemma":0.0000737344,"threshold_uncertainty_score":0.24668022},"labels":[],"label_agreement":null},{"id":"W4386920288","doi":"10.1109/sas58821.2023.10254072","title":"A Novel Approach for IMU Denoising using Machine Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Carleton University","funders":"","keywords":"Computer science; Inertial measurement unit; Noise reduction; Artificial intelligence; Computer vision; Machine learning","score_opus":0.04338130285635956,"score_gpt":0.2416659157336004,"score_spread":0.19828461287724086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386920288","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":[0.15499891,0.00012924329,0.8324131,0.000006246632,0.00022103399,0.000091625465,0.000006170004,0.0013264095,0.010807261],"genre_scores_gemma":[0.8747983,0.000009817521,0.12362936,0.000018239749,0.0002121253,0.000013028217,0.00007603878,0.000082765466,0.0011603072],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993338,0.00000471453,0.00013973523,0.00013366096,0.000076871824,0.00031120647],"domain_scores_gemma":[0.9997757,0.000066105116,0.000015125757,0.00007945191,0.00001569883,0.0000479342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017338063,0.00011927234,0.00012068704,0.00011535649,0.00013419082,0.000042724565,0.00007453495,0.000054493517,0.000013362856],"category_scores_gemma":[0.0000336103,0.00011536648,0.000059979597,0.00030841227,0.000008562087,0.00008846641,0.000029944218,0.000120435034,0.000007239233],"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":[0.0000021599526,0.0000045287616,0.0003657994,0.00006982485,0.000021916014,8.228585e-7,0.00016085517,0.9156213,0.08105211,0.0004379987,0.00005705857,0.002205654],"study_design_scores_gemma":[0.00022956207,0.000009372122,0.000030212279,0.000018582065,0.000009736562,0.000012526545,0.0000814174,0.99021405,0.0061772247,0.000023292969,0.0030379128,0.00015609729],"about_ca_topic_score_codex":0.000055589106,"about_ca_topic_score_gemma":0.000009233219,"teacher_disagreement_score":0.7197994,"about_ca_system_score_codex":0.000028330202,"about_ca_system_score_gemma":0.000006254592,"threshold_uncertainty_score":0.47045085},"labels":[],"label_agreement":null},{"id":"W4386949471","doi":"10.2139/ssrn.4580673","title":"Iceberg Draft Prediction Through Robust Tree-Based Machine Learning Algorithms","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Iceberg; Computer science; Machine learning; Algorithm; Tree (set theory); Artificial intelligence; Data mining; Mathematics; Geography","score_opus":0.018775094502370828,"score_gpt":0.22399080279819206,"score_spread":0.20521570829582123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386949471","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":[0.040099055,0.017163837,0.9228652,0.0005731128,0.008690984,0.00038009055,0.000101130565,0.0036299722,0.0064966045],"genre_scores_gemma":[0.9550125,0.027224414,0.0044890996,0.00006347308,0.0050831535,0.00007449787,0.00090385566,0.0006773419,0.0064716535],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956053,0.00012050791,0.00064692117,0.00043437062,0.00048189476,0.002711019],"domain_scores_gemma":[0.99915683,0.0000939749,0.00023637053,0.00030357542,0.00008430677,0.00012493397],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0013651935,0.00056013686,0.00048281165,0.0002870357,0.0003449431,0.00018586243,0.00047701047,0.00066914706,0.00004567899],"category_scores_gemma":[0.00007131985,0.00056882034,0.00036929012,0.00030219235,0.00003748902,0.00019673069,0.00014136535,0.0145286815,0.00005266575],"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":[0.000016525033,0.00002000087,0.000776576,0.00008769139,0.00044307663,0.000021315604,0.0001900658,0.9777454,0.000089766196,0.00081788405,0.00014110758,0.019650597],"study_design_scores_gemma":[0.0009417481,0.00023755555,0.00016883221,0.00052849133,0.00019671366,0.00021436733,0.00023018788,0.969778,0.0003805965,0.017730277,0.008859308,0.0007339092],"about_ca_topic_score_codex":0.00028190645,"about_ca_topic_score_gemma":0.0016640475,"teacher_disagreement_score":0.9183761,"about_ca_system_score_codex":0.0016407806,"about_ca_system_score_gemma":0.0010382315,"threshold_uncertainty_score":0.99967635},"labels":[],"label_agreement":null},{"id":"W4387006109","doi":"10.1109/pesgm52003.2023.10252675","title":"Supervised Federated Neural Architecture Search and Its Application in Power System Forecasting","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Hitachi (Canada); Alberta Energy","funders":"","keywords":"Mean squared error; Computer science; Convergence (economics); Architecture; Baseline (sea); Artificial neural network; Machine learning; Artificial intelligence; Power (physics); Data mining; Time series; Statistics; Mathematics","score_opus":0.021009489267376495,"score_gpt":0.21819872847566013,"score_spread":0.19718923920828363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387006109","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":[0.9894777,0.00011374484,0.0013663414,0.000045532393,0.000100287856,0.00015496441,0.0000035699404,0.00080594036,0.007931914],"genre_scores_gemma":[0.9996448,0.0000052243645,0.00010538714,0.000012296517,0.00004078087,0.00002665324,0.000024234716,0.00003174058,0.00010885759],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925876,0.000021439222,0.00016571028,0.00016129261,0.000099673074,0.00029310997],"domain_scores_gemma":[0.99977803,0.00006335954,0.0000065245067,0.00007043152,0.00002027642,0.000061395665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016336236,0.00011759731,0.00011975514,0.00016507143,0.00006831445,0.000045909757,0.000060016017,0.00006626284,0.000008398016],"category_scores_gemma":[0.000012169351,0.00010799165,0.000018113402,0.0005156071,0.000005285859,0.000069455826,0.000035461,0.00016824371,0.000021217398],"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":[0.000016843334,0.000007822028,0.004688923,0.00061511033,0.000024764176,0.000058494716,0.0018646494,0.9177273,0.03914166,0.0020624644,0.00008914656,0.033702817],"study_design_scores_gemma":[0.00020810775,0.000011444027,0.0014585919,0.000057456287,0.0000014948895,0.00002456153,0.00031686944,0.9948767,0.0028322265,0.000006791033,0.00008549658,0.000120258905],"about_ca_topic_score_codex":0.00004709567,"about_ca_topic_score_gemma":0.00012741468,"teacher_disagreement_score":0.07714939,"about_ca_system_score_codex":0.000029581375,"about_ca_system_score_gemma":0.0000053241365,"threshold_uncertainty_score":0.44037715},"labels":[],"label_agreement":null},{"id":"W4387116481","doi":"10.1016/j.jgsce.2023.205133","title":"Pseudo-correlation problem and its solution for the transfer forecasting of short-term natural gas loads","year":2023,"lang":"en","type":"article","venue":"Gas Science and Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":44,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Key Research and Development Program of China; Hunan Association for Science and Technology; Natural Science Foundation of Hebei Province; China Postdoctoral Science Foundation","keywords":"Correlation; Term (time); Computer science; Correlation coefficient; Transfer (computing); Data mining; Econometrics; Machine learning; Mathematics","score_opus":0.02000942324844006,"score_gpt":0.21179426327745082,"score_spread":0.19178484002901075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387116481","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":[0.9766214,0.0006646691,0.021738544,0.000046187197,0.00043776588,0.00016619325,0.0000035694775,0.00016307917,0.00015858593],"genre_scores_gemma":[0.998926,0.00014480036,0.00080216327,0.0000028262236,0.00006952183,0.000022799053,0.0000031765337,0.000015075757,0.000013646288],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992422,0.0000019910349,0.00015296855,0.0001418936,0.00015656526,0.00030441134],"domain_scores_gemma":[0.99972886,0.00010133118,0.000008406568,0.000062962565,0.00005092968,0.000047487036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045591278,0.000105451436,0.00009493856,0.00013663592,0.0001782294,0.000045082183,0.0000932114,0.000035573266,7.825824e-7],"category_scores_gemma":[0.00005460328,0.00008450949,0.000024066454,0.00048039402,0.000048840997,0.0003291613,0.0000252179,0.000097364486,4.0838256e-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":[0.0000048674765,0.0000021974847,0.0005241458,0.00030452976,0.0000145012955,0.0000011457948,0.0014091585,0.6978706,0.24878873,0.0013664365,0.000027580601,0.049686108],"study_design_scores_gemma":[0.00009443036,0.000019446648,0.0016296016,0.00010149744,0.000011654698,0.000017442038,0.000033166256,0.9907799,0.0070964517,0.000038914917,0.00007556603,0.00010193781],"about_ca_topic_score_codex":0.0000026890439,"about_ca_topic_score_gemma":0.0000047749627,"teacher_disagreement_score":0.2929093,"about_ca_system_score_codex":0.000021182293,"about_ca_system_score_gemma":0.000011747196,"threshold_uncertainty_score":0.3446197},"labels":[],"label_agreement":null},{"id":"W4387145727","doi":"10.1007/978-3-031-42430-4_12","title":"Enhanced Energy Characterization and Feature Selection Using Explainable Non-parametric AGGMM","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Interpretability; Feature selection; Markov chain Monte Carlo; Smart meter; Data mining; Feature (linguistics); Context (archaeology); Mixture model; Machine learning; Bayesian probability; Parametric statistics; Artificial intelligence; Engineering; Smart grid; Mathematics","score_opus":0.02320888097918546,"score_gpt":0.24123973512011013,"score_spread":0.21803085414092466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387145727","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":[0.005896624,0.0004658366,0.8953898,0.00007530579,0.0009337839,0.0002786679,0.000026445772,0.00036971568,0.09656381],"genre_scores_gemma":[0.88935006,0.03948407,0.058817945,0.00042805812,0.00027993618,0.00007448569,0.0009705288,0.00010350783,0.0104914075],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992105,0.000008082992,0.00030941505,0.0001334844,0.0001684619,0.00017009619],"domain_scores_gemma":[0.9992391,0.000072430616,0.00011979779,0.00037611317,0.00013683841,0.00005574136],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026803365,0.00016467494,0.0001644948,0.0010920202,0.00032357426,0.0002646729,0.00035656177,0.00013908977,0.000002572072],"category_scores_gemma":[0.000016072516,0.00018208542,0.000018076931,0.0006862168,0.00016604397,0.002609828,0.0003033016,0.00025046043,0.0000055649557],"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":[0.0000059268345,0.000011167233,0.000062051084,0.00026848947,0.000029634844,5.6804134e-7,0.003065512,0.07132444,0.0027030723,0.123601,0.00019083971,0.7987373],"study_design_scores_gemma":[0.00010715036,0.000015756079,0.0004453346,0.00024140574,0.0000055173696,0.000011487435,0.000010303266,0.96719635,0.00031031045,0.00024850044,0.031195842,0.00021202573],"about_ca_topic_score_codex":0.000011606789,"about_ca_topic_score_gemma":0.000010459536,"teacher_disagreement_score":0.89587194,"about_ca_system_score_codex":0.00011004289,"about_ca_system_score_gemma":0.000052758,"threshold_uncertainty_score":0.7425228},"labels":[],"label_agreement":null},{"id":"W4387167356","doi":"10.1016/j.enbuild.2023.113550","title":"Consensus-based time-series clustering approach to short-term load forecasting for residential electricity demand","year":2023,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Hydro-Québec; Université du Québec à Trois-Rivières","funders":"","keywords":"Cluster analysis; Computer science; Data mining; Dynamic time warping; Benchmark (surveying); Medoid; Jaccard index; Similarity (geometry); Term (time); Machine learning; Artificial intelligence","score_opus":0.018131166515884766,"score_gpt":0.2200697856461283,"score_spread":0.20193861913024355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387167356","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":[0.77731866,0.00029860518,0.21577747,0.000099497185,0.0003864976,0.0001875119,0.000022460566,0.0010017873,0.004907513],"genre_scores_gemma":[0.98349744,0.000027216523,0.014921627,0.00008829482,0.0003489827,0.000082642524,0.000047269823,0.000072012364,0.000914543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871707,0.000017133618,0.00026429293,0.00032180158,0.00015854351,0.000521167],"domain_scores_gemma":[0.99945533,0.0001713464,0.000028621873,0.00013069791,0.000047858375,0.00016616897],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033275617,0.00023694689,0.00025132397,0.00019661542,0.0002817038,0.000103086924,0.00012673852,0.00012187835,0.0000067635074],"category_scores_gemma":[0.000110370354,0.0002459081,0.00007549125,0.00040968464,0.000037454047,0.00009753423,0.00006600502,0.00008954221,0.0000022951313],"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":[0.0003029248,0.000028419616,0.00077773526,0.00052544574,0.00014055926,0.000036136258,0.0006684402,0.86558884,0.08591426,0.0019018215,0.012237127,0.03187831],"study_design_scores_gemma":[0.00053270365,0.00012936837,0.00016629594,0.00013471101,0.000040267554,0.00005640228,0.00003351713,0.9127852,0.06537293,0.00026898435,0.01991627,0.00056338595],"about_ca_topic_score_codex":0.000035297988,"about_ca_topic_score_gemma":0.00007508545,"teacher_disagreement_score":0.20617875,"about_ca_system_score_codex":0.000056063804,"about_ca_system_score_gemma":0.000031060557,"threshold_uncertainty_score":0.99999934},"labels":[],"label_agreement":null},{"id":"W4387184748","doi":"10.20944/preprints202309.1966.v1","title":"Hybrid Model of VMD and LSTM for Next-Hour Wind Speed Forecasting in a Hot Desert Climate","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Energy Load and Power Forecasting","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":"Mean squared error; Wind power forecasting; Mean absolute percentage error; Wind power; Terrain; Wind speed; Computer science; Meteorology; Feature engineering; Deep learning; Extreme learning machine; Numerical weather prediction; Artificial intelligence; Power (physics); Artificial neural network; Electric power system; Statistics; Engineering; Mathematics; Geography","score_opus":0.21603046837062453,"score_gpt":0.31378879192674874,"score_spread":0.09775832355612421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387184748","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":[0.99435633,0.00015586965,0.0018432911,0.000027198126,0.0007068373,0.0006461558,0.00015719922,0.0003964288,0.0017106789],"genre_scores_gemma":[0.99652654,0.00032748329,0.0024318662,0.000015770886,0.00014562605,0.000058393955,0.000079815654,0.0001845877,0.00022991678],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974938,0.00003427828,0.00083683315,0.0007396238,0.00021596125,0.00067951245],"domain_scores_gemma":[0.99870896,0.0002214617,0.00021401656,0.00063677185,0.0000835052,0.00013530086],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009220305,0.00046998684,0.0007230826,0.00034590682,0.00006365791,0.00003643036,0.0004273349,0.00028297974,0.000020428486],"category_scores_gemma":[0.00030048276,0.0005443149,0.00020208693,0.00013986722,0.00005607034,0.00016093643,0.0011724039,0.00070420007,0.000022323567],"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":[0.00008374284,0.00002706085,0.06561268,0.0019121475,0.000100196055,0.000013933732,0.0009746092,0.91853184,0.011559122,0.00010346944,0.000015735759,0.0010654593],"study_design_scores_gemma":[0.0005556794,0.000014747997,0.009663974,0.0011776414,0.0000511994,0.000010191947,0.000061644314,0.9609781,0.02393494,0.002962235,0.000078718804,0.0005109574],"about_ca_topic_score_codex":0.00012165407,"about_ca_topic_score_gemma":0.00007528734,"teacher_disagreement_score":0.055948712,"about_ca_system_score_codex":0.000104683924,"about_ca_system_score_gemma":0.00006230189,"threshold_uncertainty_score":0.99970084},"labels":[],"label_agreement":null},{"id":"W4387532887","doi":"10.55766/sujst-2023-04-e01485","title":"ROBUST EXTREME RETURN LEVEL WITH POWER NORMALIZATION FOR EXTREME EVENTS: APPLICATION OF REAL HYDROLOGY DATA","year":2023,"lang":"en","type":"article","venue":"Suranaree Journal of Science and Technology","topic":"Energy Load and Power Forecasting","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":"Normalization (sociology); Multiplicative function; Extreme value theory; Linear model; Return period; Mathematics; Statistical inference; Statistics; Econometrics; Generalized linear model; Generalized extreme value distribution; Confidence interval; Applied mathematics; Geography; Mathematical analysis","score_opus":0.06956206542715755,"score_gpt":0.24790520651265863,"score_spread":0.1783431410855011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387532887","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":[0.9094181,0.00023054588,0.08888453,0.0006306959,0.00022639651,0.00011899126,0.00003171349,0.00009855566,0.00036046607],"genre_scores_gemma":[0.99646544,0.00021155195,0.003255591,0.0000065457675,0.000025053427,0.0000031467343,0.00001049511,0.000010435201,0.000011765305],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991217,0.000005865225,0.0002922589,0.00014787306,0.00021566969,0.00021663049],"domain_scores_gemma":[0.9991514,0.00004255278,0.00016558703,0.0002476915,0.00035314605,0.000039614697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008699244,0.0000807959,0.00017224504,0.0006532867,0.000098979974,0.000008154509,0.000502955,0.000087880326,0.0000023239495],"category_scores_gemma":[0.00012899743,0.000064186555,0.000012956988,0.00145566,0.0003749631,0.00041573474,0.00007370385,0.00010959041,4.2103377e-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":[0.00032091895,0.00012942482,0.19413637,0.00040259442,0.00027544016,0.00006556475,0.0013358925,0.08105832,0.46802548,0.018137807,0.0029143859,0.23319782],"study_design_scores_gemma":[0.0032135632,0.0017726169,0.02849822,0.0004410079,0.00013871359,0.0012170756,0.0021214818,0.90968484,0.032406364,0.00919147,0.010582973,0.0007316997],"about_ca_topic_score_codex":0.000010272281,"about_ca_topic_score_gemma":0.000090982816,"teacher_disagreement_score":0.8286265,"about_ca_system_score_codex":0.000024740244,"about_ca_system_score_gemma":0.00007240321,"threshold_uncertainty_score":0.26174515},"labels":[],"label_agreement":null},{"id":"W4387652219","doi":"10.1016/j.apenergy.2023.122087","title":"Short-term load forecasting based on WM algorithm and transfer learning model","year":2023,"lang":"en","type":"article","venue":"Applied Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":83,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Transfer of learning; Term (time); Reliability (semiconductor); Computer science; Nonlinear system; Algorithm; Series (stratigraphy); Selection (genetic algorithm); Transfer (computing); Domain (mathematical analysis); Time series; Similarity (geometry); Artificial intelligence; Machine learning; Mathematics; Power (physics)","score_opus":0.018503481673140428,"score_gpt":0.20116921285695538,"score_spread":0.18266573118381496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387652219","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":[0.33337238,0.00012634572,0.5355329,0.00002616437,0.00034402972,0.000104441795,0.000015871556,0.0025562765,0.1279216],"genre_scores_gemma":[0.9968971,0.000060105634,0.0023149874,0.000063276966,0.00014291749,0.000056166897,0.00006234351,0.00009708724,0.00030602014],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988378,0.000009235119,0.00020258922,0.0002819362,0.00022246699,0.00044591737],"domain_scores_gemma":[0.99961215,0.000100968435,0.000008780271,0.00014831523,0.000015054369,0.00011473731],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013886928,0.00024795308,0.00020300267,0.00014025798,0.0001426455,0.000043979737,0.000098222205,0.0001239181,0.000012087991],"category_scores_gemma":[0.0000055757732,0.0002577708,0.000053259613,0.0002855392,0.000026985921,0.00005297939,0.000024093071,0.0002220234,0.000010070407],"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":[0.0000056450526,0.000005406168,0.00001938105,0.00002123787,0.000013735399,0.00001108375,0.00013082327,0.76943505,0.0021627278,0.0016109713,0.000072114046,0.22651184],"study_design_scores_gemma":[0.00029353536,0.00002636807,0.00003831211,0.000044076096,0.000012306791,0.0000030963306,0.000026008529,0.99089944,0.0059854914,0.0002381147,0.0021395134,0.0002937286],"about_ca_topic_score_codex":0.000011339776,"about_ca_topic_score_gemma":0.000012750968,"teacher_disagreement_score":0.6635247,"about_ca_system_score_codex":0.00004614449,"about_ca_system_score_gemma":0.000015545393,"threshold_uncertainty_score":0.9999874},"labels":[],"label_agreement":null},{"id":"W4387703332","doi":"10.2174/0123520965264083230926105355","title":"Predicting Solar PV Output based on Hybrid Deep Learning and Physical Models: Case Study of Morocco","year":2023,"lang":"en","type":"article","venue":"Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering)","topic":"Energy Load and Power Forecasting","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":"Polytechnique Montréal; Université du Québec à Rimouski","funders":"","keywords":"Renewable energy; Intermittency; Photovoltaic system; Meteorology; Probabilistic forecasting; Computer science; Grid; Solar power; Numerical weather prediction; Environmental science; Power (physics); Engineering; Artificial intelligence; Mathematics; Electrical engineering; Geography","score_opus":0.009070390562322034,"score_gpt":0.232683644695446,"score_spread":0.22361325413312397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387703332","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":[0.9716886,0.010716847,0.0131638395,0.00004153661,0.0004170803,0.0013867588,0.000008803291,0.0022680245,0.00030845756],"genre_scores_gemma":[0.9630591,0.035721045,0.00010932892,0.000026626136,0.00023467396,0.0003506792,0.0000681817,0.0003909066,0.00003941452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9911611,0.00016045524,0.0014500258,0.0014059495,0.0013077057,0.004514734],"domain_scores_gemma":[0.9973378,0.0011515706,0.00024862558,0.000575445,0.00016893819,0.0005175881],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.0008283933,0.0014283002,0.0015130957,0.001945681,0.00023570986,0.000094425406,0.0005961456,0.00035398128,0.000012044942],"category_scores_gemma":[0.00053874945,0.0013751251,0.00027624558,0.0060305847,0.000043865533,0.0006435488,0.00009034991,0.0043841847,0.000013526674],"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":[0.00018657104,0.0006955188,0.0008920846,0.00014827639,0.00017858327,0.00026613948,0.00018712798,0.80037165,0.0008206874,0.00043165413,0.000008088137,0.19581364],"study_design_scores_gemma":[0.0028972386,0.0063656615,0.00013365266,0.00022621373,0.00011591428,0.00024892803,0.000056078432,0.9808168,0.0033727896,0.0002620651,0.0041806907,0.0013239672],"about_ca_topic_score_codex":0.000024431962,"about_ca_topic_score_gemma":0.00003707976,"teacher_disagreement_score":0.19448967,"about_ca_system_score_codex":0.0021943927,"about_ca_system_score_gemma":0.00019449985,"threshold_uncertainty_score":0.9998467},"labels":[],"label_agreement":null},{"id":"W4387821028","doi":"10.9734/ajeba/2023/v23i211127","title":"Electricity Consumption (kW) Forecast for a Building of Interest Based on a Time Series Nonlinear Regression Model","year":2023,"lang":"en","type":"article","venue":"Asian Journal of Economics Business and Accounting","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Energy consumption; Consumption (sociology); Electricity; Time series; Computer science; Econometrics; Energy (signal processing); HVAC; Regression analysis; Energy accounting; Environmental economics; Environmental science; Engineering; Economics; Statistics; Mathematics; Machine learning; Air conditioning","score_opus":0.03127661447383213,"score_gpt":0.22814074967820025,"score_spread":0.19686413520436813,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387821028","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":[0.9894584,0.00009196364,0.009846886,0.0001507991,0.00020280338,0.00005592885,0.000023097507,0.000027878337,0.0001422601],"genre_scores_gemma":[0.99177676,0.00013685989,0.0078058424,0.000023545486,0.0001963924,0.0000021092421,0.00001071893,0.00003367586,0.000014081127],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926925,0.000005630204,0.00041865886,0.0000945438,0.000031327487,0.0001806196],"domain_scores_gemma":[0.9994002,0.00007929193,0.0002932023,0.00006806285,0.000121110745,0.00003811901],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003395007,0.00013181688,0.0002743358,0.00029199373,0.000076206765,0.00006191724,0.00009239444,0.00006474076,0.0000054141065],"category_scores_gemma":[0.00008382263,0.00012044799,0.000071075054,0.00015304783,0.000023029394,0.00038807056,0.000018745744,0.00011514122,8.935299e-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":[0.00017878322,0.000019204483,0.001272603,0.00040070913,0.000048854505,0.000004391626,0.00012146365,0.9653287,0.008010803,0.00046200724,0.0003255905,0.023826893],"study_design_scores_gemma":[0.00046417286,0.000046288915,0.00042631518,0.0006396818,0.000019327772,0.000020743393,0.000026623558,0.9929964,0.0044378694,0.00045923528,0.00034085297,0.0001224882],"about_ca_topic_score_codex":9.680359e-7,"about_ca_topic_score_gemma":0.0000046102728,"teacher_disagreement_score":0.027667705,"about_ca_system_score_codex":0.000030051862,"about_ca_system_score_gemma":0.00003221545,"threshold_uncertainty_score":0.49117264},"labels":[],"label_agreement":null},{"id":"W4387885974","doi":"10.1109/access.2023.3327135","title":"A Novel Two-Dimensional Convolutional Neural Network-Based an Hour-Ahead Wind Speed Prediction Method","year":2023,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Saskatchewan","funders":"University of Saskatchewan","keywords":"Wind speed; Computer science; Convolutional neural network; Data pre-processing; Wind power; Speedup; Deep learning; Artificial intelligence; Preprocessor; Perceptron; Artificial neural network; Multilayer perceptron; Machine learning; Meteorology; Engineering","score_opus":0.05199561660890452,"score_gpt":0.30858365544195876,"score_spread":0.25658803883305425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387885974","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":[0.96841043,0.000057864512,0.02403584,0.00007184226,0.0052111926,0.00013031396,0.00011626356,0.0012056662,0.0007606039],"genre_scores_gemma":[0.9946055,0.0000012494918,0.0031025088,0.00018753643,0.0017021211,0.000005865452,0.00023710371,0.000057098016,0.00010101064],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99868214,0.00004507874,0.00026290683,0.00026637985,0.00029692118,0.00044656004],"domain_scores_gemma":[0.9993468,0.00020881866,0.000043919088,0.0001949673,0.000062066356,0.00014342481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003444933,0.0001990959,0.00018324684,0.00013850973,0.00015416302,0.0000932488,0.00024335943,0.000099739096,0.000085155145],"category_scores_gemma":[0.000020601366,0.0002054738,0.00007508485,0.00058100553,0.000031437878,0.00048140134,0.000035608078,0.00023814614,0.000029522085],"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":[0.000022701797,0.00002014695,0.0028125932,0.000017081213,0.000031614727,0.000011551407,0.000027544604,0.97707605,0.014638445,0.00006639046,0.0040653036,0.0012105732],"study_design_scores_gemma":[0.00066688325,0.00003357548,0.01457146,0.000055431152,0.00002104105,0.000021043734,0.0000028306922,0.9809262,0.00283707,0.00012296726,0.00053215114,0.00020933809],"about_ca_topic_score_codex":0.00009521125,"about_ca_topic_score_gemma":0.000052071515,"teacher_disagreement_score":0.026195092,"about_ca_system_score_codex":0.000051808755,"about_ca_system_score_gemma":0.000041715066,"threshold_uncertainty_score":0.83789784},"labels":[],"label_agreement":null},{"id":"W4387914281","doi":"10.1109/codit58514.2023.10284131","title":"Multi-Level Fusion of Multi-Source Information Based Deep Learning and Ensemble Deep Learning Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Université TÉLUQ","funders":"","keywords":"Deep learning; Computer science; Artificial intelligence; Ensemble learning; Convolutional neural network; Task (project management); Machine learning; Artificial neural network; Ensemble forecasting; Field (mathematics); Engineering","score_opus":0.03308779433431422,"score_gpt":0.22626636356554067,"score_spread":0.19317856923122645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387914281","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":[0.26485267,0.00011347069,0.7319483,0.0000054038114,0.000082829785,0.000064321786,0.0000012489169,0.0006591358,0.0022725922],"genre_scores_gemma":[0.9648327,0.000103339145,0.034254506,0.000013926898,0.000017284425,0.000006743652,0.00006912709,0.000031527496,0.00067080354],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991582,0.000030712443,0.0002892288,0.00010908726,0.00014924238,0.00026351315],"domain_scores_gemma":[0.99961066,0.00012356119,0.00006160874,0.00007880482,0.00005311981,0.00007221528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025187663,0.00015268015,0.00016469561,0.00022934309,0.00013711733,0.000037032238,0.000064669104,0.00010549442,0.00003186763],"category_scores_gemma":[0.0000991823,0.000152046,0.000044266155,0.00029203043,0.000017194838,0.00043204843,0.000056597168,0.0002462992,0.000034936398],"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":[0.00000445105,0.0000048186503,0.0009632972,0.00009923034,0.000009660462,7.605471e-7,0.0017094441,0.88485545,0.009303652,0.000028482185,0.0000062447775,0.10301452],"study_design_scores_gemma":[0.0006425111,0.000025493042,0.0009586328,0.00006697132,0.000007756641,0.0000021296514,0.0007589268,0.99092096,0.0044845818,0.000005253682,0.0019498015,0.0001770105],"about_ca_topic_score_codex":0.00006026032,"about_ca_topic_score_gemma":0.00006680789,"teacher_disagreement_score":0.6999801,"about_ca_system_score_codex":0.000020367512,"about_ca_system_score_gemma":0.0000058672135,"threshold_uncertainty_score":0.62002563},"labels":[],"label_agreement":null},{"id":"W4387951140","doi":"10.1109/ccece58730.2023.10288848","title":"A Hour-Ahead Wind Speed Forecasting Using One-Dimensional Convolutional Neural Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wind speed; Convolutional neural network; Data pre-processing; Computer science; Wind power; Artificial neural network; Meteorology; Preprocessor; Wind direction; Deep learning; Speedup; Artificial intelligence; Engineering","score_opus":0.06469325440995186,"score_gpt":0.24014528060695228,"score_spread":0.17545202619700043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387951140","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":[0.98875564,0.00014599053,0.0005909139,0.000047392026,0.0018455285,0.00008652485,0.000010315266,0.0010285166,0.007489153],"genre_scores_gemma":[0.99383706,0.0000028182872,0.004176621,0.00009279982,0.0011700833,9.215504e-7,0.00005270859,0.00006170519,0.0006052756],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985515,0.000021861651,0.00029320872,0.00021261352,0.0002694564,0.00065136066],"domain_scores_gemma":[0.9994538,0.00021043576,0.00003381752,0.00013099097,0.000042640215,0.00012833255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022961861,0.00020503174,0.00020424844,0.00011271826,0.00021615994,0.000045398796,0.00010713266,0.00009695773,0.0002735534],"category_scores_gemma":[0.000041499014,0.00021268765,0.00009387057,0.0005919534,0.00003911537,0.00017295891,0.000080110076,0.0002205213,0.00009240196],"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":[0.0000066728294,0.000005408325,0.0020887929,0.000019026302,0.000042471747,0.000026271373,0.000044609045,0.9900347,0.0027913207,0.00053332315,0.0035499926,0.0008574221],"study_design_scores_gemma":[0.00022549061,0.000016528355,0.0017648841,0.00011048586,0.00001536709,0.00006413405,0.00001830335,0.9960193,0.00028231123,0.00029896444,0.00092371355,0.00026050245],"about_ca_topic_score_codex":0.000041338757,"about_ca_topic_score_gemma":0.000023156776,"teacher_disagreement_score":0.0068838773,"about_ca_system_score_codex":0.000061745006,"about_ca_system_score_gemma":0.000028151215,"threshold_uncertainty_score":0.86731505},"labels":[],"label_agreement":null},{"id":"W4387951217","doi":"10.1109/ccece58730.2023.10288790","title":"Wind Speed Forecasting using ARMA and Boosted Regression Tree Methods: A Case Study","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Wind speed; Autoregressive–moving-average model; Wind power; Moving average; Computer science; Time horizon; Wind power forecasting; Regression analysis; Probabilistic forecasting; Meteorology; Term (time); Autoregressive model; Econometrics; Electric power system; Power (physics); Machine learning; Artificial intelligence; Engineering; Mathematical optimization; Mathematics; Geography","score_opus":0.10656564137312069,"score_gpt":0.34075264823788903,"score_spread":0.23418700686476834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387951217","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":[0.99291325,0.00012664522,0.0018950662,0.000007727692,0.0004459586,0.00018295292,0.0000017878249,0.0007310816,0.0036955401],"genre_scores_gemma":[0.9835527,0.000006487692,0.01590352,0.000011357185,0.00011855298,0.0000013886659,0.0000033953315,0.00005597999,0.0003466077],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989696,0.000082842715,0.0002697721,0.00022631662,0.000116260184,0.00033517924],"domain_scores_gemma":[0.9994036,0.0002555869,0.00003544739,0.0001706622,0.000023753306,0.00011096473],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005267277,0.00020376866,0.00022581144,0.0002147492,0.00018204348,0.000065648994,0.000060596354,0.00009421193,0.000027740916],"category_scores_gemma":[0.000083318904,0.0001694518,0.000038644226,0.0005444099,0.000018069622,0.00016367825,0.00009762511,0.00022274707,0.0000048288684],"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":[0.000043491058,0.000103977756,0.021336786,0.00033448433,0.00037534235,0.020390494,0.01905775,0.2899594,0.07639193,0.000041197156,0.0010678092,0.57089734],"study_design_scores_gemma":[0.0005867063,0.000057839687,0.00026696155,0.00010601606,0.000042718253,0.0021839717,0.007380295,0.9857177,0.0031436754,0.000031442356,0.00022912453,0.00025354463],"about_ca_topic_score_codex":0.0002212081,"about_ca_topic_score_gemma":0.0001349721,"teacher_disagreement_score":0.6957583,"about_ca_system_score_codex":0.000027422242,"about_ca_system_score_gemma":0.0000076714505,"threshold_uncertainty_score":0.69100434},"labels":[],"label_agreement":null},{"id":"W4388042065","doi":"10.3390/atmos14111635","title":"Machine Learning Dynamic Ensemble Methods for Solar Irradiance and Wind Speed Predictions","year":2023,"lang":"en","type":"article","venue":"Atmosphere","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakes Environmental (Canada); University of Guelph","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Solar irradiance; Wind speed; Mean squared error; Mean absolute percentage error; Computer science; Ensemble forecasting; Irradiance; Ensemble learning; Random forest; Metric (unit); Meteorology; Support vector machine; Performance metric; Artificial neural network; Environmental science; Machine learning; Mathematics; Statistics; Engineering; Geography","score_opus":0.0134850626987397,"score_gpt":0.2668117107798162,"score_spread":0.25332664808107647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388042065","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":[0.6093789,0.010567721,0.35497472,0.0002534925,0.002750272,0.000488847,0.00005746764,0.0033475978,0.018181032],"genre_scores_gemma":[0.9163586,0.00069609366,0.075639345,0.000024992436,0.00013468464,0.000010358251,0.00007154648,0.000097298616,0.0069671166],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938846,0.000023729548,0.0001262116,0.00015299316,0.000044162443,0.00026446758],"domain_scores_gemma":[0.99961275,0.00017952474,0.000018939916,0.00010286292,0.000014701475,0.00007123297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019086286,0.000121777906,0.000134638,0.0000063089083,0.00016655448,0.000031842552,0.000060564613,0.00007339864,0.000034663666],"category_scores_gemma":[0.000060929422,0.00012834495,0.000043600983,0.0002249642,0.000018176337,0.000093243674,0.000022578617,0.0001656021,0.000018176723],"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":[0.000005618053,0.0000040539794,0.0019299428,0.000111152396,0.00007142724,0.0000045076463,0.00046476524,0.8644015,0.005106533,0.00022779191,0.0007523975,0.12692031],"study_design_scores_gemma":[0.00022159117,0.000033046028,0.0010861907,0.000036965903,0.000018092587,0.000009214703,0.00007408299,0.9251449,0.00028320626,0.00038852025,0.07257312,0.00013108375],"about_ca_topic_score_codex":0.000025612813,"about_ca_topic_score_gemma":0.00004316232,"teacher_disagreement_score":0.30697972,"about_ca_system_score_codex":0.000023708913,"about_ca_system_score_gemma":0.000007841116,"threshold_uncertainty_score":0.5233755},"labels":[],"label_agreement":null},{"id":"W4388068048","doi":"10.6036/10882","title":"IMPACT OF THE IMPLEMENTATION OF THE NEW QUARTER-HOURLY MODEL ON A WIND FARM IN THE PENINSULAR ELECTRICITY SYSTEM","year":2023,"lang":"en","type":"article","venue":"DYNA","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Quarter (Canadian coin); Electricity; Environmental economics; Wind power; Competition (biology); Electricity market; Transparency (behavior); Business; Demand response; Industrial organization; Economics; Computer science; Engineering; Computer security","score_opus":0.011628865644052744,"score_gpt":0.25381171834310606,"score_spread":0.24218285269905332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388068048","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":[0.9982242,0.000020566053,0.00027230295,0.00004306211,0.000082917926,0.00014245302,0.00002327658,0.000032901597,0.0011583079],"genre_scores_gemma":[0.99991524,0.0000023346638,0.000008223657,0.000007988514,0.000028408407,0.0000034279249,0.0000037901696,0.00001103474,0.000019526282],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994182,0.000043861935,0.00017305133,0.000060450522,0.00016132124,0.00014308834],"domain_scores_gemma":[0.9996658,0.000046977973,0.000052725707,0.00021131184,0.000010756871,0.000012427352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018139649,0.000076657474,0.00009645748,0.000047038764,0.00003470128,0.000009218486,0.00021741063,0.000028064544,0.000002902481],"category_scores_gemma":[0.0000075195467,0.000038157363,0.00009791673,0.00049233093,0.000007523754,0.000025945485,0.000015174533,0.00010919328,0.0000014780809],"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":[0.000006970893,0.000009616632,0.011098166,0.00005040777,0.00003595089,7.0300325e-7,0.0058427867,0.9712818,0.007113791,0.0016508353,0.000447895,0.00246105],"study_design_scores_gemma":[0.0003895766,0.00007905439,0.15907189,0.00009303426,0.00001853141,0.0000029304126,0.0011030193,0.83488363,0.0041096853,0.00014674845,0.00002217333,0.000079754456],"about_ca_topic_score_codex":0.0008762742,"about_ca_topic_score_gemma":0.0003908377,"teacher_disagreement_score":0.14797372,"about_ca_system_score_codex":0.000061560546,"about_ca_system_score_gemma":0.00003950928,"threshold_uncertainty_score":0.1556012},"labels":[],"label_agreement":null},{"id":"W4388326987","doi":"10.1016/j.renene.2023.119547","title":"Development of an integrated model on the basis of GCMs-RF-FA for predicting wind energy resources under climate change impact: A case study of Jing-Jin-Ji region in China","year":2023,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Environmental science; Wind power; Renewable energy; Wind speed; Climate change; Climatology; Meteorology; Precipitation; Atmospheric sciences; Geography; Engineering","score_opus":0.04452876954075573,"score_gpt":0.25561743399669956,"score_spread":0.21108866445594382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388326987","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":[0.996525,0.000086447806,0.0025308798,0.000008728989,0.00008842913,0.00014112185,0.000027063345,0.00010697821,0.0004853547],"genre_scores_gemma":[0.99911976,0.00006741584,0.00048265688,0.000009710722,0.00005636142,0.00008927153,0.000038983027,0.00007085937,0.00006500932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833924,0.00007138189,0.0006603026,0.00026379692,0.00023974282,0.00042555286],"domain_scores_gemma":[0.99910307,0.00019397013,0.00021702406,0.00034689996,0.00006579663,0.00007325929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005062686,0.00026688492,0.000426337,0.0004481638,0.00011321663,0.0000145646545,0.00021654679,0.00012057024,0.0000041271624],"category_scores_gemma":[0.000041239007,0.00020363182,0.00008668258,0.00087192986,0.000028478395,0.00012658695,0.00007877798,0.00009111462,6.890903e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000843531,0.00015272242,0.000606353,0.00009750138,0.000096587326,0.00003158113,0.010599694,0.9781012,0.0037444732,0.00016139811,0.00001709554,0.00630702],"study_design_scores_gemma":[0.00074090064,0.00035007528,0.0004262339,0.00050087343,0.000029777251,0.000022940458,0.008849644,0.9499902,0.038662866,0.000109635715,0.00009924542,0.00021760687],"about_ca_topic_score_codex":0.019159682,"about_ca_topic_score_gemma":0.021821853,"teacher_disagreement_score":0.03491839,"about_ca_system_score_codex":0.00010064733,"about_ca_system_score_gemma":0.00005082634,"threshold_uncertainty_score":0.99602735},"labels":[],"label_agreement":null},{"id":"W4388440842","doi":"10.18280/isi.280528","title":"An Advanced Hybrid Meta-Heuristic Model for Solar Power Generation Forecasting via Ensemble Deep Learning","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"Computer science; Heuristic; Artificial intelligence; Deep learning; Ensemble learning; Meta heuristic; Machine learning; Power (physics); Algorithm","score_opus":0.031790139783905526,"score_gpt":0.23123280384311504,"score_spread":0.19944266405920952,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388440842","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":[0.2275304,0.00015273038,0.7691527,0.0000034201444,0.0003953452,0.0002511457,0.000021112042,0.0009845509,0.001508598],"genre_scores_gemma":[0.982815,0.000028866465,0.015697079,0.000030312356,0.000114845774,0.00024756015,0.00092582084,0.00006228732,0.00007817626],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984655,0.000032850836,0.00062446843,0.00016264127,0.00021126216,0.00050328724],"domain_scores_gemma":[0.99923486,0.0000928602,0.000166082,0.00020057244,0.00019880859,0.00010682422],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00054035283,0.00027775887,0.00031011621,0.00030396358,0.0004741082,0.00022899825,0.00014770817,0.00009955362,0.000015880343],"category_scores_gemma":[0.00024349804,0.00028624322,0.00014484441,0.00032956718,0.000027432263,0.0030549872,0.000026774122,0.00017785748,0.00004075545],"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":[0.0000103940565,0.000003721236,0.000016994692,0.00015568007,0.000102813385,0.0000013748569,0.0031904427,0.9134948,0.004549,0.0004700649,0.00009642765,0.07790825],"study_design_scores_gemma":[0.00027875538,0.00008750111,0.000019168094,0.00003545125,0.00010103518,0.000022021532,0.0002234671,0.99015826,0.0062186797,0.0014215702,0.0011082086,0.0003258888],"about_ca_topic_score_codex":0.0000066048133,"about_ca_topic_score_gemma":0.000013893413,"teacher_disagreement_score":0.75528467,"about_ca_system_score_codex":0.00014446242,"about_ca_system_score_gemma":0.000022338927,"threshold_uncertainty_score":0.999959},"labels":[],"label_agreement":null},{"id":"W4388856344","doi":"10.1109/naps58826.2023.10318672","title":"Time Series Aggregation in Power System Studies in the Presence of Wind Energy: A Matrix-Profile Perspective","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Calgary","funders":"","keywords":"Wind power; Robustness (evolution); Electric power system; Computer science; Data aggregator; Renewable energy; Wind power forecasting; Consistency (knowledge bases); Electricity; Reliability (semiconductor); Reliability engineering; Operations research; Power (physics); Industrial engineering; Data mining; Engineering; Artificial intelligence; Wireless sensor network; Electrical engineering","score_opus":0.015802860688099392,"score_gpt":0.25149974368347305,"score_spread":0.23569688299537367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388856344","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":[0.94421256,0.001989402,0.000029748444,0.00009876151,0.0002489297,0.00012590594,0.000008793133,0.00026422582,0.053021703],"genre_scores_gemma":[0.9984504,0.00007755396,0.00009415743,0.0000032641929,0.000024655503,0.000018928484,0.00000292113,0.000011141237,0.0013169827],"study_design_codex":"simulation_or_modeling","study_design_gemma":"qualitative","domain_scores_codex":[0.9994241,0.000042902793,0.00017161835,0.00009654674,0.00011873278,0.00014608949],"domain_scores_gemma":[0.9996668,0.00015204464,0.000022627042,0.00011185798,0.000037257054,0.000009422999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020697675,0.00008539524,0.0001409708,0.0001409267,0.000021464106,0.00000920447,0.0000989771,0.000035638255,0.000016583164],"category_scores_gemma":[0.0000574088,0.000060990344,0.000023929084,0.0006145888,0.000042608724,0.00013878614,0.000029895124,0.0000626919,0.000017327628],"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":[0.00007242852,0.000052107887,0.0034946646,0.0009300082,0.00023873217,0.000216052,0.18006153,0.5764605,0.0063767373,0.22424662,0.006820515,0.0010300993],"study_design_scores_gemma":[0.0011871192,0.00024962673,0.008176903,0.0027976371,0.00003079609,0.00007011653,0.5560228,0.39542416,0.030956306,0.0023699529,0.0018924562,0.0008220653],"about_ca_topic_score_codex":0.00015462401,"about_ca_topic_score_gemma":0.00018073672,"teacher_disagreement_score":0.3759613,"about_ca_system_score_codex":0.00006992991,"about_ca_system_score_gemma":0.00000815496,"threshold_uncertainty_score":0.24871139},"labels":[],"label_agreement":null},{"id":"W4389112484","doi":"10.1016/j.seta.2023.103563","title":"Personalized PV system recommendation for enhanced solar energy harvesting using deep learning and collaborative filtering","year":2023,"lang":"en","type":"article","venue":"Sustainable Energy Technologies and Assessments","topic":"Energy Load and Power Forecasting","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":"Royal Military College of Canada","funders":"","keywords":"Renewable energy; Computer science; Photovoltaic system; Energy consumption; Population; Electricity; Machine learning; Artificial intelligence; Simulation; Industrial engineering; Engineering; Electrical engineering","score_opus":0.011697055190603205,"score_gpt":0.262686598085402,"score_spread":0.2509895428947988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389112484","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":[0.19493559,0.0011085072,0.79645985,0.000049677037,0.00027806842,0.00014203295,0.0000114657005,0.003286981,0.003727822],"genre_scores_gemma":[0.9888974,0.00092280586,0.00874357,0.000005648936,0.0000375755,0.0001590975,0.00007190277,0.000054359192,0.0011076862],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998836,0.000028141547,0.00022555616,0.00029135836,0.00008765062,0.0005313263],"domain_scores_gemma":[0.9995354,0.00014097041,0.00009251854,0.000087049964,0.000103600905,0.000040472507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021756395,0.00021733798,0.00023356559,0.00026229493,0.0005527776,0.00020255847,0.000089420086,0.00016428466,0.0000034819316],"category_scores_gemma":[0.0001001707,0.0002336791,0.000030583855,0.00057040295,0.000060085265,0.00036910264,0.00018631556,0.00012399389,1.2015691e-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":[0.000050271945,0.000014900851,0.0004236894,0.0016250357,0.00030355222,0.00005914727,0.00050614687,0.07511348,0.032654073,0.12995742,0.00010283615,0.7591894],"study_design_scores_gemma":[0.0007386165,0.00014877085,0.000012750814,0.00025559144,0.00003713196,0.000012557727,0.08434973,0.8190061,0.049469218,0.0012872219,0.044223826,0.00045849377],"about_ca_topic_score_codex":0.00010312556,"about_ca_topic_score_gemma":0.000017889039,"teacher_disagreement_score":0.79396176,"about_ca_system_score_codex":0.00014299412,"about_ca_system_score_gemma":0.000026186972,"threshold_uncertainty_score":0.95291567},"labels":[],"label_agreement":null},{"id":"W4389192978","doi":"10.22215/etd/2023-15754","title":"Federated and Multi-Task Learning for Privacy-Preserving Short-Term Electric Load Forecasting","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Energy Load and Power Forecasting","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":"Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Hyperparameter; Electrical load; Machine learning; Transformer; Task (project management); Real-time computing; Engineering","score_opus":0.03190699710173741,"score_gpt":0.26212789107626894,"score_spread":0.23022089397453152,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389192978","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":[0.9757549,0.00176144,0.007949898,0.0000053369668,0.0013688889,0.0005900272,0.000008011499,0.0020894725,0.010472012],"genre_scores_gemma":[0.97648287,0.00035173696,0.0022711423,0.000006848095,0.00030350263,0.0001898158,0.0014274794,0.00038486265,0.018581757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980757,0.000023488976,0.0005032052,0.0004810552,0.00023063122,0.0006858791],"domain_scores_gemma":[0.9991663,0.00032916188,0.000063271684,0.00014881212,0.00016516773,0.000127247],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002763445,0.0005062792,0.0004715894,0.00028617718,0.00040235757,0.00028043316,0.00021417264,0.00042932885,0.000024832652],"category_scores_gemma":[0.0005264118,0.00053374463,0.00013082198,0.0004061873,0.0000068726367,0.00017827885,0.00006144884,0.0006353849,0.000009503531],"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":[0.00028937726,0.000090534006,0.01044826,0.012569591,0.0017000168,0.00014005014,0.011092722,0.12746392,0.23763932,0.0001045937,0.005684234,0.5927774],"study_design_scores_gemma":[0.00042849136,0.00006461133,0.0015258102,0.0006510846,0.00008363676,0.000010588522,0.00047261643,0.98622864,0.008887786,0.000025232628,0.0009013296,0.000720188],"about_ca_topic_score_codex":0.00006981381,"about_ca_topic_score_gemma":0.0010091993,"teacher_disagreement_score":0.8587647,"about_ca_system_score_codex":0.00011993376,"about_ca_system_score_gemma":0.00006438948,"threshold_uncertainty_score":0.9997114},"labels":[],"label_agreement":null},{"id":"W4389311454","doi":"10.1016/j.renene.2023.119773","title":"Short-term wave power forecasting with hybrid multivariate variational mode decomposition model integrated with cascaded feedforward neural networks","year":2023,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"Department of Environment and Science, Queensland Government; Queensland Government","keywords":"Artificial neural network; Feed forward; Extreme learning machine; Feedforward neural network; Benchmark (surveying); Benchmarking; Mode (computer interface); Computer science; Renewable energy; Term (time); Artificial intelligence; Engineering; Control engineering","score_opus":0.018200331367084863,"score_gpt":0.2292037474475976,"score_spread":0.21100341608051276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389311454","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":[0.26905552,0.000054166318,0.7259585,0.00001766786,0.00029962545,0.0000859246,0.000040123035,0.0009297392,0.0035587116],"genre_scores_gemma":[0.99105835,0.000021258713,0.006956025,0.000040322782,0.00018657785,0.00007047118,0.00094727636,0.00015673289,0.0005629605],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809587,0.000039490078,0.0003690011,0.00042533618,0.00032411123,0.0007461988],"domain_scores_gemma":[0.999242,0.00010636069,0.00007267322,0.0002669491,0.00011930491,0.00019271339],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012333083,0.00043998082,0.00035318275,0.0002464439,0.0002645428,0.0001225535,0.00016563365,0.00013921355,0.000021443051],"category_scores_gemma":[0.0000092898,0.0003602893,0.000080989645,0.0006732276,0.000042565858,0.00038161332,0.000056105026,0.00024772304,0.0000017084482],"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":[0.00011695364,0.000017961998,0.0001713047,0.000017072962,0.00015460521,0.00012561664,0.00012653442,0.9944346,0.0020870124,0.00030397944,0.00023091957,0.0022134432],"study_design_scores_gemma":[0.0006191878,0.000091763184,0.00017303776,0.00016730266,0.000048141148,0.00018300703,0.000028462566,0.994001,0.0039416663,0.00016717387,0.000069161884,0.0005100974],"about_ca_topic_score_codex":0.0012010818,"about_ca_topic_score_gemma":0.00076072285,"teacher_disagreement_score":0.72200286,"about_ca_system_score_codex":0.00015554312,"about_ca_system_score_gemma":0.000047691643,"threshold_uncertainty_score":0.9998849},"labels":[],"label_agreement":null},{"id":"W4389615176","doi":"10.3390/su152416759","title":"A Hybrid Model of Variational Mode Decomposition and Long Short-Term Memory for Next-Hour Wind Speed Forecasting in a Hot Desert Climate","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"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":"Mean absolute percentage error; Mean squared error; Wind speed; Wind power; Meteorology; Terrain; Wind power forecasting; Numerical weather prediction; Feature engineering; Computer science; Mode (computer interface); Deep learning; Power (physics); Environmental science; Statistics; Electric power system; Mathematics; Artificial intelligence; Engineering; Geography","score_opus":0.02797354063029972,"score_gpt":0.28823470450245925,"score_spread":0.26026116387215953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389615176","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":[0.98271054,0.000033080683,0.016431706,0.000029123601,0.00008876967,0.00041437414,0.000057451547,0.00013966531,0.0000953165],"genre_scores_gemma":[0.99820286,0.000008924675,0.001583703,0.000004950117,0.00004584125,0.00002198706,0.00008520648,0.000031573014,0.000014969337],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988416,0.000027539025,0.00036205738,0.00023769212,0.00012122886,0.0004098951],"domain_scores_gemma":[0.99937284,0.00021076953,0.000037674316,0.0001506911,0.00016491002,0.00006309533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059461244,0.00015317077,0.00022873355,0.00017100469,0.00007884728,0.00003064918,0.00008222382,0.0000639261,0.0000026817809],"category_scores_gemma":[0.00019619944,0.000170493,0.00006669755,0.00018947503,0.000037965863,0.00028640413,0.00006578841,0.00011427123,2.1055904e-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":[0.00010977441,0.000026148562,0.020595185,0.0010126783,0.000015489763,0.000009130751,0.00095597573,0.9710345,0.0015267965,0.00037059016,0.000008556744,0.004335121],"study_design_scores_gemma":[0.00036286257,0.000030091745,0.0228823,0.00005774036,0.0000134176835,0.000006064774,0.0001760362,0.96558666,0.0013220845,0.009407578,0.0000010071545,0.00015413394],"about_ca_topic_score_codex":0.000034205277,"about_ca_topic_score_gemma":0.00006879938,"teacher_disagreement_score":0.015492333,"about_ca_system_score_codex":0.00027287877,"about_ca_system_score_gemma":0.00007865002,"threshold_uncertainty_score":0.6952503},"labels":[],"label_agreement":null},{"id":"W4389841718","doi":"10.1016/j.egyr.2023.12.031","title":"Unveiling the backbone of the renewable energy forecasting process: Exploring direct and indirect methods and their applications","year":2023,"lang":"en","type":"article","venue":"Energy Reports","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"United Nations University Institute for Water, Environment, and Health","funders":"Belgian Federal Science Policy Office","keywords":"Renewable energy; Process (computing); Computer science; Industrial engineering; Artificial intelligence; Data science; Machine learning; Engineering","score_opus":0.03750939064578147,"score_gpt":0.24962827049297556,"score_spread":0.2121188798471941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389841718","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":[0.85983473,0.0151979,0.03871141,0.00013687632,0.0016602047,0.00035201895,0.000013316598,0.0014989012,0.08259463],"genre_scores_gemma":[0.9974707,0.0004906322,0.0008683681,0.000019018691,0.00015694377,0.00027741172,0.0000081005055,0.000055142766,0.0006537044],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99880904,0.000071589966,0.00041561844,0.00028117374,0.00012858365,0.00029396918],"domain_scores_gemma":[0.99893934,0.0004011917,0.0001629128,0.00039128537,0.000042380612,0.00006287401],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007139556,0.00020528841,0.0002619677,0.00010006947,0.00030247864,0.000040330502,0.00012264503,0.00006865319,0.0000049153864],"category_scores_gemma":[0.00011402069,0.00012676627,0.00006466837,0.00082604965,0.00008621956,0.00013446837,0.00013141433,0.00010577837,1.0439189e-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":[0.000008107457,0.000021289326,0.003340311,0.00031160403,0.00029713486,0.000032752534,0.0043567074,0.6342322,0.031666975,0.00094966387,0.0004839593,0.3242993],"study_design_scores_gemma":[0.00017337351,0.000032287557,0.00074026926,0.0004390808,0.000071933915,0.00050532393,0.0013347556,0.16206221,0.708106,0.0068163862,0.119151056,0.00056731154],"about_ca_topic_score_codex":0.0002713508,"about_ca_topic_score_gemma":0.00025378243,"teacher_disagreement_score":0.67643905,"about_ca_system_score_codex":0.00001615456,"about_ca_system_score_gemma":0.000026377253,"threshold_uncertainty_score":0.51693785},"labels":[],"label_agreement":null},{"id":"W4389888459","doi":"10.1109/cgee59468.2023.10351847","title":"An Efficient Approach for Short-Term Load Forecasting Using the Regression Learner Application","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Term (time); Computer science; Regression; Electricity; Regression analysis; Demand forecasting; Energy (signal processing); Mains electricity; Machine learning; Operations research; Engineering; Statistics","score_opus":0.05499122157662889,"score_gpt":0.2808110789451546,"score_spread":0.22581985736852572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389888459","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":[0.47536644,0.000047583897,0.5189634,0.000007975808,0.0001529458,0.00025077196,0.0000029515652,0.00058727036,0.0046206564],"genre_scores_gemma":[0.98867404,0.0000040371488,0.010723518,0.000011421743,0.00025434926,0.00008430774,0.00006553645,0.00005000702,0.00013275554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991267,0.000014825493,0.00018073252,0.00020766578,0.00016263338,0.00030746666],"domain_scores_gemma":[0.9995543,0.00006872492,0.000022800597,0.00025968745,0.000040353632,0.000054148157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039274813,0.00014017968,0.00010644713,0.000058257967,0.00025215716,0.000056025394,0.00016537074,0.000071657516,0.00000464822],"category_scores_gemma":[0.000017187815,0.000091479546,0.00005808573,0.00031098063,0.000021219548,0.00007122622,0.000030999334,0.00010650168,0.0000037516647],"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":[0.000004231385,0.000009752373,0.00029382663,0.00004555148,0.000008531149,3.9839816e-7,0.00036356645,0.95179605,0.018168656,0.00033061698,0.00011966766,0.028859166],"study_design_scores_gemma":[0.000089504414,0.00001294118,0.00009517902,0.000026020298,0.000011557645,0.0000068697695,0.0002525162,0.9938598,0.0048267613,0.000022453072,0.00065941626,0.00013697517],"about_ca_topic_score_codex":0.000011715648,"about_ca_topic_score_gemma":0.0000055619175,"teacher_disagreement_score":0.51330763,"about_ca_system_score_codex":0.000056600933,"about_ca_system_score_gemma":0.000011602484,"threshold_uncertainty_score":0.37304276},"labels":[],"label_agreement":null},{"id":"W4389894955","doi":"10.5194/wes-2023-157","title":"Data-driven surrogate model for wind turbine damage equivalent load","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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 Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Aeroelasticity; Turbine; Wind power; Wake; Computer science; Range (aeronautics); Surrogate model; Component (thermodynamics); Set (abstract data type); Engineering; Simulation; Marine engineering; Aerodynamics; Aerospace engineering; Machine learning","score_opus":0.12523226052166722,"score_gpt":0.30101366719745215,"score_spread":0.17578140667578493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389894955","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":[0.100566536,0.0009774998,0.8278221,0.0004929815,0.009500845,0.0017017449,0.012981504,0.0071004326,0.038856346],"genre_scores_gemma":[0.88024247,0.00070182164,0.06342507,0.00011591435,0.0016888768,0.00016038676,0.012790277,0.0006733992,0.040201765],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99806845,0.000012405921,0.00044957857,0.0006514382,0.0002828477,0.0005352783],"domain_scores_gemma":[0.99816763,0.00012186025,0.00006739524,0.0014234639,0.00008254862,0.00013709873],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003710488,0.00043664983,0.00045153114,0.00011297714,0.00006182264,0.000111778565,0.0010595772,0.0003173893,0.000061658844],"category_scores_gemma":[0.00006580037,0.0004301787,0.00015389685,0.000089970774,0.000026329355,0.00014420068,0.0017732761,0.0005164169,0.000079262194],"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":[0.000006381107,0.000010366307,0.000019258272,0.00059945317,0.00015070495,0.00000849839,0.00016016576,0.97739995,0.00020495846,0.0003818999,0.02035858,0.00069981033],"study_design_scores_gemma":[0.00030426518,0.000010322507,0.000021030088,0.00025519333,0.00007163259,0.0000011876225,0.000013343843,0.99057937,0.000225405,0.0012690253,0.006769271,0.00047995237],"about_ca_topic_score_codex":0.00007349555,"about_ca_topic_score_gemma":0.00052140356,"teacher_disagreement_score":0.77967596,"about_ca_system_score_codex":0.00013454976,"about_ca_system_score_gemma":0.00011366068,"threshold_uncertainty_score":0.999815},"labels":[],"label_agreement":null},{"id":"W4390189488","doi":"10.1109/pvsc48320.2023.10359788","title":"Using Neural Network Decomposition to Estimate Field Photovoltaic Performance Loss Rate","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Morgan Solar (Canada)","funders":"Solar Energy Technologies Office","keywords":"Computer science; Photovoltaic system; Artificial neural network; Field (mathematics); Estimation; Data mining; Decomposition; Artificial intelligence; Engineering","score_opus":0.02188451552013747,"score_gpt":0.28188707390165657,"score_spread":0.2600025583815191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390189488","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":[0.9870479,0.000018330176,0.0074105896,0.000026858119,0.00080537953,0.00005766671,9.780855e-7,0.00073783676,0.0038944292],"genre_scores_gemma":[0.9950516,0.000010308435,0.004298161,0.00017942632,0.00022062725,0.0000068570257,0.00001127538,0.000026830267,0.00019488027],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993502,0.0000083067,0.00013439849,0.00010872223,0.000059863036,0.00033852344],"domain_scores_gemma":[0.9997486,0.000053508284,0.00001027349,0.000103239974,0.000012923691,0.000071454066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000102662016,0.00011073691,0.00009615954,0.000060711172,0.000105073996,0.00003888478,0.00007392801,0.000041088864,0.000058830392],"category_scores_gemma":[0.000006444332,0.0001094958,0.000029507259,0.00040767773,0.0000047463227,0.00012726853,0.000035781803,0.00008857344,0.00010712427],"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":[0.0000055303367,0.000001080512,0.002531111,0.00002518088,0.0000065226686,0.000010477015,0.00003970344,0.9812174,0.012694686,0.000030796222,0.0013533747,0.002084166],"study_design_scores_gemma":[0.00006993388,0.000026018904,0.002074486,0.00006325408,0.0000047235976,0.000011161049,0.0000036049087,0.9735846,0.023143353,0.000029411533,0.0008453245,0.00014416111],"about_ca_topic_score_codex":0.000024086481,"about_ca_topic_score_gemma":0.000026685151,"teacher_disagreement_score":0.010448666,"about_ca_system_score_codex":0.000021053553,"about_ca_system_score_gemma":0.0000038481076,"threshold_uncertainty_score":0.4465109},"labels":[],"label_agreement":null},{"id":"W4390270904","doi":"10.21203/rs.3.rs-3789748/v1","title":"A fast and enhanced shallow learning framework for solving free boundary options pricing problems","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Energy Load and Power Forecasting","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 Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Boundary (topology); Convexity; Computer science; Volatility (finance); Mathematical optimization; Generalization; Valuation of options; Local volatility; Artificial neural network; Implied volatility; Applied mathematics; Econometrics; Mathematics; Economics; Machine learning; Financial economics; Mathematical analysis","score_opus":0.06325340161860953,"score_gpt":0.3428639785573067,"score_spread":0.2796105769386972,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390270904","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":[0.1947015,0.011360839,0.778734,0.00053858804,0.0021339157,0.0032139672,0.0002862802,0.0034597313,0.00557119],"genre_scores_gemma":[0.95157534,0.0029078936,0.04043867,0.0000067720503,0.0011789155,0.0015554486,0.00029500353,0.00037389086,0.0016680856],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99713326,0.00010268043,0.0004019579,0.00065741176,0.00061157066,0.0010931528],"domain_scores_gemma":[0.99738514,0.0014715234,0.000065416345,0.0005819195,0.00027775366,0.00021821531],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0015742675,0.00035472243,0.00041830755,0.00055302226,0.00078669505,0.0006252548,0.00051183143,0.0005574398,0.000031204454],"category_scores_gemma":[0.0018592803,0.00038753846,0.00016036158,0.00047242315,0.00012392251,0.00013691277,0.0012741006,0.0034562193,0.000020629219],"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":[0.000017592092,0.00002550429,0.000809451,0.00953625,0.00020952163,0.000017274926,0.006356693,0.95491725,0.0034854086,0.0043886295,0.00059012795,0.019646276],"study_design_scores_gemma":[0.00081921346,0.00038805848,0.0023805352,0.028210334,0.00005706457,0.000010581553,0.0030712178,0.84115285,0.002235631,0.11162743,0.008490745,0.0015563184],"about_ca_topic_score_codex":0.00011143555,"about_ca_topic_score_gemma":0.00033961123,"teacher_disagreement_score":0.75687385,"about_ca_system_score_codex":0.00025945198,"about_ca_system_score_gemma":0.0001658648,"threshold_uncertainty_score":0.99985766},"labels":[],"label_agreement":null},{"id":"W4390345689","doi":"10.1007/s40866-023-00188-9","title":"Multi-Term Electrical Load Forecasting of Smart Cities Using a New Hybrid Highly Accurate Neural Network-Based Predictive Model","year":2023,"lang":"en","type":"article","venue":"Smart Grids and Sustainable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Smart grid; Artificial neural network; Markov chain; Term (time); Electrical load; Grid; Artificial intelligence; Machine learning; Voltage; Engineering; Electrical engineering","score_opus":0.022341091880720428,"score_gpt":0.22129337744758026,"score_spread":0.19895228556685984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390345689","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":[0.884544,0.0010508491,0.11253045,0.000027402028,0.0004672373,0.00013741423,0.000021166814,0.00051897694,0.0007024593],"genre_scores_gemma":[0.99540234,0.000100444275,0.001903503,0.000050150753,0.0003978573,0.000024229197,0.000041348518,0.00008863012,0.0019915267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976638,0.000046192366,0.00045876164,0.00034785277,0.00027126804,0.0012121516],"domain_scores_gemma":[0.999122,0.00016188141,0.00009514404,0.00021400477,0.00017774501,0.00022919108],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00032524145,0.00036334994,0.00044050926,0.00025119685,0.0002995131,0.00008897449,0.00016909654,0.00012544586,0.000006203687],"category_scores_gemma":[0.00008019493,0.000364173,0.00012825117,0.00074095745,0.00007822176,0.00031155616,0.0001397349,0.00022990955,4.3389838e-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":[0.00008167056,0.000012806609,0.0016223417,0.00015769576,0.00006243506,0.00012474816,0.00018143372,0.9931093,0.0004562331,0.0010802676,0.0014876332,0.001623432],"study_design_scores_gemma":[0.0008100046,0.00014730124,0.00026352945,0.000098012606,0.000046401175,0.000020764792,0.00012987005,0.99472374,0.0018562292,0.00062944787,0.00090874574,0.00036593043],"about_ca_topic_score_codex":0.00093556213,"about_ca_topic_score_gemma":0.00010653149,"teacher_disagreement_score":0.11085827,"about_ca_system_score_codex":0.00017979427,"about_ca_system_score_gemma":0.000311008,"threshold_uncertainty_score":0.999881},"labels":[],"label_agreement":null},{"id":"W4390397929","doi":"10.3389/fenrg.2023.1321459","title":"Smart grid power load type forecasting: research on optimization methods of deep learning models","year":2023,"lang":"en","type":"article","venue":"Frontiers in Energy Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"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":"China Electric Power Research Institute; Electric Power Research Institute","keywords":"Computer science; Smart grid; Big data; Artificial intelligence; Deep learning; Graph; Leverage (statistics); Data mining; Machine learning; Theoretical computer science; Engineering","score_opus":0.15015927863928466,"score_gpt":0.3740422525239781,"score_spread":0.22388297388469344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390397929","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":[0.053479634,0.005080735,0.64814746,0.00013527741,0.0056920843,0.000407911,0.000010038762,0.00087710586,0.28616977],"genre_scores_gemma":[0.9148699,0.002146151,0.07917665,0.000008800912,0.0002553623,0.000091530346,0.00008301041,0.00018415158,0.0031844513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955432,0.0012950102,0.0004244467,0.0004145155,0.0011983827,0.0011244686],"domain_scores_gemma":[0.99778026,0.0010579176,0.000035430166,0.0003861129,0.0005870181,0.00015327496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008716313,0.00019609762,0.00033959898,0.001971297,0.00023477805,0.00005739438,0.00046709552,0.00025685783,0.000060495586],"category_scores_gemma":[0.0012624012,0.0002045053,0.00006967798,0.0047715553,0.00015907898,0.00020989233,0.00021588225,0.0012454117,0.000011890075],"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":[0.00011566847,0.000024148881,0.00045006804,0.000051632822,0.00003763585,0.000025880328,0.0007539067,0.95389444,0.00029501354,0.0017123536,0.006510573,0.036128666],"study_design_scores_gemma":[0.0002714926,0.00021520005,0.000046611156,0.0001448454,0.0000022021063,0.0000021478343,0.0008156732,0.9774261,0.0028360265,0.0020891884,0.015970882,0.00017961774],"about_ca_topic_score_codex":0.00017120023,"about_ca_topic_score_gemma":0.00004074352,"teacher_disagreement_score":0.86139023,"about_ca_system_score_codex":0.00037775407,"about_ca_system_score_gemma":0.00010275095,"threshold_uncertainty_score":0.83394843},"labels":[],"label_agreement":null},{"id":"W4390549974","doi":"10.1109/sita60746.2023.10373741","title":"Electricity demand Forecasting: A systematic literature review","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Université du Québec à Rimouski","funders":"","keywords":"Electricity; Demand forecasting; Electricity demand; Electricity retailing; Electricity market; Consumption (sociology); Electricity generation; Computer science; Demand management; Environmental economics; Process (computing); Operations research; Economics; Engineering; Power (physics)","score_opus":0.018099421609701502,"score_gpt":0.22011982832160307,"score_spread":0.20202040671190158,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390549974","genre_codex":"review","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":[0.026222019,0.7193046,0.014880156,0.00049697614,0.0017213919,0.0019000843,0.000019691859,0.0111394655,0.22431561],"genre_scores_gemma":[0.87192947,0.11611884,0.0011591928,0.0011229349,0.0004001677,0.0002639968,0.00011563704,0.00014628889,0.008743488],"study_design_codex":"systematic_review","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918824,0.000028595858,0.0002649879,0.00011620441,0.00012511572,0.00027684096],"domain_scores_gemma":[0.99957997,0.00011649594,0.00002514312,0.00016842669,0.000028197923,0.000081799706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003537464,0.00013885135,0.00027566208,0.00010864141,0.000052886666,0.0000428271,0.000109970824,0.000055390887,0.000039618168],"category_scores_gemma":[0.00020384399,0.00010561206,0.00007641129,0.0012332008,0.000004810298,0.00008844067,0.000018860717,0.00013245712,0.00011690241],"study_design_candidate":"systematic_review","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":[0.0000019814668,0.00001061761,0.00011788291,0.8819472,0.00014747004,0.00026923133,0.0005210304,0.01311591,0.00059539155,0.0029844232,0.09816496,0.0021238618],"study_design_scores_gemma":[0.00027867057,0.000048390408,0.000039172453,0.28917676,0.00013909613,0.0005008066,0.000027049064,0.69926196,0.0015542596,0.0004899773,0.0077772085,0.0007066601],"about_ca_topic_score_codex":7.5425316e-7,"about_ca_topic_score_gemma":0.0000042238707,"teacher_disagreement_score":0.8457074,"about_ca_system_score_codex":0.000022544498,"about_ca_system_score_gemma":0.00000615058,"threshold_uncertainty_score":0.43067348},"labels":[],"label_agreement":null},{"id":"W4390670030","doi":"10.3390/en17020307","title":"An Empirical Mode Decomposition-Based Hybrid Model for Sub-Hourly Load Forecasting","year":2024,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","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":"University of Regina","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Hilbert–Huang transform; Series (stratigraphy); Range (aeronautics); Mode (computer interface); Context (archaeology); Computer science; Term (time); Algorithm; Decomposition; Singular spectrum analysis; Time series; Detrended fluctuation analysis; Mathematics; Singular value decomposition; Statistics; Machine learning; Engineering","score_opus":0.02728204635523143,"score_gpt":0.31008329033648135,"score_spread":0.28280124398124995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390670030","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":[0.6385483,0.0007526894,0.35790047,0.00004858541,0.00048174657,0.000061658335,0.00008968185,0.0011098882,0.0010069811],"genre_scores_gemma":[0.9780112,0.000013115565,0.021128457,0.000079246245,0.00036705608,0.00007611431,0.00014146556,0.000097606884,0.00008576187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895275,0.000012805964,0.00023555256,0.00026352567,0.00016567559,0.00036968454],"domain_scores_gemma":[0.9994487,0.00019598988,0.000015214208,0.00018779618,0.00004898136,0.00010331962],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012924314,0.00022215208,0.00017747834,0.00011670476,0.00012209105,0.00017372891,0.00014950929,0.00007020504,0.00000888914],"category_scores_gemma":[0.00001885998,0.0002190308,0.00013030648,0.00012591135,0.000026373627,0.0002901169,0.0000130584185,0.0001386443,0.000006567416],"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":[0.000011865118,0.000012479587,0.00003502188,0.00012074869,0.000026575719,0.0000140203765,0.00030893186,0.9791913,0.0088586025,0.000594424,0.0025147586,0.008311273],"study_design_scores_gemma":[0.0001572614,0.000043392,0.0000056002236,0.00012532686,0.00002327962,0.000010457161,0.000016102245,0.9426158,0.05394659,0.0012212468,0.0015658273,0.0002690953],"about_ca_topic_score_codex":0.000020546355,"about_ca_topic_score_gemma":0.00007040668,"teacher_disagreement_score":0.33946288,"about_ca_system_score_codex":0.00011194535,"about_ca_system_score_gemma":0.00008860057,"threshold_uncertainty_score":0.8931817},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"grok","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"medium"}],"label_agreement":"agree"},{"id":"W4390713350","doi":"10.23977/acss.2023.071106","title":"Research on the influencing factors of wind power generation based on clustering and decision tree","year":2023,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Decision tree; Wind power; Cluster analysis; Electricity generation; Power (physics); Computer science; Mean squared error; Wind speed; Tree (set theory); Generator (circuit theory); AdaBoost; Decision tree learning; Algorithm; Data mining; Control theory (sociology); Mathematical optimization; Artificial intelligence; Mathematics; Engineering; Statistics; Meteorology; Electrical engineering; Support vector machine; Geography","score_opus":0.05924849057714729,"score_gpt":0.3055355905099297,"score_spread":0.2462870999327824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390713350","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":[0.9738509,0.0008579946,0.024181217,0.000008133193,0.00050408766,0.00010845319,0.0000025758693,0.000034766217,0.00045185108],"genre_scores_gemma":[0.99953204,0.00013047947,0.00020519123,0.000012852207,0.00009591976,0.0000054892603,0.00000215205,0.000012445525,0.000003422454],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991603,0.00007446847,0.00022331289,0.00015255404,0.00022319618,0.00016618916],"domain_scores_gemma":[0.9987306,0.0010599869,0.000027529213,0.000125032,0.000026879403,0.000029970402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006934249,0.0000967489,0.00014504531,0.00021478413,0.00008222649,0.00006046284,0.00007624302,0.00004334166,0.0000015822543],"category_scores_gemma":[0.00001569939,0.0000655828,0.000015871903,0.0002750146,0.000025023184,0.00011170352,0.00003313053,0.00012814901,0.0000010807213],"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":[0.0000056769254,0.0000033038027,0.0015734315,0.000044436594,0.0000033902747,0.000002885655,0.00039083956,0.9809316,0.0011593708,0.00024524072,0.00003981027,0.015600062],"study_design_scores_gemma":[0.00014625574,0.00012827024,0.0021407017,0.00065869495,7.209053e-7,8.5217204e-7,0.00014002493,0.99551886,0.0007840346,0.000057124485,0.00035112072,0.00007334202],"about_ca_topic_score_codex":0.000014765737,"about_ca_topic_score_gemma":0.000030489755,"teacher_disagreement_score":0.025681127,"about_ca_system_score_codex":0.000018632638,"about_ca_system_score_gemma":0.0000044449425,"threshold_uncertainty_score":0.2674389},"labels":[],"label_agreement":null},{"id":"W4390790868","doi":"10.1021/acs.estlett.3c00829","title":"Assessing Climate Change Impacts on Wind Energy Resources over China Based on CMIP6 Multimodel Ensemble","year":2024,"lang":"en","type":"article","venue":"Environmental Science & Technology Letters","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"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 Regina","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Environmental science; Climate change; Context (archaeology); Meteorology; Wind speed; Climatology; China; Wind power; Global warming; Geography; Geology; Engineering","score_opus":0.008869350350400875,"score_gpt":0.21718622558259215,"score_spread":0.20831687523219128,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390790868","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":[0.99421316,0.00020215011,0.0008074516,0.0011046393,0.00046340155,0.00007029008,0.000010505723,0.0007001099,0.0024282667],"genre_scores_gemma":[0.9980634,0.000045338125,0.00052518287,0.0011866294,0.00010106683,0.000017344282,0.000005731398,0.000046840032,0.000008492191],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982298,0.00001400097,0.0001676798,0.00050353474,0.00036877132,0.0007161965],"domain_scores_gemma":[0.99950266,0.000039333092,0.000029765919,0.00032930312,8.5867646e-7,0.00009807818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021485137,0.00026920572,0.0001506845,0.0007875996,0.00030074007,0.00016902758,0.00036294875,0.00013759242,0.000049509097],"category_scores_gemma":[0.000008583717,0.0002445599,0.00005907529,0.0006532764,0.0005631124,0.00052355736,0.00010048765,0.00035847782,0.000050925246],"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":[0.0000072848775,0.000044987944,0.0023890564,0.000017958053,0.000010313041,0.00013987806,0.00033473034,0.13649027,0.7865928,0.00039601585,0.00011688841,0.07345983],"study_design_scores_gemma":[0.0003446853,0.00018501737,0.02410161,0.00054624357,0.000017164975,0.0000287972,0.000111131725,0.76215744,0.2056298,0.00006873869,0.0061429325,0.0006664266],"about_ca_topic_score_codex":0.000020914234,"about_ca_topic_score_gemma":0.0000037069988,"teacher_disagreement_score":0.62566715,"about_ca_system_score_codex":0.00036528826,"about_ca_system_score_gemma":0.00000673902,"threshold_uncertainty_score":0.99728626},"labels":[],"label_agreement":null},{"id":"W4391076635","doi":"10.21203/rs.3.rs-3877425/v1","title":"Ensemble deep neural network method for solving free boundary American style stochastic volatility models","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Energy Load and Power Forecasting","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":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Volatility (finance); Deep neural networks; Artificial intelligence; Computer science; Style (visual arts); Stochastic volatility; Econometrics; Machine learning; Economics; History; Archaeology","score_opus":0.054431505916920774,"score_gpt":0.35706851184835553,"score_spread":0.3026370059314348,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391076635","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":[0.028400099,0.011146617,0.95108986,0.00017601335,0.0018768628,0.0014553671,0.000398592,0.0010610308,0.004395579],"genre_scores_gemma":[0.94258857,0.00006052078,0.053983744,0.000017691846,0.0018705273,0.0007232888,0.00020924729,0.00027624774,0.00027018407],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99578506,0.0002694802,0.0005461952,0.00089817436,0.0008674928,0.0016335838],"domain_scores_gemma":[0.9961997,0.0018254974,0.00007049216,0.001206507,0.0003595762,0.00033820633],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002531675,0.0005175178,0.00071661465,0.00036694977,0.00043076905,0.000569074,0.00088788476,0.00032847494,0.000038468235],"category_scores_gemma":[0.0004789678,0.0005344523,0.00039410344,0.0006763759,0.00018736593,0.00012662105,0.0021270795,0.0031842869,0.000013184481],"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":[0.000045136007,0.000016259477,0.000015669943,0.002444268,0.00016353736,0.000017876167,0.00090813846,0.942198,0.00009932591,0.0010196847,0.0038555379,0.049216617],"study_design_scores_gemma":[0.00015425718,0.00011290947,0.000026169677,0.00075696985,0.000040790892,0.0000054140737,0.00019828755,0.9126912,0.00005451154,0.08456935,0.0009583308,0.00043182855],"about_ca_topic_score_codex":0.00068069325,"about_ca_topic_score_gemma":0.001017964,"teacher_disagreement_score":0.91418844,"about_ca_system_score_codex":0.0004999156,"about_ca_system_score_gemma":0.00025497525,"threshold_uncertainty_score":0.9997107},"labels":[],"label_agreement":null},{"id":"W4391088739","doi":"10.1016/j.apenergy.2024.122649","title":"Price forecasting in the Ontario electricity market via TriConvGRU hybrid model: Univariate vs. multivariate frameworks","year":2024,"lang":"en","type":"article","venue":"Applied Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; HEC Montréal","funders":"","keywords":"Autoregressive integrated moving average; Electricity price forecasting; Univariate; Electricity; Electricity market; Econometrics; Computer science; Pooling; Volatility (finance); Probabilistic forecasting; Time series; Autoregressive model; Deep learning; Artificial intelligence; Multivariate statistics; Economics; Machine learning; Engineering","score_opus":0.010496445363467362,"score_gpt":0.19474926123338232,"score_spread":0.18425281586991496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391088739","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":[0.08743767,0.0004996546,0.68127143,0.000072411116,0.00068983686,0.00018769322,0.000008236755,0.0008287304,0.22900435],"genre_scores_gemma":[0.99513805,0.00005167134,0.0034245977,0.0002914475,0.00023044807,0.000105623934,0.00003173841,0.00009844994,0.0006279904],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981291,0.000045404617,0.00043673863,0.00041918305,0.00026081654,0.000708755],"domain_scores_gemma":[0.99904513,0.00045923248,0.000043843684,0.00034761126,0.000018353403,0.00008580309],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00049504446,0.00037509337,0.0003077603,0.00025279194,0.00011599985,0.00017301626,0.0004175865,0.00026914265,0.0000945728],"category_scores_gemma":[0.000019747922,0.0003105312,0.00010421223,0.00082797324,0.000024949266,0.00015971808,0.000068396635,0.0011871932,0.000009439721],"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":[0.000051155814,0.000042727213,0.000025293859,0.000064111366,0.000095798176,0.00010779462,0.0017761809,0.90878165,0.00091131934,0.04615481,0.0014818673,0.040507294],"study_design_scores_gemma":[0.00026748254,0.000020462723,0.0000843865,0.00007567642,0.000028894337,0.000036126323,0.00001526488,0.9654382,0.001430452,0.010531294,0.021692937,0.00037883388],"about_ca_topic_score_codex":0.005631977,"about_ca_topic_score_gemma":0.004320888,"teacher_disagreement_score":0.90770036,"about_ca_system_score_codex":0.00031846509,"about_ca_system_score_gemma":0.00007595565,"threshold_uncertainty_score":0.9999347},"labels":[],"label_agreement":null},{"id":"W4391234540","doi":"10.1088/1742-6596/2689/1/012005","title":"Multi-layered perceptron network for short-term load forecasting","year":2024,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Term (time); Perceptron; Computer science; Multilayer perceptron; Artificial intelligence; Artificial neural network; Machine learning; Econometrics; Economics; Physics","score_opus":0.05330369953246535,"score_gpt":0.2617839903523755,"score_spread":0.20848029081991012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391234540","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":[0.23340055,0.0025223012,0.7571498,0.000098728924,0.0045037647,0.00017229951,0.000040680545,0.0002547268,0.001857116],"genre_scores_gemma":[0.97786045,0.00015231382,0.019779652,0.000008230797,0.0019665712,0.0000063679927,0.0000070414244,0.00004839264,0.00017097151],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989823,0.000013062355,0.00038754338,0.00011375532,0.00018200291,0.00032129648],"domain_scores_gemma":[0.9994434,0.00009606854,0.000060052098,0.00009568194,0.00022124493,0.000083568135],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020721086,0.00019920296,0.00029074406,0.00003777806,0.00008705207,0.0002071784,0.00016910047,0.00006813979,0.000018558101],"category_scores_gemma":[0.000025662925,0.00017522235,0.00019153433,0.0001344851,0.000041604573,0.000649697,0.000023839963,0.00029251524,0.0000030827384],"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":[0.00009899679,0.000040813127,0.00087918265,0.00095288403,0.0004968342,0.00008698756,0.0049313693,0.2441758,0.06809635,0.014251186,0.0024462084,0.6635434],"study_design_scores_gemma":[0.0008777492,0.00077831,0.0006479728,0.0039178063,0.00026758635,0.0004114689,0.0011784371,0.8829141,0.074631155,0.012883579,0.020365108,0.0011266846],"about_ca_topic_score_codex":0.0000016958476,"about_ca_topic_score_gemma":0.000020381249,"teacher_disagreement_score":0.7444599,"about_ca_system_score_codex":0.00007426813,"about_ca_system_score_gemma":0.000121281286,"threshold_uncertainty_score":0.71453595},"labels":[],"label_agreement":null},{"id":"W4391307184","doi":"10.1109/smc53992.2023.10394230","title":"Explainable Robust Smart Meter Data Clustering for Improved Energy Management","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Outlier; Smart meter; Mixture model; Computer science; Cluster analysis; Robustness (evolution); Software deployment; Energy consumption; Smart grid; Data mining; Decision tree; Energy management; Energy (signal processing); Artificial intelligence; Engineering","score_opus":0.04842033823232392,"score_gpt":0.2322401957935997,"score_spread":0.18381985756127578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391307184","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":[0.0019160686,0.000113541086,0.914076,0.000112386544,0.0014953593,0.00019407288,0.00005675784,0.0018640318,0.08017181],"genre_scores_gemma":[0.5994535,0.0009381478,0.25637206,0.00080604386,0.0013269952,0.000784274,0.0036277308,0.00054343726,0.13614778],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991242,0.000005012825,0.00017248298,0.00024129152,0.000061708095,0.00039527533],"domain_scores_gemma":[0.999377,0.00004947482,0.000012843883,0.0004929263,0.0000117518775,0.00005596151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018472581,0.00013814228,0.00012386813,0.00011364187,0.00008730048,0.000058078087,0.00033015636,0.00004384235,0.000058232086],"category_scores_gemma":[0.0000074799536,0.00013238583,0.00003760722,0.00020809805,0.0000069823636,0.00021866261,0.00032167335,0.000040111507,0.00002017473],"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":[0.000027172171,0.000020608733,0.000035348992,0.0006935254,0.00043858777,0.00003139577,0.00015425023,0.664882,0.0045092683,0.0070930733,0.16334498,0.15876979],"study_design_scores_gemma":[0.0002212896,0.0000106323805,0.000012650986,0.000018803046,0.000013778934,0.000001466571,0.000059656864,0.7519517,0.0012847395,0.000082966646,0.24619576,0.00014655426],"about_ca_topic_score_codex":0.00004705126,"about_ca_topic_score_gemma":0.00019380538,"teacher_disagreement_score":0.65770394,"about_ca_system_score_codex":0.000024173412,"about_ca_system_score_gemma":0.0000027688045,"threshold_uncertainty_score":0.53985375},"labels":[],"label_agreement":null},{"id":"W4391361566","doi":"10.18280/mmep.110117","title":"Seasonal Autoregressive Integrated Moving Average Modelling and Forecasting of Monthly Rainfall in Selected African Stations","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"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":"Autoregressive model; Autoregressive integrated moving average; Environmental science; Meteorology; Climatology; Geography; Econometrics; Statistics; Mathematics; Time series; Geology","score_opus":0.019605522601567893,"score_gpt":0.195312808999655,"score_spread":0.1757072863980871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391361566","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":[0.33857504,0.0017382543,0.6585504,0.0000106754105,0.0000602928,0.00010986327,0.000016997084,0.00031968157,0.0006187847],"genre_scores_gemma":[0.9440335,0.00012321223,0.055657618,0.0000013607508,0.000031097905,0.00003279036,0.000016580192,0.000070353,0.00003350899],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99867284,0.000014494502,0.00051295373,0.0002577075,0.00016155877,0.0003804185],"domain_scores_gemma":[0.9993034,0.0003995369,0.000032901644,0.000097110606,0.000049986356,0.00011703734],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029985575,0.00027051714,0.0003600071,0.00027386166,0.000047343652,0.00010696634,0.0000720657,0.00011553592,0.000005995961],"category_scores_gemma":[0.00005556612,0.00025389693,0.00004295801,0.00039939152,0.000033684868,0.00021782255,0.00003089682,0.00040289527,7.913335e-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":[0.000003131034,0.000011195596,0.00001619691,0.0018088917,0.000048157435,0.000010446377,0.0036606698,0.98699045,0.0005246729,0.005669278,0.0000022018999,0.0012547278],"study_design_scores_gemma":[0.00015080377,0.000021683187,0.0000052546743,0.0026491857,0.000019901941,0.000016935628,0.000067723944,0.99320954,0.00021421336,0.003313389,0.000068108166,0.0002632814],"about_ca_topic_score_codex":0.00004437876,"about_ca_topic_score_gemma":0.000005506487,"teacher_disagreement_score":0.60545844,"about_ca_system_score_codex":0.000050022027,"about_ca_system_score_gemma":0.000023071243,"threshold_uncertainty_score":0.9999913},"labels":[],"label_agreement":null},{"id":"W4391451243","doi":"10.3390/forecast6010007","title":"Forecasting the Occurrence of Electricity Price Spikes: A Statistical-Economic Investigation Study","year":2024,"lang":"en","type":"article","venue":"Forecasting","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Computer science; Decision tree; Machine learning; Hyperparameter; Econometrics; Artificial intelligence; Electricity; Electricity price forecasting; Statistical model; Binary classification; Random forest; Electricity market; Data mining; Support vector machine; Economics; Engineering","score_opus":0.03612643993758135,"score_gpt":0.2410028268789844,"score_spread":0.20487638694140303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391451243","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":[0.9681378,0.000697758,0.023595678,0.000017210568,0.0010538746,0.00037551814,0.00009465115,0.00043566007,0.005591863],"genre_scores_gemma":[0.9981874,0.0000090778285,0.0013909857,0.000009996486,0.000260635,0.000042962856,0.000027297487,0.000041612755,0.000029986806],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838877,0.00006222189,0.000595955,0.00030537287,0.000208855,0.0004388014],"domain_scores_gemma":[0.998572,0.0009879251,0.00009159921,0.00022258823,0.000040586227,0.000085338215],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007656684,0.0002499311,0.00025594013,0.0001625668,0.00016733138,0.00013290267,0.00025441952,0.000062213876,0.00005667483],"category_scores_gemma":[0.000294935,0.00019932492,0.00007056505,0.0005311812,0.0000796531,0.00026354298,0.000064030355,0.00038790837,0.000019191837],"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":[0.000045248355,0.000118378295,0.040840823,0.0023400772,0.0005271103,0.00017876261,0.023573097,0.5932951,0.0032526283,0.010106188,0.005679704,0.32004285],"study_design_scores_gemma":[0.00013321053,0.00014305241,0.00039659842,0.00028617223,0.000055273355,0.00004909267,0.00037533176,0.99521565,0.0015829345,0.00075738755,0.00076942926,0.00023588771],"about_ca_topic_score_codex":0.00010657059,"about_ca_topic_score_gemma":0.00007269232,"teacher_disagreement_score":0.4019205,"about_ca_system_score_codex":0.0001232831,"about_ca_system_score_gemma":0.00008075048,"threshold_uncertainty_score":0.8128234},"labels":[],"label_agreement":null},{"id":"W4391687937","doi":"10.1049/gtd2.13130","title":"Guest Editorial: Artificial intelligence‐empowered reliable forecasting for energy sectors","year":2024,"lang":"en","type":"editorial","venue":"IET Generation Transmission & Distribution","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Computer science; Energy sector; Artificial intelligence; Energy (signal processing); Operations research; Data science; Machine learning; Engineering; Environmental economics; Economics","score_opus":0.02577477872660308,"score_gpt":0.25249589755537477,"score_spread":0.2267211188287717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391687937","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000020049403,0.0012678119,0.34277102,0.000037161633,0.652897,0.00022761941,0.0020003747,0.00060140586,0.00017754562],"genre_scores_gemma":[0.0047925455,0.00072659255,0.00065624225,0.000004744149,0.933586,0.00027679827,0.059249524,0.00022104415,0.000486514],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99632335,0.000057650588,0.0011951606,0.0008120677,0.00091072527,0.00070106966],"domain_scores_gemma":[0.9984022,0.00032205606,0.00017201963,0.00033940093,0.0005083129,0.00025603265],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0005246442,0.000740719,0.00058868434,0.00019002186,0.00041219377,0.00046991106,0.00029789377,0.001884999,0.00006526458],"category_scores_gemma":[0.00025255623,0.00075071695,0.00041515508,0.00048345167,0.000046498415,0.00028824952,0.000025847621,0.00084757066,0.000023500968],"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":[0.000045984667,0.000032128064,8.4568434e-8,0.00043834376,0.00008271459,0.000004827841,0.00009565217,0.029993901,0.0049516708,0.00054397585,0.94683427,0.016976438],"study_design_scores_gemma":[0.0001220337,0.00011265442,1.3852244e-8,0.0005658285,0.00013952017,0.0000015562133,0.000014295681,0.20634995,0.041532442,0.0009367243,0.7496324,0.00059253274],"about_ca_topic_score_codex":0.00008065305,"about_ca_topic_score_gemma":0.000081081875,"teacher_disagreement_score":0.34211478,"about_ca_system_score_codex":0.0004768031,"about_ca_system_score_gemma":0.000285065,"threshold_uncertainty_score":0.9994944},"labels":[],"label_agreement":null},{"id":"W4391738567","doi":"10.1007/s10462-023-10678-y","title":"Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting","year":2024,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":75,"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":"Science Fund of the Republic of Serbia","keywords":"Computer science; Wind power; Benchmark (surveying); Metaheuristic; Hyperparameter; Artificial neural network; Renewable energy; Decomposition; Artificial intelligence; Machine learning","score_opus":0.11953714791841913,"score_gpt":0.3208487149796302,"score_spread":0.20131156706121106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391738567","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":[0.0017883085,0.12071827,0.8748609,0.000049438917,0.0012766134,0.00029555228,0.0000135075425,0.00032232082,0.00067508727],"genre_scores_gemma":[0.94142735,0.036745526,0.019880796,0.0001458653,0.0012345916,0.00013394908,0.00026585456,0.00012005717,0.000046019784],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99816424,0.000046447913,0.00083193654,0.00037030893,0.0001845708,0.00040250018],"domain_scores_gemma":[0.99930274,0.00020153848,0.00005088586,0.00023024098,0.0001052805,0.00010928431],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004940691,0.00030063107,0.00039563785,0.00012630073,0.00016742092,0.00017653196,0.00017298432,0.00010443099,0.000052830725],"category_scores_gemma":[0.000049791128,0.00029706577,0.00024368086,0.00034904122,0.00003237559,0.00044783327,0.000031495212,0.00016679364,0.000019737054],"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":[0.0000023587963,0.000010310705,3.198783e-7,0.0015538717,0.00005836959,0.00001580211,0.00011113261,0.36899924,0.0044544213,0.0057316795,0.00015449515,0.618908],"study_design_scores_gemma":[0.000007730261,0.00003121777,1.099132e-7,0.0029902589,0.00018343993,0.0000440017,0.000008938464,0.9309246,0.06027467,0.0041314084,0.0010906535,0.00031295928],"about_ca_topic_score_codex":0.00001255245,"about_ca_topic_score_gemma":0.000068161346,"teacher_disagreement_score":0.93963903,"about_ca_system_score_codex":0.000096792486,"about_ca_system_score_gemma":0.000029404664,"threshold_uncertainty_score":0.99994814},"labels":[],"label_agreement":null},{"id":"W4391774748","doi":"10.1016/j.segan.2024.101319","title":"A deep clustering framework for load pattern segmentation","year":2024,"lang":"en","type":"article","venue":"Sustainable Energy Grids and Networks","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Education; CHEO Research Institute","keywords":"Cluster analysis; Computer science; Autoencoder; Profiling (computer programming); Dimensionality reduction; Bottleneck; Data mining; Smart grid; Smart meter; Big data; Artificial intelligence; Machine learning; Deep learning; Engineering","score_opus":0.005344232620995314,"score_gpt":0.21410019984558046,"score_spread":0.20875596722458514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391774748","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":[0.0024851363,0.015502543,0.9788631,0.000040409766,0.0010495902,0.000082204926,0.0000017907146,0.00038420307,0.0015910065],"genre_scores_gemma":[0.9926086,0.0012870732,0.0031651754,0.00011573356,0.0012850737,0.00013343271,0.000038034013,0.0000806649,0.0012862041],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899834,0.00000990075,0.0001782335,0.00022342864,0.000088813555,0.00050125946],"domain_scores_gemma":[0.9995992,0.00014995404,0.000014385081,0.00010811308,0.000042229527,0.00008610787],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015025568,0.00018740544,0.0001455646,0.00007210658,0.00014544571,0.00022139007,0.00006811419,0.00015314238,0.000022565144],"category_scores_gemma":[0.000011165175,0.00018195463,0.000060833612,0.00020413437,0.00002035507,0.00019360961,0.000046705805,0.00015895585,6.429227e-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":[0.000008264112,0.0000035635212,0.000035364563,0.0003798175,0.000057000547,0.000040801784,0.00031296842,0.7502809,0.000014667393,0.024397582,0.0007698854,0.22369917],"study_design_scores_gemma":[0.00013060281,0.00004587958,0.000010457753,0.00018071599,0.00002401019,0.000011283374,0.00046760414,0.9467654,0.00011069355,0.0055074007,0.046520658,0.00022524968],"about_ca_topic_score_codex":0.00008109021,"about_ca_topic_score_gemma":0.00006953443,"teacher_disagreement_score":0.99012345,"about_ca_system_score_codex":0.00012346277,"about_ca_system_score_gemma":0.000019746343,"threshold_uncertainty_score":0.74198943},"labels":[],"label_agreement":null},{"id":"W4392052195","doi":"10.3390/su16051789","title":"Using Probabilistic Machine Learning Methods to Improve Beef Cattle Price Modeling and Promote Beef Production Efficiency and Sustainability in Canada","year":2024,"lang":"en","type":"article","venue":"Sustainability","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Autoregressive integrated moving average; Univariate; Econometrics; Sustainability; Probabilistic logic; Beef cattle; Computer science; Economics; Multivariate statistics; Machine learning; Time series; Artificial intelligence","score_opus":0.011013180127288146,"score_gpt":0.27180725872815065,"score_spread":0.2607940786008625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392052195","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":[0.93214023,0.0018083912,0.064018264,0.00036288626,0.00038074932,0.0010195394,0.0000061098744,0.00020871543,0.00005511039],"genre_scores_gemma":[0.99513316,0.000011699383,0.0046118624,0.0000060318125,0.000067141475,0.00006641891,0.000004112377,0.000046570996,0.00005302564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977622,0.00018559527,0.00046069498,0.0007560937,0.00018449251,0.000650924],"domain_scores_gemma":[0.99897593,0.00018837178,0.000027961376,0.0002882693,0.00033241214,0.0001870315],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0019464751,0.00029467986,0.0003251241,0.00018247333,0.00017827022,0.000104436556,0.00010051454,0.00008545128,0.0000038880435],"category_scores_gemma":[0.0034593306,0.00029409927,0.000032814856,0.00075145386,0.000069939175,0.0002608415,0.00015847938,0.0005594428,1.0366546e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017112552,0.00002026107,0.005861293,0.003994276,0.000012325093,0.000015010865,0.0028330972,0.9501107,0.0005255809,0.00021342459,0.0000016798655,0.036395267],"study_design_scores_gemma":[0.00009692337,0.000052937274,0.0009989542,0.00008554069,0.000022681867,0.000019297599,0.0013795254,0.99135333,0.0005266569,0.004804275,0.0003371384,0.00032272108],"about_ca_topic_score_codex":0.32087976,"about_ca_topic_score_gemma":0.13824815,"teacher_disagreement_score":0.1826316,"about_ca_system_score_codex":0.0051467456,"about_ca_system_score_gemma":0.0015952804,"threshold_uncertainty_score":0.9999511},"labels":[],"label_agreement":null},{"id":"W4392090995","doi":"10.29103/jreece.v3i2.11281","title":"Short-Term Forecasting of Electricity Consumption Using Fuzzy Logic","year":2023,"lang":"en","type":"article","venue":"Journal of Renewable Energy Electrical and Computer Engineering","topic":"Energy Load and Power Forecasting","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":"Heritage College","funders":"","keywords":"Term (time); Fuzzy logic; Consumption (sociology); Electricity; Computer science; Environmental science; Artificial intelligence; Engineering; Electrical engineering","score_opus":0.025701937165774135,"score_gpt":0.21581964551122398,"score_spread":0.19011770834544986,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392090995","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":[0.6285821,0.0016036932,0.36922482,0.0000027037795,0.00038511775,0.000018496905,8.7408074e-7,0.000109724475,0.00007244967],"genre_scores_gemma":[0.99098957,0.0008308834,0.0076735276,0.00000772568,0.00044369514,0.0000011166211,0.0000028591417,0.000034862584,0.000015747743],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986909,0.000018806379,0.0005465144,0.00012695571,0.0002188128,0.0003980145],"domain_scores_gemma":[0.99943954,0.00015828517,0.000104453364,0.000083706735,0.00007859832,0.00013542776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022103371,0.00020500038,0.0004030893,0.00051247916,0.000049968276,0.000034972636,0.00014262709,0.0001100876,0.0000024267028],"category_scores_gemma":[0.000020979112,0.00018986133,0.000119280725,0.0007471394,0.000015103877,0.00017102293,0.00004368985,0.00022005095,2.536854e-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":[0.000009355472,0.000011422494,0.0012912567,0.00008504021,0.00008383394,0.000052350977,0.000025677675,0.95467573,0.020911,0.0003287737,0.00009165956,0.022433907],"study_design_scores_gemma":[0.00021898717,0.00014115914,0.00086370116,0.00016727243,0.000034282413,0.00040459086,0.0000012071672,0.9823912,0.015093519,0.00021843561,0.00027013017,0.00019554097],"about_ca_topic_score_codex":0.000020100038,"about_ca_topic_score_gemma":0.0000026812738,"teacher_disagreement_score":0.36240748,"about_ca_system_score_codex":0.000065445725,"about_ca_system_score_gemma":0.000019520068,"threshold_uncertainty_score":0.774232},"labels":[],"label_agreement":null},{"id":"W4392164686","doi":"10.1002/9781394167319.ch2","title":"Renewable Power Generation Price Prediction and Forecasting Using Machine Learning","year":2024,"lang":"en","type":"other","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"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":"Electricity price forecasting; Renewable energy; Computer science; Predictive power; Machine learning; Econometrics; Economics; Artificial intelligence; Electricity price; Engineering; Electricity; Electrical engineering","score_opus":0.023240095147018157,"score_gpt":0.20793716870427642,"score_spread":0.18469707355725826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392164686","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0011839684,0.010594328,0.017546438,0.000003555591,0.001560272,0.00010618796,0.000025880337,0.0016961639,0.9672832],"genre_scores_gemma":[0.02503978,0.0005321844,0.012352077,0.000015953203,0.0015243122,0.000010080693,0.00015750967,0.0012363679,0.9591317],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999144,0.000016842827,0.00021934132,0.000265353,0.00012562462,0.00022884127],"domain_scores_gemma":[0.999758,0.000013877373,0.000051841555,0.00010521699,0.000011370238,0.000059681413],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013236163,0.00026082975,0.00020452886,0.000251381,0.000054952463,0.00010349709,0.00004316351,0.0002444823,0.000644168],"category_scores_gemma":[0.000020477084,0.000250997,0.00004048456,0.00014857961,0.000012941841,0.000075555516,0.00004032573,0.00029087858,0.000021379845],"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":[0.0000058656506,0.000016656302,0.0008245445,0.001654695,0.00060879893,0.000055172575,0.0006713973,0.60959363,0.0352261,0.00092036964,0.34350234,0.0069204024],"study_design_scores_gemma":[0.00007051932,0.00001445671,0.0000011282386,0.00033863404,0.0000306043,0.000033861652,0.000011381523,0.6256293,0.0003860186,0.000012335288,0.37326536,0.00020636986],"about_ca_topic_score_codex":0.0006116844,"about_ca_topic_score_gemma":0.0004419035,"teacher_disagreement_score":0.03484008,"about_ca_system_score_codex":0.000051576215,"about_ca_system_score_gemma":0.000007932798,"threshold_uncertainty_score":0.9999942},"labels":[],"label_agreement":null},{"id":"W4392255252","doi":"10.1145/3638209.3638218","title":"Addressing Load Forecasting Challenges in Industrial Environments Using Time Series Deep Models","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Concordia University","funders":"","keywords":"Computer science; Mean squared error; Energy consumption; Deep learning; Electricity; Artificial intelligence; Energy management; Task (project management); Machine learning; Consumption (sociology); Preprocessor; Time series; Data pre-processing; Demand forecasting; Energy (signal processing); Operations research; Engineering; Statistics","score_opus":0.23532123005201314,"score_gpt":0.25726609098262754,"score_spread":0.0219448609306144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392255252","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":[0.9180747,0.0017071932,0.004350807,0.000065045795,0.0007901808,0.0002105063,0.000008177749,0.0008916007,0.073901735],"genre_scores_gemma":[0.996823,0.00033821372,0.001982436,0.0000075208422,0.00028195092,0.000011617202,0.000014084504,0.0000783841,0.0004627973],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875283,0.000027410135,0.00028941003,0.00022146938,0.0002214422,0.00048740976],"domain_scores_gemma":[0.9996742,0.00006820389,0.00003189164,0.00014898536,0.0000059549006,0.00007076634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028575398,0.00020500807,0.00021607777,0.00017272029,0.0000818681,0.000041811858,0.000118760945,0.00016640888,0.000070869144],"category_scores_gemma":[0.000033000917,0.00021541373,0.000043751457,0.0002792311,0.00002621486,0.0005332967,0.00008698752,0.00022214423,0.000053948403],"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":[0.000006862138,0.0000060946495,0.00014616798,0.000025140745,0.000018037821,0.00003487708,0.0008657742,0.9691846,0.002940184,0.00010333503,0.000036506437,0.026632419],"study_design_scores_gemma":[0.00035390616,0.000013197174,0.000035046265,0.000198885,0.0000067007495,0.000015696078,0.00030408552,0.9946042,0.0027913582,0.00046669348,0.0009536561,0.00025663472],"about_ca_topic_score_codex":0.000025601721,"about_ca_topic_score_gemma":0.00006593991,"teacher_disagreement_score":0.07874824,"about_ca_system_score_codex":0.00015009801,"about_ca_system_score_gemma":0.000017302536,"threshold_uncertainty_score":0.87843174},"labels":[],"label_agreement":null},{"id":"W4392349944","doi":"10.18280/ts.410148","title":"Deep Learning-Based Auto-LSTM Approach for Renewable Energy Forecasting: A Hybrid Network Model","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"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":"Renewable energy; Computer science; Deep learning; Artificial intelligence; Energy (signal processing); Machine learning; Engineering; Mathematics","score_opus":0.021523533150136304,"score_gpt":0.20244386933981515,"score_spread":0.18092033618967884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392349944","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":[0.0012306141,0.001530754,0.98470575,0.00001934852,0.00033629665,0.00017354487,0.00001593732,0.0011018066,0.010885923],"genre_scores_gemma":[0.96364737,0.000015496997,0.0335778,0.0000963897,0.00090194563,0.00027878524,0.00032435305,0.00013901756,0.0010188365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99824953,0.000027726841,0.0003888495,0.00039087862,0.00023917347,0.00070381525],"domain_scores_gemma":[0.9994819,0.00015975234,0.000037986363,0.00013893371,0.00003778239,0.00014362155],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000359212,0.00033397548,0.00027050357,0.00012673259,0.00019781118,0.00016510658,0.00019366926,0.000089160734,0.00010327577],"category_scores_gemma":[0.000011562997,0.00033306296,0.00021088035,0.00024718913,0.000028493501,0.00015017357,0.000025995114,0.00020508596,0.000003082023],"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":[0.000028817023,0.00002925615,0.00001722801,0.000278601,0.000104993,0.000011795748,0.00010863732,0.9787063,0.00049699825,0.0010463396,0.007369167,0.011801888],"study_design_scores_gemma":[0.00043360458,0.000118053016,0.0000012382452,0.00013068327,0.00006331327,0.000011265751,0.00001306393,0.96011764,0.0015438217,0.00073130254,0.036461648,0.00037433815],"about_ca_topic_score_codex":0.000031942353,"about_ca_topic_score_gemma":0.000025327477,"teacher_disagreement_score":0.96241677,"about_ca_system_score_codex":0.00009945318,"about_ca_system_score_gemma":0.000053189506,"threshold_uncertainty_score":0.99991214},"labels":[],"label_agreement":null},{"id":"W4392577905","doi":"10.5194/egusphere-egu24-11394","title":"Attention-based postprocessing of ensemble weather forecasts for renewable energy applications by leveraging inter-ensemble relationships of multiple predictors","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"United Nations University Institute for Water, Environment, and Health","funders":"","keywords":"Renewable energy; Ensemble forecasting; Computer science; Energy (signal processing); Ensemble learning; Meteorology; Artificial intelligence; Environmental science; Geography; Statistics; Mathematics; Engineering","score_opus":0.02328072723867422,"score_gpt":0.22872134640352038,"score_spread":0.20544061916484616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392577905","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":[0.027041769,0.002045669,0.9572284,0.000040091832,0.0005205534,0.00041790368,0.00037515696,0.00041146923,0.011918993],"genre_scores_gemma":[0.97833997,0.000027641627,0.017568486,0.000010741699,0.00011878188,0.00047167,0.00089986407,0.00014921643,0.0024136526],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981198,0.00003325741,0.00084786373,0.00045979867,0.00020788613,0.0003314388],"domain_scores_gemma":[0.9985378,0.00045306163,0.00024609122,0.00045664894,0.00021709783,0.00008934079],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00029597097,0.00037724277,0.00048301363,0.00034361143,0.00010570988,0.000057701152,0.00028213172,0.0003626944,0.000029511786],"category_scores_gemma":[0.00006278378,0.00039112277,0.0002975105,0.0002952149,0.000051181603,0.00007780609,0.0001627242,0.00037967318,0.0000018005377],"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":[0.000033585977,0.00010074379,0.0032262418,0.005078769,0.00039494762,6.2396776e-7,0.0004907066,0.91612804,0.053311627,0.00087301753,0.00727013,0.013091556],"study_design_scores_gemma":[0.00040500722,0.00003839449,0.000021846392,0.001903335,0.00017823293,0.000001942704,0.00015272696,0.824387,0.15939493,0.0063395635,0.006736993,0.0004400003],"about_ca_topic_score_codex":0.0007217173,"about_ca_topic_score_gemma":0.0005389576,"teacher_disagreement_score":0.9512982,"about_ca_system_score_codex":0.00011204178,"about_ca_system_score_gemma":0.00012505267,"threshold_uncertainty_score":0.9998541},"labels":[],"label_agreement":null},{"id":"W4392579546","doi":"10.5194/egusphere-egu24-9828","title":"Predicting Vp/Vs in North America with a Machine Learning Approach","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Computer science; Artificial intelligence; Machine learning","score_opus":0.008978436411621538,"score_gpt":0.18889253028052183,"score_spread":0.17991409386890028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392579546","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":[0.7314781,0.002239983,0.011347202,0.000029407522,0.0005746651,0.0002700514,0.00003102543,0.0024524203,0.25157714],"genre_scores_gemma":[0.98881495,0.0001279796,0.009284537,0.000019047126,0.00022844828,0.0000816527,0.00027241473,0.00014280614,0.0010281376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998574,0.000029760542,0.00032940164,0.00045756827,0.00020541015,0.00040383523],"domain_scores_gemma":[0.9995548,0.000046019115,0.0000489686,0.00024942635,0.00001655752,0.00008423831],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00011341631,0.0004008887,0.00040931813,0.00026901579,0.000040163417,0.00011116164,0.00021865839,0.00017048596,0.00004367392],"category_scores_gemma":[0.000018187642,0.00033102918,0.000078310084,0.0004323918,0.000027592301,0.000041235387,0.0004420215,0.0024461893,0.00002240356],"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":[0.000008908016,0.00001199375,0.038080867,0.00080557284,0.00009289495,0.000042092124,0.001491378,0.9537752,0.000006331884,0.000035676847,0.00004078762,0.0056082774],"study_design_scores_gemma":[0.00013647963,0.000039053535,0.0005740886,0.00043297076,0.00003372293,0.000018355926,0.00012539813,0.99507385,0.000027000415,0.00003203241,0.003091193,0.00041585744],"about_ca_topic_score_codex":0.00076101965,"about_ca_topic_score_gemma":0.0015111427,"teacher_disagreement_score":0.25733688,"about_ca_system_score_codex":0.000093254916,"about_ca_system_score_gemma":0.00003925008,"threshold_uncertainty_score":0.99991417},"labels":[],"label_agreement":null},{"id":"W4392582818","doi":"10.5267/j.msl.2024.2.003","title":"Hybrid optimization model with Neural Network approach for renewable energy prediction and scheduling in large scale systems","year":2024,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"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":"Computer science; Scheduling (production processes); Renewable energy; Artificial neural network; Scale (ratio); Industrial engineering; Artificial intelligence; Mathematical optimization; Operations research; Mathematics; Engineering","score_opus":0.007519698189413437,"score_gpt":0.18009195831432834,"score_spread":0.1725722601249149,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392582818","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":[0.038605306,0.00019039522,0.95891684,0.000055128497,0.00030199747,0.00014396751,0.0000034746813,0.00018435704,0.0015985044],"genre_scores_gemma":[0.92723644,0.000025134841,0.07232676,0.000099706034,0.00009348751,0.00008383646,0.000020335314,0.000018818308,0.00009551087],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991278,0.0000056684153,0.000119652876,0.00026682514,0.0001533758,0.0003266922],"domain_scores_gemma":[0.9998469,0.000008339418,0.000011925718,0.0000924426,0.0000047332983,0.00003569422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003385438,0.00009571098,0.00007554563,0.00017924441,0.00012180007,0.00023584413,0.00009766596,0.000013601981,3.529964e-7],"category_scores_gemma":[0.0000011050321,0.000087022985,0.000011853122,0.00041496655,0.000039452956,0.0004366615,0.00003472503,0.00004639394,8.135553e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000002995958,0.0000032802623,0.00040000444,0.00016789655,0.000007167645,0.0000023308778,0.00006526577,0.99821085,0.00013422567,0.0005057174,0.0003097019,0.0001905386],"study_design_scores_gemma":[0.00013827998,0.0000093969065,0.000028044142,0.00012361507,0.000010957156,0.0000035783967,0.000059223683,0.9993279,0.00003430823,0.000014049907,0.00015288558,0.0000977711],"about_ca_topic_score_codex":0.000021980357,"about_ca_topic_score_gemma":0.0000074126765,"teacher_disagreement_score":0.8886311,"about_ca_system_score_codex":0.000059331272,"about_ca_system_score_gemma":0.0000025111508,"threshold_uncertainty_score":0.35486943},"labels":[],"label_agreement":null},{"id":"W4392597103","doi":"10.5194/egusphere-egu24-1432","title":"Mapping wind speed distribution across large regions using machine learning and asymmetric kernel estimators.","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Institut National de la Recherche Scientifique","funders":"","keywords":"Estimator; Kernel (algebra); Distribution (mathematics); Wind speed; Econometrics; Computer science; Mathematics; Artificial intelligence; Statistics; Statistical physics; Geography; Meteorology; Physics; Mathematical analysis; Discrete mathematics","score_opus":0.021105591776849338,"score_gpt":0.2674809816548835,"score_spread":0.24637538987803415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392597103","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":[0.8876032,0.010246248,0.09169548,0.000048677033,0.0027714998,0.00023074169,0.0005061545,0.0017579973,0.005139953],"genre_scores_gemma":[0.99549806,0.00033152165,0.0022410336,0.000009313301,0.00032094808,0.0000018963552,0.0006955165,0.000117656615,0.00078402745],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99812156,0.00003455367,0.00043106286,0.00050744665,0.000210534,0.0006948464],"domain_scores_gemma":[0.9993965,0.00009089558,0.00008376085,0.00022878921,0.00004315406,0.00015691499],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004492176,0.0004633566,0.00043604916,0.00023673668,0.00029510682,0.00036831634,0.00014741023,0.0004038406,0.00003116454],"category_scores_gemma":[0.0001053275,0.00047037096,0.0001470421,0.0005244336,0.000040558156,0.000081777325,0.0011347972,0.0016468059,0.000026746486],"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":[0.000010954828,0.000054036955,0.023845173,0.0041400483,0.0007370965,0.00023279693,0.0026974948,0.95127314,0.002017369,0.004719367,0.00087790715,0.009394626],"study_design_scores_gemma":[0.00020859428,0.000008019875,0.00041780545,0.0008030387,0.00005494996,0.000078969424,0.000181434,0.98720855,0.00035156286,0.0007915407,0.009343109,0.00055245403],"about_ca_topic_score_codex":0.00027762147,"about_ca_topic_score_gemma":0.000042671243,"teacher_disagreement_score":0.10789484,"about_ca_system_score_codex":0.0002112716,"about_ca_system_score_gemma":0.00003215427,"threshold_uncertainty_score":0.9997748},"labels":[],"label_agreement":null},{"id":"W4392623600","doi":"10.5194/egusphere-egu24-16157","title":"Enhancing Renewable Energy Forecasting: A Comprehensive Evaluation of Weather Forecast Models and Post-Processing Methods for Belgium","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"United Nations University Institute for Water, Environment, and Health","funders":"","keywords":"Renewable energy; Meteorology; Probabilistic forecasting; Weather forecasting; Tropical cyclone forecast model; Technology forecasting; Weather prediction; Environmental science; Computer science; Operations research; Climatology; Engineering; Artificial intelligence; Geography; Geology","score_opus":0.08376862788512825,"score_gpt":0.32933531482013345,"score_spread":0.24556668693500522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392623600","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":[0.021499442,0.023534244,0.931363,0.00003381682,0.0014354947,0.0005566543,0.00008288001,0.00042482794,0.021069678],"genre_scores_gemma":[0.69743645,0.00012657512,0.30066925,0.000045468787,0.00035100698,0.00035019347,0.0001642568,0.00022445034,0.0006323561],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976247,0.00010983591,0.0007813953,0.0006312185,0.00034102856,0.000511798],"domain_scores_gemma":[0.9980644,0.00036278664,0.00020073334,0.0003433527,0.00090578373,0.00012294693],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0012844767,0.00055044773,0.0007429833,0.0003756545,0.00010138423,0.00014317602,0.00020482214,0.0004535181,0.000029690655],"category_scores_gemma":[0.000103528764,0.00052960095,0.00023524517,0.00020771474,0.000054839467,0.00015867189,0.0004925563,0.00035650874,3.473863e-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":[0.00001766529,0.000009490946,8.622264e-7,0.0025655497,0.00020161635,6.958781e-7,0.0011822061,0.8086978,0.015216271,0.0006634848,0.00014989323,0.17129445],"study_design_scores_gemma":[0.0003220598,0.000049313752,0.0000011606487,0.0015709837,0.0004660895,0.000018737059,0.00031427905,0.8754522,0.048928123,0.07168619,0.0007339921,0.00045686774],"about_ca_topic_score_codex":0.0007631155,"about_ca_topic_score_gemma":0.00063561334,"teacher_disagreement_score":0.675937,"about_ca_system_score_codex":0.00016124242,"about_ca_system_score_gemma":0.00023395495,"threshold_uncertainty_score":0.99971557},"labels":[],"label_agreement":null},{"id":"W4392628723","doi":"10.26868/25222708.2023.1400","title":"Ensemble transfer learning strategy in forecasting power consumption for residential buildings","year":2023,"lang":"en","type":"article","venue":"Building Simulation Conference proceedings","topic":"Energy Load and Power Forecasting","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","keywords":"Computer science; Ensemble forecasting; Transfer of learning; Ensemble learning; Artificial intelligence; Machine learning; Power consumption; Predictive power; Deep learning; Energy consumption; Long short term memory; Data modeling; Predictive modelling; Power (physics); Transfer (computing); Recurrent neural network; Artificial neural network; Engineering; Database","score_opus":0.05504695749078569,"score_gpt":0.2839213225859204,"score_spread":0.2288743650951347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392628723","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":[0.91816497,0.000036888447,0.078366116,0.000026117197,0.0002492019,0.0003619608,0.0000047920653,0.0009653782,0.0018246041],"genre_scores_gemma":[0.9981798,0.000023460669,0.0012730359,0.000010896072,0.00013512986,0.00009029623,0.000027323831,0.000079541365,0.00018053113],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982087,0.000010309039,0.0005166185,0.0003941204,0.00024555504,0.00062467146],"domain_scores_gemma":[0.9992833,0.00029570417,0.000059957587,0.00006336418,0.00020677132,0.00009088641],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005809234,0.00027940265,0.00027903583,0.0005228544,0.0002151439,0.00026216477,0.00016415643,0.0002010312,0.000052230145],"category_scores_gemma":[0.00025482345,0.00032921042,0.00008793221,0.0006143363,0.00003391421,0.0006615556,0.000029154111,0.00033138195,0.0000151177355],"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":[0.000040524745,0.0000073444976,0.008378112,0.00024281895,0.000017729179,0.0000027757792,0.001499855,0.93152094,0.042466022,0.009624555,0.000086484484,0.0061128205],"study_design_scores_gemma":[0.00077161886,0.0000702266,0.0018526277,0.00036053747,0.000015495008,0.0000047312083,0.0003412771,0.98431253,0.008408975,0.0021494096,0.0013283473,0.00038421978],"about_ca_topic_score_codex":0.000014848803,"about_ca_topic_score_gemma":0.000015390904,"teacher_disagreement_score":0.08001484,"about_ca_system_score_codex":0.000089880734,"about_ca_system_score_gemma":0.000028930812,"threshold_uncertainty_score":0.999916},"labels":[],"label_agreement":null},{"id":"W4392628937","doi":"10.26868/25222708.2023.1376","title":"Surrogate Modeling performance for building design problems","year":2023,"lang":"en","type":"article","venue":"Building Simulation Conference proceedings","topic":"Energy Load and Power Forecasting","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":"Alberta Energy; University of Victoria","funders":"","keywords":"Surrogate model; Computer science; Energy performance; Efficient energy use; Surrogate endpoint; Energy (signal processing); Reliability engineering; Artificial intelligence; Machine learning; Engineering; Mathematics","score_opus":0.07654674412662912,"score_gpt":0.2737537038733637,"score_spread":0.19720695974673455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392628937","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":[0.53783065,0.000026728254,0.4600518,0.000019997557,0.00022937436,0.00028917188,0.00000283781,0.001212338,0.0003370944],"genre_scores_gemma":[0.9756313,0.000056543468,0.023749715,0.000013656633,0.00020483474,0.00014434214,0.000013151282,0.00008476524,0.00010168988],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99841785,0.00000511292,0.00040415162,0.00034703038,0.00023210423,0.0005937403],"domain_scores_gemma":[0.9992316,0.00018482271,0.00007447985,0.000089593996,0.00031946957,0.000100049714],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00062854256,0.00027479292,0.00023889949,0.00031189746,0.000299557,0.00022860547,0.00023225223,0.00013684809,0.00001098886],"category_scores_gemma":[0.00015243437,0.00029824127,0.0000700601,0.00062480377,0.000020784682,0.00069328217,0.00004575851,0.00017604475,0.000014921864],"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":[0.000011818838,0.000003609275,0.0005925599,0.0003020362,0.000018741268,2.2649856e-7,0.00069986214,0.97540706,0.013449238,0.002643656,0.000063422594,0.0068077664],"study_design_scores_gemma":[0.00036434975,0.000043568423,0.000052332918,0.00039368166,0.000017426426,0.0000018976559,0.00006907554,0.9888954,0.006173636,0.0027465671,0.0008776302,0.0003644019],"about_ca_topic_score_codex":0.0000033801966,"about_ca_topic_score_gemma":4.2776009e-7,"teacher_disagreement_score":0.43780065,"about_ca_system_score_codex":0.000070789596,"about_ca_system_score_gemma":0.000029036653,"threshold_uncertainty_score":0.99994695},"labels":[],"label_agreement":null},{"id":"W4392644853","doi":"10.5194/egusphere-egu24-20474","title":"CAMEMBERT: A Mini-Neptunes General Circulation Model Intercomparison","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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 Victoria","funders":"","keywords":"Circulation (fluid dynamics); General Circulation Model; Environmental science; Climatology; Physics; Geology; Oceanography; Mechanics; Climate change","score_opus":0.02756373536975101,"score_gpt":0.24983340399194448,"score_spread":0.22226966862219347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392644853","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":[0.7101723,0.0024106542,0.06625428,0.000083764506,0.004484835,0.00017917278,0.000037706952,0.0020269486,0.21435033],"genre_scores_gemma":[0.9872906,0.000049117065,0.008536189,0.00003611768,0.0005418991,0.000044764605,0.00012147232,0.00010189923,0.0032779295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884415,0.000011319764,0.00036694208,0.00035176633,0.00015606725,0.0002697414],"domain_scores_gemma":[0.99952143,0.000018339158,0.000030370538,0.00032246253,0.000028014609,0.00007937977],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000078518104,0.00034710474,0.00031764552,0.00017762308,0.000025477626,0.00014524633,0.000189153,0.00033250393,0.00009239961],"category_scores_gemma":[0.0000058551273,0.0003358636,0.00018815027,0.000087975575,0.000015505873,0.00004529067,0.00038798383,0.00070314074,0.000102725186],"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":[0.0000013469873,0.00000619268,0.000089707224,0.0004127668,0.00009010394,0.000005568897,0.00060668535,0.98575294,0.00076064054,0.0023211867,0.0069041266,0.0030487068],"study_design_scores_gemma":[0.000051870735,0.0000032703797,0.000036558195,0.00030123102,0.000048330974,0.0000062668814,0.000018285513,0.99031955,0.0009328859,0.0063916165,0.0015099712,0.00038016614],"about_ca_topic_score_codex":0.00015237762,"about_ca_topic_score_gemma":0.00023516105,"teacher_disagreement_score":0.2771183,"about_ca_system_score_codex":0.00011285124,"about_ca_system_score_gemma":0.000037802896,"threshold_uncertainty_score":0.99990934},"labels":[],"label_agreement":null},{"id":"W4392652724","doi":"10.5194/egusphere-egu24-19297","title":"Machine Learning Approaches to Improve Accuracy in Extreme Seasonal Temperature Forecasts: A Multi-Model Assessment&amp;#160;","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Computer science; Machine learning; Climatology; Artificial intelligence; Econometrics; Economics; Geology","score_opus":0.1314127795528083,"score_gpt":0.2803176119919021,"score_spread":0.14890483243909378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392652724","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":[0.67251706,0.011271094,0.1842698,0.0012244886,0.0065018707,0.0031894145,0.0010957771,0.0061592516,0.113771215],"genre_scores_gemma":[0.87985027,0.00011625337,0.11030984,0.000079045734,0.00040104153,0.00037399802,0.00062128773,0.00026486194,0.007983387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99710584,0.0000641705,0.0006502363,0.0010134204,0.00038813666,0.0007781859],"domain_scores_gemma":[0.998934,0.00014483795,0.000075982316,0.0005317248,0.000043243548,0.0002702269],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0004952618,0.0008595863,0.00069102115,0.0004689699,0.00008460936,0.00045709527,0.0005114029,0.00068702083,0.00012881428],"category_scores_gemma":[0.00012356926,0.0007944125,0.00028269427,0.0003995249,0.00002329252,0.0001535016,0.0013229038,0.0043201265,0.000054594308],"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":[0.000012507478,0.000054739838,0.00050997006,0.0009894907,0.00014781505,0.000024118784,0.0010413637,0.976472,0.004009964,0.0013023447,0.00040190888,0.0150337275],"study_design_scores_gemma":[0.0003673744,0.000020507898,0.0000862584,0.0007960866,0.00004348014,0.000011605271,0.000086332526,0.993496,0.00037308937,0.00079956726,0.0030309234,0.0008887805],"about_ca_topic_score_codex":0.00016986387,"about_ca_topic_score_gemma":0.001357211,"teacher_disagreement_score":0.2073332,"about_ca_system_score_codex":0.00043222046,"about_ca_system_score_gemma":0.00020041387,"threshold_uncertainty_score":0.9994507},"labels":[],"label_agreement":null},{"id":"W4392654206","doi":"10.5194/egusphere-egu24-18122","title":"Enhancing SVM&amp;#8217;s robustness of weekly streamflow prediction based on three feature selection algorithms","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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 du Québec à Trois-Rivières","funders":"","keywords":"Robustness (evolution); Feature selection; Support vector machine; Computer science; Selection (genetic algorithm); Artificial intelligence; Algorithm; Machine learning; Pattern recognition (psychology); Chemistry","score_opus":0.0126987267954229,"score_gpt":0.21817760362762942,"score_spread":0.20547887683220653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392654206","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":[0.13027166,0.0007761189,0.8276579,0.00015812706,0.011469519,0.0005501357,0.00031321344,0.00307476,0.02572859],"genre_scores_gemma":[0.95392084,0.000060353883,0.040588293,0.000019185942,0.0021255612,0.000104939696,0.0006530546,0.00023463747,0.0022931506],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982232,0.000031216816,0.00044356036,0.0005394087,0.00040411772,0.0003585158],"domain_scores_gemma":[0.99919075,0.000096898366,0.00009070485,0.00041510587,0.00011106489,0.00009549648],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00025381576,0.00050947233,0.00045487803,0.00041262005,0.00007136302,0.00009188891,0.00020508627,0.00075708516,0.0001355368],"category_scores_gemma":[0.00003052889,0.00048416987,0.00024486578,0.0003860233,0.000022572254,0.00006309286,0.00013425767,0.0015229597,0.000015706886],"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":[0.000014530333,0.000029220166,0.00012540091,0.0013398858,0.00013591295,0.000002380402,0.00006774236,0.9845666,0.002561992,0.000063438114,0.0021905059,0.008902371],"study_design_scores_gemma":[0.00017846236,0.00007058997,0.00020643536,0.0019853322,0.00011914338,0.000007186853,0.0000176413,0.9716053,0.024256496,0.00018804272,0.00097476767,0.0003905885],"about_ca_topic_score_codex":0.00012529473,"about_ca_topic_score_gemma":0.0011097692,"teacher_disagreement_score":0.82364917,"about_ca_system_score_codex":0.00024495643,"about_ca_system_score_gemma":0.00010556569,"threshold_uncertainty_score":0.999761},"labels":[],"label_agreement":null},{"id":"W4392924130","doi":"10.21203/rs.3.rs-4094819/v1","title":"Willow Algorithm for Consumption-Investment under Stochastic Volatility Model with Jump Diffusion","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; National Science Foundation","keywords":"Jump diffusion; Stochastic volatility; Volatility (finance); Jump; Willow; Diffusion; Computer science; Econometrics; Algorithm; Economics; Thermodynamics; Physics","score_opus":0.06362716752142841,"score_gpt":0.3399049201610956,"score_spread":0.27627775263966714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392924130","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":[0.12523963,0.0039936895,0.86432195,0.00012533502,0.0007436116,0.0019742998,0.00094362616,0.0007916821,0.0018662147],"genre_scores_gemma":[0.9729552,0.00021298906,0.022075757,0.00002785588,0.00045778972,0.0013262564,0.0006300338,0.00024713346,0.00206697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99732393,0.00007043029,0.00032447182,0.00066631456,0.0008455621,0.0007692619],"domain_scores_gemma":[0.99844396,0.00035050802,0.00003219439,0.0006142034,0.00029017995,0.0002689505],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006805683,0.00040513984,0.0003767998,0.0003751613,0.00021413653,0.00020683421,0.00029783236,0.000357087,0.000049930735],"category_scores_gemma":[0.00004564884,0.00034122192,0.00016441985,0.0002113719,0.00015906448,0.000059249138,0.0006095556,0.001647485,0.000039851613],"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":[0.000025810823,0.00005283462,0.000043791435,0.003366299,0.00014400341,0.0000080984455,0.00042546928,0.9834655,0.000095146264,0.0019347562,0.0012683456,0.009169947],"study_design_scores_gemma":[0.00033193413,0.00009369553,0.000109421424,0.0022260584,0.000042731852,0.0000038558983,0.000066902074,0.97489053,0.00010168791,0.021499636,0.00027658566,0.00035697414],"about_ca_topic_score_codex":0.00006386581,"about_ca_topic_score_gemma":0.0001021625,"teacher_disagreement_score":0.8477156,"about_ca_system_score_codex":0.0005976328,"about_ca_system_score_gemma":0.00031058135,"threshold_uncertainty_score":0.999904},"labels":[],"label_agreement":null},{"id":"W4392938633","doi":"10.1016/j.engappai.2024.108201","title":"Short term wind speed forecasting using artificial and wavelet neural networks with and without wavelet filtered data based on feature selections technique","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Wavelet; Artificial neural network; Term (time); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Wavelet transform","score_opus":0.043919669350426994,"score_gpt":0.26909228693326903,"score_spread":0.22517261758284204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392938633","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":[0.12222738,0.00025024422,0.8763779,0.000033733773,0.00015006629,0.00043656057,0.00006750511,0.00039109366,0.00006551539],"genre_scores_gemma":[0.9613959,0.000014928246,0.038127836,0.0000048062266,0.00026715972,0.000029694917,0.00008252899,0.0000718596,0.0000052740697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987778,0.00001395391,0.00033272762,0.00043164974,0.00014326439,0.00030061786],"domain_scores_gemma":[0.9992133,0.00016788005,0.000031739182,0.00044007116,0.000046343688,0.00010066638],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024223053,0.0002731833,0.0002257186,0.0002595303,0.00015577876,0.00013783277,0.00021309017,0.00013086777,0.0000035303494],"category_scores_gemma":[0.00003121364,0.00026826598,0.000028499451,0.0006526514,0.00008437681,0.00018182749,0.00006182807,0.00050950516,5.692198e-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":[0.000013619997,0.000018003655,0.00009409395,0.00015518449,0.000037788424,0.0000038571984,0.00007759154,0.92394006,0.021657323,0.00086831494,0.00001140935,0.05312276],"study_design_scores_gemma":[0.000012266734,0.000045995694,0.00007244194,0.00028740868,0.000047284244,0.000066443776,0.00003455785,0.97667205,0.022270264,0.00007254527,0.00014915783,0.00026957117],"about_ca_topic_score_codex":0.000014780624,"about_ca_topic_score_gemma":0.000020824038,"teacher_disagreement_score":0.83916855,"about_ca_system_score_codex":0.000037273254,"about_ca_system_score_gemma":0.000020473193,"threshold_uncertainty_score":0.99997693},"labels":[],"label_agreement":null},{"id":"W4392943060","doi":"10.1109/icetsis61505.2024.10459633","title":"A Proposed Hybrid Deep Learning Model for Wind Power Forecasting","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Wind power forecasting; Wind power; Computer science; Deep learning; Power (physics); Artificial intelligence; Meteorology; Electric power system; Engineering; Electrical engineering; Geography","score_opus":0.01895000285623004,"score_gpt":0.21595240761040466,"score_spread":0.19700240475417463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392943060","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":[0.102413125,0.00094103767,0.8186721,0.00003369678,0.0006835566,0.00020861236,0.0000067484925,0.0018716492,0.07516942],"genre_scores_gemma":[0.9802279,0.000008548216,0.01562067,0.000023460738,0.00014390095,0.000013553608,0.000017137952,0.00009331901,0.00385155],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991083,0.0000058459036,0.0002004782,0.00020887732,0.00010197283,0.00037449753],"domain_scores_gemma":[0.9996907,0.000107839216,0.00001092646,0.00009110234,0.000027251643,0.000072164265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016277414,0.00017497984,0.00013688211,0.00009894858,0.000097184944,0.00012591889,0.000087997454,0.000051127558,0.00007797051],"category_scores_gemma":[0.000045559995,0.0001564729,0.00010446222,0.00011989359,0.000013150016,0.00019645112,0.000023774815,0.00021315954,0.000022698267],"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":[0.0000044050466,0.000003716467,0.000018132796,0.00017103681,0.000042907486,0.000013485237,0.0006026216,0.9640048,0.0015369342,0.0024179448,0.00053308764,0.0306509],"study_design_scores_gemma":[0.00012994593,0.00003475833,0.0000013515981,0.000112416106,0.000014896606,0.000030084544,0.00003427612,0.987945,0.002857664,0.0010714666,0.0075474274,0.0002206967],"about_ca_topic_score_codex":0.0000023676348,"about_ca_topic_score_gemma":0.000006625787,"teacher_disagreement_score":0.8778147,"about_ca_system_score_codex":0.000042556105,"about_ca_system_score_gemma":0.000018042176,"threshold_uncertainty_score":0.638078},"labels":[],"label_agreement":null},{"id":"W4392944178","doi":"10.1109/icetsis61505.2024.10459413","title":"Analyzing the Performance of Direct and Indirect Net Load Forecasting Strategies Under Varying Penetration Levels of PV and Wind Power","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Wind power; Penetration (warfare); Computer science; Power (physics); Environmental science; Automotive engineering; Control theory (sociology); Econometrics; Electrical engineering; Engineering; Operations research; Economics; Artificial intelligence; Physics","score_opus":0.023092830302993656,"score_gpt":0.22354296419665542,"score_spread":0.20045013389366176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392944178","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":[0.96090627,0.0040595857,0.001301523,0.000013377284,0.00013199857,0.00005459947,0.000005689837,0.00008105782,0.03344589],"genre_scores_gemma":[0.99931616,0.00017743354,0.00036830214,0.0000038974426,0.000033234246,0.0000013568979,0.0000016149792,0.000019261317,0.00007876776],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993437,0.000016462347,0.00024189542,0.00013205575,0.00011036167,0.00015555126],"domain_scores_gemma":[0.9996111,0.00021331526,0.000034745703,0.000085809545,0.00002940899,0.000025662677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030958618,0.00013018599,0.00016718142,0.000088988185,0.00006604192,0.00009044952,0.000054327997,0.000051230803,0.00003470397],"category_scores_gemma":[0.000013481165,0.00009306939,0.000028186898,0.00023133314,0.000058678434,0.0003774737,0.000029317502,0.00011471513,3.067646e-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":[0.000024963283,0.000015127284,0.014755992,0.0019829266,0.00051601423,0.000007348152,0.00962625,0.76183337,0.15253378,0.004228615,0.00009194185,0.054383695],"study_design_scores_gemma":[0.00020471273,0.00012986436,0.0202662,0.00084279873,0.000076952994,0.000041560616,0.0007977498,0.8868595,0.09004288,0.000274618,0.00015526003,0.0003079109],"about_ca_topic_score_codex":0.000031924807,"about_ca_topic_score_gemma":0.000030726977,"teacher_disagreement_score":0.12502615,"about_ca_system_score_codex":0.000016264055,"about_ca_system_score_gemma":0.00003329764,"threshold_uncertainty_score":0.37952596},"labels":[],"label_agreement":null},{"id":"W4392980596","doi":"10.1109/eesat59125.2024.10471219","title":"Assessing the Impacts of Energy Storage Systems on the Transmission System Expansion Planning with a Quasi-Static Time Series Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Computer science; Series (stratigraphy); Energy storage; Transmission system; Energy (signal processing); Transmission (telecommunications); Geology; Telecommunications; Mathematics; Physics","score_opus":0.015381160644255825,"score_gpt":0.2243183522595224,"score_spread":0.20893719161526658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392980596","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":[0.46601784,0.004335676,0.46597758,0.0001020463,0.00047905886,0.00026636722,0.000007661859,0.0011113224,0.061702464],"genre_scores_gemma":[0.9990184,0.000010782886,0.000460917,0.000009750001,0.000077080185,0.000024382747,0.0000092376595,0.000054306143,0.00033516955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899465,0.000105424544,0.00024732263,0.00015601504,0.0002707285,0.0002258719],"domain_scores_gemma":[0.99935836,0.00031718786,0.00003730824,0.00021641189,0.000019764717,0.000050953982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037570062,0.0002019479,0.00021007279,0.000078977515,0.00014615423,0.00026777992,0.00013852924,0.000060982842,0.0000076938795],"category_scores_gemma":[0.000005526023,0.0000875831,0.000050892337,0.00028297663,0.000033856344,0.00026961465,0.00001069992,0.00017356378,0.0000028459312],"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":[0.000020542411,0.000018947261,0.000014669139,0.0020612685,0.00013554505,0.00003818296,0.0052418998,0.9648466,0.009215101,0.016331393,0.0005305745,0.0015452837],"study_design_scores_gemma":[0.00007260299,0.00010882511,0.00001800636,0.00416344,0.00003472703,0.000102298625,0.0066263536,0.9835692,0.004004544,0.000006107886,0.0011442318,0.00014965952],"about_ca_topic_score_codex":0.000059125614,"about_ca_topic_score_gemma":9.965728e-7,"teacher_disagreement_score":0.5330005,"about_ca_system_score_codex":0.000057621713,"about_ca_system_score_gemma":0.00003502348,"threshold_uncertainty_score":0.35715353},"labels":[],"label_agreement":null},{"id":"W4393066021","doi":"10.1109/tpec60005.2024.10472180","title":"Cascaded Ensemble-Based Short-Term Load Forecasting for Smart Energy Management","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Lakehead University","funders":"","keywords":"Term (time); Computer science; Energy management; Energy (signal processing); Statistics","score_opus":0.0262929758820706,"score_gpt":0.22900605424603585,"score_spread":0.20271307836396524,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393066021","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":[0.019561157,0.0015401858,0.76905113,0.00007285757,0.002249712,0.00023309587,0.000018048795,0.0021143786,0.20515943],"genre_scores_gemma":[0.98666674,0.000031382133,0.008660399,0.00009654509,0.00029145172,0.0001425415,0.000048143465,0.00009751726,0.0039652935],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987747,0.000007845202,0.0002795133,0.00029144337,0.00018202464,0.00046443744],"domain_scores_gemma":[0.999529,0.00014168354,0.000009012146,0.00019373222,0.000028993874,0.00009758228],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016507214,0.00024657563,0.00017519384,0.00014871993,0.00008072834,0.00013399549,0.00013801972,0.000091305235,0.000076267854],"category_scores_gemma":[0.0000066702237,0.0002296252,0.00015605133,0.00021647944,0.000014754674,0.00011608046,0.000031118456,0.00009634905,0.000013211372],"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":[0.00003947598,0.000035737874,0.00019237575,0.0018708139,0.00050092343,0.0003487511,0.00027195213,0.107507244,0.0069530387,0.046813488,0.020397067,0.81506914],"study_design_scores_gemma":[0.00025539278,0.000039284925,0.00001642339,0.00032048873,0.000058842943,0.000020292735,0.000021094069,0.8398556,0.037538268,0.0003125008,0.121200785,0.00036104306],"about_ca_topic_score_codex":0.000022326538,"about_ca_topic_score_gemma":0.00014422918,"teacher_disagreement_score":0.96710557,"about_ca_system_score_codex":0.00013301174,"about_ca_system_score_gemma":0.000022465007,"threshold_uncertainty_score":0.93638444},"labels":[],"label_agreement":null},{"id":"W4393146183","doi":"10.1109/hpcc-dss-smartcity-dependsys60770.2023.00115","title":"A Comprehensive Analysis of a Hybrid Deep Learning Model for Midterm Electric Load Forecasting","year":2023,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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; Concordia University","keywords":"Computer science; Technology forecasting; Artificial intelligence","score_opus":0.032654191625333784,"score_gpt":0.23745779803841024,"score_spread":0.20480360641307646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393146183","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":[0.6752397,0.00019328172,0.31979635,0.000004542878,0.000065461056,0.00009281581,0.000008405245,0.0005153525,0.0040841005],"genre_scores_gemma":[0.9953737,0.00004397026,0.0038600175,0.000013994941,0.000033022432,0.000030091964,0.00006290551,0.00004028229,0.0005420006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895895,0.000010737545,0.00030458212,0.0001820826,0.00015547728,0.00038818058],"domain_scores_gemma":[0.99935156,0.00028562333,0.000055450026,0.0001264558,0.00012317918,0.00005770312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013517113,0.00015829274,0.00035160422,0.00054242177,0.0000798435,0.000019299125,0.000112146976,0.000036899746,0.00002094762],"category_scores_gemma":[0.00007013627,0.0001598381,0.000247088,0.00162044,0.000010603151,0.00007569723,0.000030933767,0.00011858009,0.0000035555954],"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":[0.000008078065,0.000004281818,0.00036765184,0.00009085858,0.0005191247,0.0000037733091,0.0003699387,0.97901934,0.006129147,0.00011210732,0.00009648326,0.0132792],"study_design_scores_gemma":[0.00020760566,0.00002956233,0.00013349614,0.000018786475,0.00027238028,0.000003045904,0.00005466797,0.9943696,0.004336775,0.00015554878,0.00024407786,0.00017443564],"about_ca_topic_score_codex":0.000017567005,"about_ca_topic_score_gemma":0.000036895566,"teacher_disagreement_score":0.32013404,"about_ca_system_score_codex":0.00004849392,"about_ca_system_score_gemma":0.000015409314,"threshold_uncertainty_score":0.6518009},"labels":[],"label_agreement":null},{"id":"W4393191404","doi":"10.1145/3653980","title":"Explainable finite mixture of mixtures of bounded asymmetric generalized Gaussian and Uniform distributions learning for energy demand management","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Intelligent Systems and Technology","topic":"Energy Load and Power Forecasting","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":"Concordia University","funders":"","keywords":"Computer science; Bounded function; Gaussian; Mixture model; Energy (signal processing); Gaussian process; Mathematical optimization; Applied mathematics; Artificial intelligence; Mathematics; Statistics; Physics; Mathematical analysis","score_opus":0.00954383941726799,"score_gpt":0.22166796202049796,"score_spread":0.21212412260322996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393191404","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":[0.017808978,0.011716709,0.9690084,0.0000937166,0.00040768133,0.00018831139,0.00006235377,0.00023220213,0.00048168277],"genre_scores_gemma":[0.9935859,0.003915635,0.0018301829,0.0000018592611,0.000016302696,0.00012099455,0.0000240282,0.00002313974,0.00048197396],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99920785,0.0000138578125,0.00032679233,0.00018446456,0.00007141485,0.00019564127],"domain_scores_gemma":[0.9995409,0.00015689766,0.00004176583,0.00018335067,0.000039561026,0.00003752895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000108313565,0.00015420605,0.00025280856,0.00073637936,0.0001232013,0.000030223337,0.0001038804,0.00018845659,0.000005795528],"category_scores_gemma":[0.000016746664,0.00013797452,0.00005896892,0.0006408889,0.000074649295,0.000052025105,0.000008816784,0.00015348179,3.557906e-7],"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":[0.000054470423,0.000117156575,0.0001118993,0.004871294,0.0014289467,0.00002056911,0.00040596206,0.13956553,0.003644287,0.59099805,0.00021317249,0.25856867],"study_design_scores_gemma":[0.001105693,0.00097237824,0.000026381089,0.0022733493,0.0005815498,0.00017364151,0.0021454012,0.34161457,0.34361917,0.026176522,0.28049687,0.00081447716],"about_ca_topic_score_codex":0.00006424275,"about_ca_topic_score_gemma":0.000023192697,"teacher_disagreement_score":0.9757769,"about_ca_system_score_codex":0.000031399864,"about_ca_system_score_gemma":0.000007252618,"threshold_uncertainty_score":0.56264377},"labels":[],"label_agreement":null},{"id":"W4393233644","doi":"10.18280/mmep.110321","title":"Optimizing Energy Consumption in Buildings: Intelligent Power Management Through Machine Learning","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"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":"Energy consumption; Energy management; Power consumption; Computer science; Energy (signal processing); Power management; Consumption (sociology); Power (physics); Architectural engineering; Automotive engineering; Engineering; Electrical engineering; Sociology","score_opus":0.02127392080172137,"score_gpt":0.21284763737820633,"score_spread":0.19157371657648498,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393233644","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":[0.016939143,0.009615418,0.96825594,0.000016217851,0.00022769302,0.00007983829,0.0000013036441,0.00085793657,0.0040064855],"genre_scores_gemma":[0.9245872,0.0026769354,0.07241705,0.000007307896,0.000033116332,0.000044716166,0.000007114264,0.0000856982,0.00014088207],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99883485,0.0000090805315,0.0003745169,0.00026355154,0.00013959824,0.00037842023],"domain_scores_gemma":[0.99969745,0.00010328996,0.000012695981,0.000107448825,0.0000065654153,0.00007252772],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002313981,0.00025723325,0.00023687769,0.00019417935,0.000042382795,0.00014512731,0.00008247468,0.000097092685,0.000037286114],"category_scores_gemma":[0.0000057214966,0.00024666992,0.000054580312,0.00019478844,0.000017438584,0.00016772206,0.000048394188,0.00034973072,0.000014679955],"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":[0.000001354967,0.000010983131,0.000004728042,0.0015514159,0.000047837482,0.000021099444,0.0015450704,0.9193404,0.00023073192,0.07513135,0.0000052527375,0.0021098051],"study_design_scores_gemma":[0.00009208027,0.000017764181,4.524904e-7,0.0020814226,0.000018393257,0.000026854963,0.000030496296,0.9849506,0.00046990512,0.0057734735,0.006262837,0.00027573024],"about_ca_topic_score_codex":0.000009869674,"about_ca_topic_score_gemma":8.6499176e-7,"teacher_disagreement_score":0.907648,"about_ca_system_score_codex":0.00006057928,"about_ca_system_score_gemma":0.0000018867452,"threshold_uncertainty_score":0.99999857},"labels":[],"label_agreement":null},{"id":"W4393254326","doi":"10.1016/j.jobe.2024.109163","title":"Optimizing electricity peak shaving through stochastic programming and probabilistic time series forecasting","year":2024,"lang":"en","type":"article","venue":"Journal of Building Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Series (stratigraphy); Probabilistic logic; Peaking power plant; Electricity; Time series; Computer science; Mathematical optimization; Econometrics; Engineering; Mathematics; Machine learning; Artificial intelligence; Electrical engineering","score_opus":0.010925680217880759,"score_gpt":0.20907372779550196,"score_spread":0.1981480475776212,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393254326","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":[0.47209668,0.012171484,0.51324105,0.00003902489,0.0013121419,0.00015352083,0.000003247435,0.00077763497,0.00020520414],"genre_scores_gemma":[0.9070663,0.000056336852,0.09213028,0.000003779739,0.00060239696,0.0000062083013,0.0000011628468,0.0001081059,0.000025427333],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99845856,0.000012902815,0.0005845881,0.00019226989,0.0002516658,0.0005000062],"domain_scores_gemma":[0.9993258,0.0002927059,0.000085385625,0.00010129924,0.000071067574,0.00012371296],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00048707283,0.00030451233,0.00038685568,0.00031935633,0.000096845855,0.00033116402,0.00016384105,0.00009855968,0.0000077251925],"category_scores_gemma":[0.00024974023,0.0002918874,0.00012746204,0.0004757338,0.000025516625,0.0009574239,0.000054742748,0.0006335491,0.0000011793578],"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":[0.000006221456,0.0000059241397,0.000009212261,0.00061573094,0.00011779049,0.0001248905,0.0009803108,0.9595122,0.028134,0.0006428302,0.00003895423,0.009811946],"study_design_scores_gemma":[0.00014731931,0.000109658984,0.00001554069,0.0024928085,0.000094850046,0.0018423442,0.000040652678,0.9912113,0.0022995372,0.00019509248,0.0011982066,0.0003526793],"about_ca_topic_score_codex":0.0000027587853,"about_ca_topic_score_gemma":6.456114e-7,"teacher_disagreement_score":0.4349696,"about_ca_system_score_codex":0.00017284298,"about_ca_system_score_gemma":0.000035265537,"threshold_uncertainty_score":0.9999533},"labels":[],"label_agreement":null},{"id":"W4394008336","doi":"10.1016/j.ijepes.2024.109975","title":"ATTnet: An explainable gated recurrent unit neural network for high frequency electricity price forecasting","year":2024,"lang":"en","type":"article","venue":"International Journal of Electrical Power & Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Electricity price forecasting; Artificial neural network; Electricity; Unit (ring theory); Computer science; Electricity price; Electricity market; Econometrics; Artificial intelligence; Economics; Engineering; Electrical engineering; Mathematics","score_opus":0.0210651182514274,"score_gpt":0.24743055427209157,"score_spread":0.22636543602066417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394008336","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":[0.5147569,0.04372616,0.39325508,0.00028775638,0.03977161,0.00039817212,0.000077172175,0.0008182545,0.0069088945],"genre_scores_gemma":[0.9950162,0.000142266,0.000917612,0.000052893756,0.003493062,0.000019242621,0.000054208413,0.000087125314,0.00021735673],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973158,0.000109316345,0.001027954,0.00026682363,0.00062550546,0.00065456546],"domain_scores_gemma":[0.99825305,0.000481413,0.00024607155,0.00013542583,0.00064735976,0.00023669054],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061723357,0.00031651757,0.00042790367,0.00046810057,0.00010245394,0.0003517506,0.00060273556,0.00017014443,0.00003254076],"category_scores_gemma":[0.00014755824,0.00028033028,0.00021967733,0.00074208644,0.000020558837,0.0006297147,0.000027097172,0.0005039478,0.0000024968313],"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":[0.0002509455,0.00013811268,0.0004409202,0.00008992324,0.0011165984,0.00061073684,0.0002112247,0.85531545,0.0039925035,0.097435184,0.008274624,0.032123797],"study_design_scores_gemma":[0.0006156841,0.0008953359,0.00007629921,0.0005307077,0.000062802574,0.0011947112,0.000020561283,0.92217046,0.001239639,0.0022764425,0.07045923,0.000458123],"about_ca_topic_score_codex":0.00014897464,"about_ca_topic_score_gemma":0.000022191372,"teacher_disagreement_score":0.48025933,"about_ca_system_score_codex":0.00041808444,"about_ca_system_score_gemma":0.00012186188,"threshold_uncertainty_score":0.9999649},"labels":[],"label_agreement":null},{"id":"W4394423949","doi":"10.6084/m9.figshare.13206008","title":"wind_data.txt","year":2020,"lang":"en","type":"dataset","venue":"Figshare","topic":"Energy Load and Power Forecasting","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":"Meteorology; Environmental science; Geography","score_opus":0.03011618957374279,"score_gpt":0.2174357169141386,"score_spread":0.1873195273403958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394423949","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[4.0213553e-8,0.00022637293,2.3504323e-7,0.000004450369,0.0002995483,0.000054938017,0.99710643,0.00030230873,0.002005691],"genre_scores_gemma":[0.0000020733016,0.00002505712,0.000020802798,0.00019815103,0.00089321606,0.000051515315,0.99872786,0.000042475545,0.000038828563],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99926245,0.000007502472,0.00016139442,0.00019540543,0.00013998359,0.00023323389],"domain_scores_gemma":[0.9994431,0.000049421364,0.000034063287,0.00033518992,0.000015374888,0.00012282457],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000008907759,0.00025447385,0.00021406308,0.000051881416,0.000034005247,0.0000647302,0.00040355063,0.0002525935,0.3515752],"category_scores_gemma":[0.00022471145,0.0002648427,0.000077072546,0.00012807474,0.0000017795691,0.000074174364,0.00013448899,0.00046333668,0.07162909],"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":[5.4871504e-7,0.0000020744935,2.0694664e-8,0.0006650367,0.000025294865,0.00006510486,0.000004444024,0.0005082684,7.8018724e-7,1.9021594e-7,0.99823403,0.00049422216],"study_design_scores_gemma":[0.00005265145,0.000009525177,0.0000011682903,0.0014621649,0.00001090205,0.0000072019675,0.0000011278627,0.00031573226,0.000037132162,0.0000015995903,0.99780273,0.00029807037],"about_ca_topic_score_codex":0.0000040954283,"about_ca_topic_score_gemma":0.000015058301,"teacher_disagreement_score":0.27994612,"about_ca_system_score_codex":0.000023183089,"about_ca_system_score_gemma":0.000023825478,"threshold_uncertainty_score":0.9999804},"labels":[],"label_agreement":null},{"id":"W4394564206","doi":"10.1109/access.2024.3386092","title":"Harmonics Forecasting of Renewable Energy System Using Hybrid Model Based on LSTM and ANFIS","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Renewable energy; Harmonics; Adaptive neuro fuzzy inference system; Computer science; Artificial intelligence; Energy (signal processing); Machine learning; Electrical engineering; Fuzzy logic; Fuzzy control system; Engineering; Mathematics; Statistics; Voltage","score_opus":0.043907753871322903,"score_gpt":0.2487434604640143,"score_spread":0.2048357065926914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394564206","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":[0.5427566,0.001388255,0.44190046,0.000007716941,0.0012192264,0.00006467338,0.00006261263,0.0004874195,0.012113024],"genre_scores_gemma":[0.9980486,0.000022770979,0.0016352289,0.000023777546,0.00013436227,0.00000704855,0.000007062463,0.0000637517,0.00005740788],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990998,0.000014164976,0.00026147687,0.0002158239,0.0001557825,0.00025295155],"domain_scores_gemma":[0.9995818,0.00010349004,0.0000363801,0.00017984424,0.000030285737,0.00006819277],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013769222,0.0001781835,0.00020964575,0.00018263653,0.00006451319,0.00014413144,0.00016524059,0.00005942245,0.000004181677],"category_scores_gemma":[0.000009292539,0.0001745634,0.0000557898,0.0002276765,0.00001976695,0.00028140852,0.00003108222,0.00010242015,4.4246175e-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":[0.0000060146863,0.0000044834533,0.000044256896,0.00069439097,0.000023821958,0.00003121984,0.00003942944,0.9913851,0.00404335,0.00018780674,0.00036206073,0.0031780521],"study_design_scores_gemma":[0.00010574341,0.000014367879,0.0000012680568,0.0009749788,0.00002753045,0.000020808238,0.00000935573,0.904346,0.093982406,0.000095741176,0.0002601856,0.00016164363],"about_ca_topic_score_codex":0.00039976425,"about_ca_topic_score_gemma":0.00003736975,"teacher_disagreement_score":0.45529196,"about_ca_system_score_codex":0.00007659996,"about_ca_system_score_gemma":0.00003854617,"threshold_uncertainty_score":0.7118489},"labels":[],"label_agreement":null},{"id":"W4394585959","doi":"10.1109/tnnls.2024.3382763","title":"Statistical Machine Learning for Power Flow Analysis Considering the Influence of Weather Factors on Photovoltaic Power Generation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Energy Load and Power Forecasting","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":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Photovoltaic system; Power (physics); Electricity generation; Numerical weather prediction; Artificial intelligence; Power analysis; Generative grammar; Machine learning; Meteorology; Simulation; Reliability engineering; Engineering; Electrical engineering; Algorithm; Geography","score_opus":0.012994214495173972,"score_gpt":0.2205962875309678,"score_spread":0.20760207303579384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394585959","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":[0.5624358,0.0005393935,0.4361102,0.0000060148,0.0005964324,0.0001225366,0.0000152260445,0.00014182262,0.000032580763],"genre_scores_gemma":[0.9995194,0.000061637555,0.000055724147,0.000010099994,0.00006880788,0.000034382694,0.00001596628,0.00004685459,0.00018709934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893296,0.00014144655,0.0003051158,0.00023298511,0.00015083411,0.00023664668],"domain_scores_gemma":[0.9988603,0.00089776376,0.000042810258,0.000108516644,0.000030680938,0.000059925376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027034234,0.00021680105,0.00027783855,0.00016694528,0.0003135667,0.00014648147,0.00005565663,0.00009732234,0.000030407315],"category_scores_gemma":[0.000015845257,0.00015238304,0.000133042,0.00032728893,0.000042463074,0.00009204662,0.0000010205179,0.00067763025,0.0000010914657],"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":[0.000017665572,0.0000056047347,0.00043056338,0.00004699678,0.00031532208,0.0000030698352,0.0005012609,0.995693,0.0015720737,0.00004362308,0.000017312308,0.0013534976],"study_design_scores_gemma":[0.000119843135,0.00021973714,0.00023624348,0.00010560101,0.00018179369,0.0000073411034,0.000104105995,0.997369,0.00039541442,6.959267e-7,0.0011042285,0.00015597879],"about_ca_topic_score_codex":0.0000945673,"about_ca_topic_score_gemma":0.00003775862,"teacher_disagreement_score":0.43708363,"about_ca_system_score_codex":0.000026452368,"about_ca_system_score_gemma":0.0000052267715,"threshold_uncertainty_score":0.6214},"labels":[],"label_agreement":null},{"id":"W4394918548","doi":"10.5194/piahs-385-267-2024","title":"Seasonal precipitation forecasting with large scale climate predictors: a hybrid ensemble empirical mode decomposition-NARX scheme","year":2024,"lang":"en","type":"article","venue":"Proceedings of the International Association of Hydrological Sciences","topic":"Energy Load and Power Forecasting","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":"Institut National de la Recherche Scientifique","funders":"","keywords":"Hilbert–Huang transform; Nonlinear autoregressive exogenous model; Climatology; Precipitation; Mode (computer interface); Scale (ratio); Environmental science; Autoregressive model; Decomposition; Econometrics; Meteorology; Statistics; Computer science; Mathematics; Geography; Geology; Ecology","score_opus":0.015217618788485918,"score_gpt":0.28056379746369753,"score_spread":0.2653461786752116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394918548","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":[0.9874017,0.000060860508,0.00037318488,0.0009850404,0.0003275691,0.00008256315,0.000057026915,0.00011795912,0.010594059],"genre_scores_gemma":[0.99656403,0.000019219247,0.0031447124,0.000043352204,0.00013052007,0.000015586806,0.000008042873,0.000008189852,0.000066371744],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985122,0.000008182685,0.0003025267,0.00020284613,0.0007326718,0.00024154363],"domain_scores_gemma":[0.9993369,0.00023334862,0.00017155134,0.000025909934,0.00019463808,0.00003766223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073422724,0.000111766254,0.0001410633,0.00008400142,0.0001318845,0.00010647481,0.0003569081,0.000059774186,0.000032775737],"category_scores_gemma":[0.0002015447,0.00007149615,0.00009092498,0.00032422107,0.00007862349,0.00043762548,0.0000898264,0.00015293012,0.0000022060271],"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":[0.00008135033,0.00017092444,0.867339,0.00021630577,0.00028466745,0.0000015916053,0.0017453735,0.07983618,0.036538564,0.00960435,0.0029346272,0.0012470447],"study_design_scores_gemma":[0.00015963722,0.00012152506,0.0064674644,0.0002725749,0.000021858203,0.000014705283,0.00008252115,0.96864647,0.020651858,0.0028267887,0.00062640785,0.00010816292],"about_ca_topic_score_codex":0.0000038173284,"about_ca_topic_score_gemma":0.000008337165,"teacher_disagreement_score":0.88881034,"about_ca_system_score_codex":0.00013966541,"about_ca_system_score_gemma":0.000024736679,"threshold_uncertainty_score":0.29155284},"labels":[],"label_agreement":null},{"id":"W4394983035","doi":"10.1016/j.resourpol.2024.105008","title":"Fossil energy market price prediction by using machine learning with optimal hyper-parameters: A comparative study","year":2024,"lang":"en","type":"article","venue":"Resources Policy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Econometrics; Fossil fuel; Energy (signal processing); Economics; Computer science; Machine learning; Artificial intelligence; Natural resource economics; Ecology; Statistics; Mathematics; Biology","score_opus":0.016395253955047623,"score_gpt":0.24214251347124646,"score_spread":0.22574725951619884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394983035","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":[0.97645587,0.003249914,0.0034675333,0.000015940494,0.00010218365,0.00010119272,0.000037447793,0.0007179303,0.015851976],"genre_scores_gemma":[0.9975348,0.000037155067,0.00075209426,0.000011960657,0.00032195833,0.000020360518,0.000015314768,0.00006626185,0.0012400775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988049,0.0000950967,0.00022433675,0.0002773902,0.0002258335,0.00037244576],"domain_scores_gemma":[0.9995673,0.00012225256,0.000034899353,0.00014300442,0.00001883988,0.000113737704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001357211,0.0002613867,0.00024286231,0.0002642638,0.00015772156,0.0002084163,0.00012737338,0.00006664817,0.00003284794],"category_scores_gemma":[0.000014461503,0.00022192148,0.00005198929,0.00058777025,0.0000458056,0.0001933091,0.000045308785,0.00030750653,0.000004206814],"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":[0.000090345886,0.00009256624,0.0041225296,0.00011480689,0.00053179875,0.000044473767,0.016660431,0.9648889,0.0029925355,0.000077836085,0.0011981409,0.009185684],"study_design_scores_gemma":[0.00031153634,0.00032944328,0.00018742577,0.00016563368,0.000055312266,0.00007177255,0.0007726806,0.9036912,0.0008063596,0.0000038642675,0.09335545,0.0002493492],"about_ca_topic_score_codex":0.0013430031,"about_ca_topic_score_gemma":0.000055676916,"teacher_disagreement_score":0.092157304,"about_ca_system_score_codex":0.00014210657,"about_ca_system_score_gemma":0.000023963306,"threshold_uncertainty_score":0.9049695},"labels":[],"label_agreement":null},{"id":"W4395027700","doi":"10.1109/access.2024.3392592","title":"Short-Term Load Foresting Using Combination of Linear and Non-Linear Models","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Term (time); Computer science; Physics","score_opus":0.05278718450791655,"score_gpt":0.30435483117756446,"score_spread":0.2515676466696479,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395027700","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":[0.90118766,0.0005284971,0.09571806,0.000003926538,0.00065039843,0.000059318132,0.000006175286,0.00015207607,0.0016938684],"genre_scores_gemma":[0.9984022,0.00004605636,0.0013036977,0.000004629353,0.00017766221,0.0000033287668,0.000005186439,0.000036889945,0.000020358188],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935174,0.0000061519295,0.00021082141,0.00013811067,0.00013375528,0.00015939132],"domain_scores_gemma":[0.99973655,0.000057871188,0.00001699379,0.000100385965,0.000046979014,0.000041208914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012211822,0.00011653935,0.00013822933,0.00009372045,0.000046921672,0.00007207333,0.00011794826,0.00006557401,0.0000033225529],"category_scores_gemma":[0.000008723517,0.000116353884,0.00003323673,0.00019178409,0.000022905422,0.00058978633,0.00003778367,0.00012185141,0.0000010279286],"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":[0.000003323871,0.000008938413,0.0024632514,0.0005595737,0.000036908517,0.000017046717,0.0004916182,0.9719095,0.013725396,0.0002489992,0.000032988544,0.01050244],"study_design_scores_gemma":[0.00008983227,0.000010487906,0.00030172386,0.00040152937,0.00002088335,0.000009975942,0.0000072194066,0.9749919,0.023569675,0.00044789087,0.000026565893,0.0001223162],"about_ca_topic_score_codex":0.000053952823,"about_ca_topic_score_gemma":0.000014643018,"teacher_disagreement_score":0.09721451,"about_ca_system_score_codex":0.00003506405,"about_ca_system_score_gemma":0.000018518629,"threshold_uncertainty_score":0.47447738},"labels":[],"label_agreement":null},{"id":"W4395464887","doi":"10.18280/ijdne.190205","title":"Predicting Global Energy Consumption Through Data Mining Techniques","year":2024,"lang":"en","type":"article","venue":"International Journal of Design & Nature and Ecodynamics","topic":"Energy Load and Power Forecasting","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":"Energy consumption; Consumption (sociology); Environmental science; Computer science; Engineering; Electrical engineering; Sociology","score_opus":0.020930670481166175,"score_gpt":0.27858226946785264,"score_spread":0.25765159898668644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395464887","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":[0.08399544,0.025191491,0.87930685,0.00029119692,0.0064841,0.000044355773,0.00017487729,0.00029869672,0.004213004],"genre_scores_gemma":[0.95909625,0.0034820973,0.03642785,0.000087018445,0.0008227542,6.9824875e-7,0.000046736324,0.000018183848,0.00001841918],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923265,0.000018141924,0.00029589943,0.00011709228,0.0002227625,0.0001134695],"domain_scores_gemma":[0.9995937,0.00013085165,0.00006576113,0.00008699831,0.00008383394,0.00003885684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021647893,0.00011361832,0.00011749303,0.00009750423,0.000028475626,0.0001618122,0.0003448167,0.0001584955,0.0000108531385],"category_scores_gemma":[0.00004482345,0.00010171865,0.000039886574,0.00008334243,0.000021538917,0.00067253114,0.00007029914,0.00031875225,6.300179e-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":[0.00024241807,0.000068664056,0.009009285,0.0002783876,0.0026606177,0.0015930624,0.0011156345,0.047797214,0.0047915876,0.03803875,0.016685503,0.87771887],"study_design_scores_gemma":[0.00017598644,0.00006133697,0.00020238428,0.0009160903,0.000070059556,0.0019195463,0.000052037834,0.9677782,0.0011125925,0.0035747981,0.023940794,0.00019617556],"about_ca_topic_score_codex":0.000005728661,"about_ca_topic_score_gemma":0.000019953415,"teacher_disagreement_score":0.919981,"about_ca_system_score_codex":0.000092938455,"about_ca_system_score_gemma":0.000033987966,"threshold_uncertainty_score":0.41479662},"labels":[],"label_agreement":null},{"id":"W4395956384","doi":"10.59256/ijire.20240502037","title":"Review on Design and Simulation of Electricity Price Fore Casting Using Artificial Neural Network","year":2024,"lang":"en","type":"article","venue":"International Journal of Innovative Research in Engineering","topic":"Energy Load and Power Forecasting","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":"Artificial neural network; Computer science; Perceptron; Electricity; MATLAB; Backpropagation; Artificial intelligence; Restructuring; Electricity market; Multilayer perceptron; Electric power; Power (physics); Machine learning; Engineering; Economics; Electrical engineering; Finance","score_opus":0.12315069605780705,"score_gpt":0.390646740197968,"score_spread":0.267496044140161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395956384","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":[0.4128364,0.017753648,0.5677808,0.00010413946,0.001068722,0.00018575421,0.0000036109802,0.00004024256,0.00022669774],"genre_scores_gemma":[0.9940115,0.00069357426,0.0047985376,0.000011870354,0.00045793309,0.0000020076106,0.0000012044698,0.000022404,9.3276816e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998622,0.00006246458,0.0005494795,0.00009034831,0.00044420015,0.00023152475],"domain_scores_gemma":[0.9982668,0.0009721091,0.000069247944,0.00004248785,0.00061396585,0.000035362937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002028854,0.000103510014,0.00018544178,0.0007653062,0.000020052903,0.000047803504,0.00014716342,0.00004120697,0.000004553636],"category_scores_gemma":[0.00054611336,0.0000959475,0.000032711105,0.0014760661,0.000021273669,0.00025386404,0.000033235734,0.0006576901,3.113102e-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":[0.000023038847,0.000007768855,0.00010411161,0.00041776415,0.00006574069,0.00007565064,0.00007368931,0.9733573,0.0056834407,0.0011959992,0.00003182394,0.018963628],"study_design_scores_gemma":[0.00007462263,0.000077827775,0.000119122014,0.008603995,0.0000022641805,0.000058713482,0.000008146585,0.98784506,0.0026277502,0.000312522,0.00019834984,0.00007160484],"about_ca_topic_score_codex":0.0000040110385,"about_ca_topic_score_gemma":4.663005e-7,"teacher_disagreement_score":0.5811751,"about_ca_system_score_codex":0.00022296865,"about_ca_system_score_gemma":0.00005480272,"threshold_uncertainty_score":0.39126256},"labels":[],"label_agreement":null},{"id":"W4396213970","doi":"10.2316/j.2023.203-0451","title":"A TIME-SERIES FORECASTING OF POWER CONSUMPTION AND FEATURE EXTRACTION IN AGRICULTURE SECTOR USING MACHINE LEARNING, 1-11.","year":2023,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Agriculture; Power consumption; Series (stratigraphy); Computer science; Feature (linguistics); Feature extraction; Time series; Consumption (sociology); Extraction (chemistry); Artificial intelligence; Industrial engineering; Agricultural engineering; Machine learning; Power (physics); Agricultural economics; Engineering; Economics; Geography; Social science; Sociology; Archaeology","score_opus":0.013875037236448094,"score_gpt":0.22684938342141672,"score_spread":0.21297434618496863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396213970","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":[0.9952708,0.0022935492,0.00045399833,0.00002999137,0.0014749683,0.000018191737,0.000013264478,0.000031232208,0.00041404294],"genre_scores_gemma":[0.9988064,0.0003429201,0.00015063758,0.0000041533335,0.00017775942,0.0000010623419,0.000013364478,0.000016935399,0.00048672268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991854,0.000038909508,0.00034517847,0.00008409527,0.00022465769,0.00012178461],"domain_scores_gemma":[0.9995231,0.00006780197,0.00019829907,0.000031823074,0.00012878086,0.000050162125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023040197,0.00012231979,0.00020697355,0.00029627534,0.000031910717,0.000054063366,0.0000718863,0.00009734737,0.000017950426],"category_scores_gemma":[0.000039913088,0.000100882964,0.000047601647,0.00012123907,0.000027150094,0.00031069276,0.000024308816,0.00017715838,5.6972016e-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":[0.00018914981,0.000045550907,0.09483036,0.00022477715,0.0005130801,0.0003814994,0.0038450775,0.7512354,0.14495268,0.0008198334,0.0011329654,0.0018295913],"study_design_scores_gemma":[0.0026552717,0.00037282766,0.027423123,0.0036672214,0.00008088561,0.0086924005,0.0017666696,0.88100046,0.007195797,0.00009034999,0.06628863,0.00076635636],"about_ca_topic_score_codex":0.00012350819,"about_ca_topic_score_gemma":0.00007254795,"teacher_disagreement_score":0.13775688,"about_ca_system_score_codex":0.000048224683,"about_ca_system_score_gemma":0.000009912726,"threshold_uncertainty_score":0.41138878},"labels":[],"label_agreement":null},{"id":"W4396656326","doi":"10.1007/s00521-024-09677-z","title":"Wind speed prediction and insight for generalized predictive modeling framework: a comparative study for different artificial intelligence models","year":2024,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Multivariate adaptive regression splines; Mars Exploration Program; Wind speed; Environmental science; Relative humidity; Random forest; Meteorology; Computer science; Statistics; Regression analysis; Mathematics; Machine learning; Nonparametric regression; Geography","score_opus":0.08200420354806671,"score_gpt":0.31552290025657787,"score_spread":0.23351869670851116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396656326","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":[0.4202374,0.00047833915,0.5781485,0.000019913214,0.00014588,0.00067518186,0.000046241785,0.0002084454,0.000040125433],"genre_scores_gemma":[0.9960097,0.00002447095,0.003339752,0.00000966473,0.00044040586,0.00010512666,0.000039737613,0.000023772462,0.0000073608894],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917805,0.000014894309,0.00026350556,0.00029956008,0.00006861983,0.00017536212],"domain_scores_gemma":[0.99949026,0.0002916189,0.000021113365,0.00008945935,0.000046510304,0.000061015086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000084587096,0.00016114733,0.00018399663,0.00007104212,0.0002754237,0.00013653877,0.000056984165,0.00005896509,4.7520976e-7],"category_scores_gemma":[0.0000064239025,0.00014585456,0.000040629948,0.000120842866,0.000026435644,0.00009134719,0.00003134635,0.0001592401,2.6101625e-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":[0.000017556598,0.00003218321,0.000022936927,0.00008313591,0.00005016876,1.2588815e-7,0.0033592165,0.96010405,0.0003178568,0.014538502,0.000008108459,0.021466138],"study_design_scores_gemma":[0.00008895998,0.00011925615,0.000013828076,0.00007241967,0.00006772426,0.000002779092,0.00050631154,0.9743034,0.00027850768,0.024357041,0.00006494331,0.00012481649],"about_ca_topic_score_codex":0.000005531678,"about_ca_topic_score_gemma":0.000004772239,"teacher_disagreement_score":0.57577235,"about_ca_system_score_codex":0.000020182933,"about_ca_system_score_gemma":0.000005812778,"threshold_uncertainty_score":0.59477764},"labels":[],"label_agreement":null},{"id":"W4396699441","doi":"10.1016/j.jhydrol.2024.131275","title":"Ensemble learning using multivariate variational mode decomposition based on the Transformer for multi-step-ahead streamflow forecasting","year":2024,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue; McGill University; United Nations University Institute for Water, Environment, and Health","funders":"Youth Innovation Promotion Association of the Chinese Academy of Sciences; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Streamflow; Multivariate statistics; Transformer; Computer science; Ensemble learning; Artificial intelligence; Mathematics; Econometrics; Machine learning; Geography; Engineering; Drainage basin; Cartography","score_opus":0.0442928805979693,"score_gpt":0.3067143882621564,"score_spread":0.2624215076641871,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396699441","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":[0.2500117,0.00014326171,0.7482998,0.00020849599,0.0008482471,0.000088282366,0.000009144276,0.000048414076,0.0003426301],"genre_scores_gemma":[0.9549406,0.0000051718416,0.04451304,0.00007492129,0.00038838523,0.0000069662965,0.000009910346,0.00004267247,0.00001834124],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998992,0.00007410069,0.00041004136,0.0001130471,0.00014095061,0.00026981428],"domain_scores_gemma":[0.99893725,0.00079351553,0.000091301976,0.00005907472,0.000066268236,0.000052566837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051195215,0.00015724136,0.0002070431,0.00020134707,0.00019074079,0.000051543117,0.00010115612,0.000111163696,0.000030376645],"category_scores_gemma":[0.00007824861,0.00011515338,0.00018460196,0.00011849099,0.000021048329,0.00015871983,0.0000043353775,0.00045127206,0.0000019792496],"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":[0.00007710869,0.000022760529,0.000054695236,0.00005083834,0.00009951815,0.000019539257,0.00028896076,0.9611208,0.030495519,0.0005875331,0.000035264384,0.007147504],"study_design_scores_gemma":[0.00063597114,0.00022648614,0.00003163293,0.00020075531,0.0000720849,0.00016706024,0.000022904447,0.9937742,0.0036207521,0.00025780624,0.00087611936,0.00011424419],"about_ca_topic_score_codex":0.000016265207,"about_ca_topic_score_gemma":0.000017004097,"teacher_disagreement_score":0.7049289,"about_ca_system_score_codex":0.000091603186,"about_ca_system_score_gemma":0.00005266204,"threshold_uncertainty_score":0.46958187},"labels":[],"label_agreement":null},{"id":"W4396780668","doi":"10.1117/12.3012012","title":"Federated autoML learning for community building energy prediction","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Okanagan University College; University of British Columbia","funders":"","keywords":"Computer science; Automation; Analytics; Data modeling; Energy management; Resource (disambiguation); Artificial intelligence; Deep learning; Energy modeling; Building management system; Machine learning; Energy (signal processing); Efficient energy use; Big data; Data science; Software engineering; Data mining; Engineering; Control (management)","score_opus":0.015708744050566107,"score_gpt":0.22900433009863777,"score_spread":0.21329558604807167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396780668","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":[0.11451348,0.0005206602,0.81743264,0.000026500205,0.0013343074,0.000045491703,0.000006402756,0.0043637156,0.061756827],"genre_scores_gemma":[0.9964948,0.000026167138,0.0013481668,0.000014952702,0.00019092303,0.000021985355,0.000052950025,0.000040891413,0.0018091303],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953437,0.000028684237,0.00012962322,0.00007734566,0.00005266249,0.00017733929],"domain_scores_gemma":[0.9997139,0.00017179344,0.000005865932,0.000051469593,0.000018513165,0.000038472546],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001794096,0.00009823034,0.00007994056,0.00007426194,0.00026335986,0.00013861444,0.00004877186,0.000067377194,0.000044275686],"category_scores_gemma":[0.000024429442,0.00009448491,0.00004700538,0.00014709227,0.000008034238,0.00013815342,0.000017337978,0.00026095175,0.0000039688744],"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":[0.0000069945536,0.000014331119,0.00020233993,0.00036856293,0.00016917287,0.000003511697,0.0006335289,0.7764297,0.047306117,0.024508335,0.0071536186,0.14320377],"study_design_scores_gemma":[0.00006928049,0.000032489774,0.000036003265,0.00009089409,0.000008991547,0.0000065397903,0.00006434423,0.8925971,0.01427585,0.00026302336,0.09246232,0.000093172144],"about_ca_topic_score_codex":0.00010320077,"about_ca_topic_score_gemma":0.00006188378,"teacher_disagreement_score":0.8819814,"about_ca_system_score_codex":0.000047203517,"about_ca_system_score_gemma":0.000008923206,"threshold_uncertainty_score":0.38529828},"labels":[],"label_agreement":null},{"id":"W4396941301","doi":"10.1109/jiot.2024.3401236","title":"HALO: HVAC Load Forecasting With Industrial IoT and Local–Global-Scale Transformer","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"HVAC; Computer science; Transformer; Air conditioning; Efficient energy use; Real-time computing; Reliability engineering; Voltage; Electrical engineering; Engineering","score_opus":0.018096962372038485,"score_gpt":0.21258700847488535,"score_spread":0.19449004610284687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396941301","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":[0.87984204,0.0027320196,0.09786089,0.00009852165,0.0025745346,0.000069383546,0.0000093264125,0.00017672704,0.01663655],"genre_scores_gemma":[0.99775416,0.000068147936,0.0013909021,0.00002959423,0.0004441876,0.0000017298893,9.289493e-7,0.000039893104,0.00027046376],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878365,0.000016715552,0.00041184895,0.00015856342,0.00030845634,0.00032075174],"domain_scores_gemma":[0.999594,0.000061395935,0.000059547587,0.000071954164,0.000060499566,0.0001526194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034858304,0.0002182862,0.0002562777,0.00009498992,0.000044809498,0.00019900192,0.00017216019,0.00014028845,0.00006603386],"category_scores_gemma":[0.000014091122,0.00017009834,0.00009805328,0.00017674413,0.000089900685,0.00036886524,0.000012257527,0.000706821,0.0000046234727],"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":[0.00037061016,0.000054039527,0.0030803082,0.00081767404,0.0011888002,0.0007973407,0.014428146,0.06429309,0.008714233,0.0005565271,0.012622154,0.8930771],"study_design_scores_gemma":[0.002803444,0.0010818415,0.00010432864,0.010969627,0.00035297935,0.014800089,0.000846961,0.82553804,0.092594676,0.0019578226,0.047778346,0.0011718638],"about_ca_topic_score_codex":0.000120920304,"about_ca_topic_score_gemma":0.00005492496,"teacher_disagreement_score":0.8919052,"about_ca_system_score_codex":0.00015079736,"about_ca_system_score_gemma":0.000064588494,"threshold_uncertainty_score":0.69364095},"labels":[],"label_agreement":null},{"id":"W4398131379","doi":"10.1016/j.compeleceng.2024.109305","title":"Power system flexibility analysis using net-load forecasting based on deep learning considering distributed energy sources and electric vehicles","year":2024,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Flexibility (engineering); Net (polyhedron); Energy (signal processing); Electric power system; Computer science; Electric power; Power (physics); Distributed generation; Automotive engineering; Engineering; Industrial engineering; Artificial intelligence; Renewable energy; Electrical engineering; Mathematics; Statistics","score_opus":0.009819648752273653,"score_gpt":0.193168809895167,"score_spread":0.18334916114289335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398131379","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":[0.36509344,0.0038664453,0.62886065,0.000005624053,0.00029011688,0.000060970437,0.0000029126688,0.0016682638,0.00015155759],"genre_scores_gemma":[0.9949531,0.000022536513,0.004685023,0.000018178476,0.00018154335,0.000011666549,0.000020551965,0.00010395575,0.0000034646196],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975323,0.00006430469,0.00053432875,0.000626378,0.00037216314,0.0008705415],"domain_scores_gemma":[0.9984689,0.0009227172,0.000051542203,0.00023931974,0.000057900987,0.000259634],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036016482,0.00051035685,0.0006224745,0.0008478591,0.00020947059,0.000361367,0.00018137343,0.00019379084,0.0000056597237],"category_scores_gemma":[0.000118524615,0.00053537794,0.00025804972,0.0028785635,0.000020586172,0.00017686532,0.000062182015,0.00067786087,0.0000016648692],"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":[0.000009970424,0.000009728916,0.00036943855,0.00021352193,0.00037427788,0.00010258327,0.000111289475,0.9810298,0.004557144,0.00062317494,0.000008505654,0.01259057],"study_design_scores_gemma":[0.00019621209,0.000091964495,0.0002112204,0.00035039973,0.00021998977,0.000066153705,0.000015833013,0.9947305,0.0028789751,0.000008417481,0.0006865684,0.00054381904],"about_ca_topic_score_codex":0.000037248534,"about_ca_topic_score_gemma":0.0000030453323,"teacher_disagreement_score":0.6298596,"about_ca_system_score_codex":0.0006972934,"about_ca_system_score_gemma":0.000048988346,"threshold_uncertainty_score":0.9997098},"labels":[],"label_agreement":null},{"id":"W4398471467","doi":"10.7910/dvn/fie0s4/myknvr","title":"Electricity_Q.tab","year":2016,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Energy Load and Power Forecasting","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":"Simon Fraser University","funders":"","keywords":"Electricity; History; Geography; Engineering; Electrical engineering","score_opus":0.009349171812643825,"score_gpt":0.20041469161996617,"score_spread":0.19106551980732234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398471467","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000011564025,0.0000069901957,0.00024449624,0.0000010343339,0.0017530795,0.000078164696,0.9956725,0.00030607183,0.0019260796],"genre_scores_gemma":[0.00002356419,0.0011740918,0.00008834715,0.00008270703,0.0011303156,0.000014581813,0.9969572,0.00005240304,0.00047682616],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986778,0.000023348588,0.00027380354,0.00029665654,0.00023700361,0.0004913994],"domain_scores_gemma":[0.99875623,0.00007616327,0.000057664263,0.0009442159,0.000024777135,0.00014095637],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00011565832,0.00036664706,0.00032417744,0.00020360424,0.00005209598,0.000064711254,0.0005778452,0.0003204521,0.01264088],"category_scores_gemma":[0.000072251736,0.0003195112,0.00010374918,0.00015879546,0.000033414788,0.00021052391,0.00013745748,0.0003987289,0.09877697],"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":[0.0000045384077,0.000009148072,7.835501e-7,0.000111079506,0.000072464085,0.00006352953,0.0000047001704,0.000100639474,0.00007737989,0.000017788117,0.99820024,0.0013376973],"study_design_scores_gemma":[0.00021781039,0.000016955473,0.0000013767622,0.00016142182,0.0000565136,0.000021197566,0.0000016868752,0.00015053045,0.00022224833,0.000015447606,0.9987039,0.0004308966],"about_ca_topic_score_codex":0.00006846304,"about_ca_topic_score_gemma":0.0000809519,"teacher_disagreement_score":0.08613609,"about_ca_system_score_codex":0.000099463905,"about_ca_system_score_gemma":0.000038634316,"threshold_uncertainty_score":0.9999257},"labels":[],"label_agreement":null},{"id":"W4398471566","doi":"10.7910/dvn/mcw60q/njjuu7","title":"minwage_out_15Jun2015.tab","year":2018,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Energy Load and Power Forecasting","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":"McGill University","funders":"","keywords":"Wage; Minimum wage; Economics; Computer science; Labour economics","score_opus":0.011972963786033231,"score_gpt":0.210207814380067,"score_spread":0.19823485059403376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398471566","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000024226563,0.000006482029,0.000050320992,5.690678e-7,0.0038846727,0.00009193357,0.9929144,0.0003123072,0.0027150952],"genre_scores_gemma":[0.000010677034,0.00047451127,0.00046142843,0.00009426075,0.0022143123,0.000015087505,0.99616873,0.000066435794,0.0004945713],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99853665,0.000024950516,0.00031610898,0.00036112266,0.0002704801,0.00049068144],"domain_scores_gemma":[0.99838454,0.000046616733,0.00006851212,0.0012819186,0.000037524565,0.00018091396],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00015555898,0.00044125164,0.00036768123,0.00018528878,0.000085964515,0.00010393748,0.0007022619,0.00039110924,0.023021827],"category_scores_gemma":[0.00006958342,0.0004519647,0.000117220006,0.00016443519,0.00008328071,0.0002228897,0.00023452514,0.0004330871,0.14561974],"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":[0.000004868142,0.000012032721,8.062724e-7,0.00017181209,0.0000893259,0.00009174092,0.000013515282,0.00013424325,0.00002034587,0.0000046192777,0.999129,0.0003277133],"study_design_scores_gemma":[0.00019954806,0.000024486986,0.0000019810016,0.00017333121,0.000095880314,0.000026658616,0.0000075687303,0.00038597788,0.000100490324,0.0000071528543,0.99846876,0.00050817954],"about_ca_topic_score_codex":0.00012357051,"about_ca_topic_score_gemma":0.00025141693,"teacher_disagreement_score":0.12259791,"about_ca_system_score_codex":0.00007356926,"about_ca_system_score_gemma":0.000036821184,"threshold_uncertainty_score":0.99979323},"labels":[],"label_agreement":null},{"id":"W4398540733","doi":"10.7910/dvn/fie0s4/xosvhj","title":"Electricity_B2E.tab","year":2016,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Energy Load and Power Forecasting","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":"Simon Fraser University","funders":"","keywords":"Electricity; Geography; Environmental science; Cartography; Computer science; Engineering; Electrical engineering","score_opus":0.009299562138273917,"score_gpt":0.2002172553500779,"score_spread":0.19091769321180396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398540733","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000009431582,0.0000071503882,0.00028776104,0.0000010914608,0.0017838832,0.000080991915,0.9949712,0.00031558232,0.002542879],"genre_scores_gemma":[0.000016900914,0.0010720118,0.00010785531,0.00010108686,0.0011469153,0.0000152478615,0.9969881,0.00005419207,0.000497663],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986229,0.000024169709,0.00029906956,0.0003053723,0.00024359497,0.00050491904],"domain_scores_gemma":[0.9987239,0.00007693763,0.00006315974,0.0009649869,0.000025640586,0.00014534457],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00012479801,0.00037751015,0.00033170218,0.00020959743,0.00006753327,0.000066592525,0.0005920088,0.00032929715,0.012763822],"category_scores_gemma":[0.00007770097,0.0003292597,0.000105861276,0.00016258945,0.00003517866,0.00021620347,0.0001413316,0.000407989,0.09943077],"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":[0.00000474425,0.000009513015,7.0556723e-7,0.000115432325,0.00007451072,0.00006756747,0.0000036681552,0.0000994304,0.00008017998,0.00002333127,0.9983039,0.0012170472],"study_design_scores_gemma":[0.00022370236,0.0000171086,0.0000011154646,0.00016684383,0.000057955072,0.00002112157,0.0000016539606,0.00015316678,0.00018330061,0.000016274493,0.9987139,0.0004438318],"about_ca_topic_score_codex":0.000071845876,"about_ca_topic_score_gemma":0.000071692564,"teacher_disagreement_score":0.08666694,"about_ca_system_score_codex":0.000102569946,"about_ca_system_score_gemma":0.00003972241,"threshold_uncertainty_score":0.99991596},"labels":[],"label_agreement":null},{"id":"W4399293303","doi":"10.3390/electronics13112155","title":"A Proposed Hybrid Machine Learning Model Based on Feature Selection Technique for Tidal Power Forecasting and Its Integration","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Adaptive neuro fuzzy inference system; Tidal power; Kalman filter; Computer science; Neuro-fuzzy; Feature selection; Power (physics); Machine learning; Artificial intelligence; Engineering; Fuzzy logic; Fuzzy control system","score_opus":0.008493565142545007,"score_gpt":0.21281150318654318,"score_spread":0.20431793804399817,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399293303","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":[0.0464957,0.005255344,0.944158,0.00014855868,0.00019911173,0.00062273274,0.000024684037,0.0011303293,0.0019655246],"genre_scores_gemma":[0.9927293,0.0000613691,0.0064071957,0.000026157557,0.000081510974,0.000086308646,0.000088251334,0.00007816525,0.000441751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992199,0.000016018006,0.00012291168,0.00021919492,0.000099953635,0.0003220408],"domain_scores_gemma":[0.9997777,0.000071216724,0.000019890615,0.000052498843,0.00003871535,0.00004002245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000216265,0.00018851252,0.00012067648,0.00014656107,0.00012528969,0.00008307507,0.00004726121,0.000099412304,0.0000050418075],"category_scores_gemma":[0.00005752579,0.00017419037,0.000053775835,0.00018463102,0.0000056087415,0.00012666744,0.0000073329543,0.00061351625,0.000001105764],"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":[0.000064497035,0.0000147429755,0.0000106357475,0.0002555427,0.000035999346,0.0000042793076,0.00017577947,0.79945934,0.16751708,0.0039407765,0.0005359345,0.027985405],"study_design_scores_gemma":[0.00012726909,0.00027352115,5.5803366e-7,0.00015481155,0.000014150983,0.00002799329,0.00000212447,0.8208697,0.17286491,0.0003841201,0.005125556,0.00015532],"about_ca_topic_score_codex":6.079151e-7,"about_ca_topic_score_gemma":0.000015923784,"teacher_disagreement_score":0.9462336,"about_ca_system_score_codex":0.00016683778,"about_ca_system_score_gemma":0.00006340876,"threshold_uncertainty_score":0.7103277},"labels":[],"label_agreement":null},{"id":"W4399323356","doi":"10.1063/5.0213366","title":"State of the art in energy consumption using deep learning models","year":2024,"lang":"en","type":"article","venue":"AIP Advances","topic":"Energy Load and Power Forecasting","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":"Princess Nourah Bint Abdulrahman University","keywords":"Energy consumption; Mean squared error; Deep learning; Consumption (sociology); Mean absolute error; Mean absolute percentage error; Error correction model; Energy (signal processing); Econometrics; Computer science; Statistics; Artificial intelligence; Mathematics; Engineering; Sociology; Cointegration","score_opus":0.018970865377855566,"score_gpt":0.23334177973923842,"score_spread":0.21437091436138286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399323356","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":[0.837159,0.04563781,0.11055069,0.000010723658,0.0009033329,0.000034268018,0.000002742021,0.00017538462,0.0055260886],"genre_scores_gemma":[0.9974679,0.0018263914,0.00056372245,0.000006705752,0.000024651,0.0000024065262,0.0000014774638,0.000014707664,0.00009206513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995987,0.00001668358,0.00012613322,0.00007587318,0.00006770075,0.00011494255],"domain_scores_gemma":[0.9998588,0.00005227213,0.00001486251,0.000055243152,0.000006707266,0.000012095981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006013129,0.000064279935,0.00007313418,0.000051538736,0.000022565066,0.000013563716,0.000053234664,0.000017195762,0.000009537496],"category_scores_gemma":[0.000005709893,0.00005068337,0.000026953301,0.00014271944,0.000021929682,0.00024368349,0.000015361395,0.00009951801,0.0000020807684],"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":[0.0000010286626,0.0000013890882,0.00058693194,0.00006487713,0.000005891137,0.0000024773171,0.00021942549,0.93657017,0.0022103493,0.00082659064,0.0000024408935,0.05950841],"study_design_scores_gemma":[0.000036019435,0.000004566701,0.00007605139,0.00026362002,0.0000035947016,0.0000042348743,0.000016719416,0.9826934,0.004678353,0.0024297428,0.009730801,0.0000629489],"about_ca_topic_score_codex":0.000014497199,"about_ca_topic_score_gemma":0.00015056774,"teacher_disagreement_score":0.16030891,"about_ca_system_score_codex":0.000026042562,"about_ca_system_score_gemma":0.0000058467713,"threshold_uncertainty_score":0.20668079},"labels":[],"label_agreement":null},{"id":"W4399563058","doi":"10.1109/access.2024.3413339","title":"Intelligent Fault-Tolerant Active Power Control Using Reinforcement Learning for Offshore Wind Farms","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","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":"Concordia University; University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Offshore wind power; Fault tolerance; Computer science; Wind power; Control (management); Submarine pipeline; Marine engineering; Distributed computing; Artificial intelligence; Electrical engineering; Engineering; Geotechnical engineering","score_opus":0.028802991756984555,"score_gpt":0.2897015584575698,"score_spread":0.26089856670058525,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399563058","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":[0.46059632,0.00082000956,0.530537,0.000026768743,0.003273449,0.0003769166,0.0000140394495,0.00047966995,0.00387581],"genre_scores_gemma":[0.9991234,0.000021352627,0.00013492935,0.0000769378,0.00035946726,0.000019891257,0.000013929494,0.00007600809,0.0001741015],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988807,0.000015003779,0.00030421632,0.00024423678,0.0001702687,0.00038561903],"domain_scores_gemma":[0.9995528,0.0001389951,0.00003707389,0.00013751614,0.000054665983,0.00007893509],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014057478,0.00023498021,0.00023251199,0.00014246893,0.00011601247,0.00027711727,0.00022823222,0.00009910217,0.00007549472],"category_scores_gemma":[0.000021122873,0.00021377468,0.00013462934,0.00019243547,0.000023084987,0.00043096356,0.000029049632,0.00029146526,0.00001468829],"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":[0.000033328317,0.0000060277694,0.00012860658,0.0001372066,0.0001833355,0.0000156047,0.0010679855,0.9839286,0.0036990608,0.00015488693,0.000162672,0.010482647],"study_design_scores_gemma":[0.00030945064,0.00005381702,0.000041487303,0.00031769247,0.00005557338,0.0000102762115,0.00017506097,0.9366289,0.03858779,0.00007923908,0.023438867,0.00030184042],"about_ca_topic_score_codex":0.000024123075,"about_ca_topic_score_gemma":0.000009178688,"teacher_disagreement_score":0.5385271,"about_ca_system_score_codex":0.00014481493,"about_ca_system_score_gemma":0.000030921445,"threshold_uncertainty_score":0.8717478},"labels":[],"label_agreement":null},{"id":"W4399568373","doi":"10.1109/oajpe.2024.3413606","title":"Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability","year":2024,"lang":"en","type":"article","venue":"IEEE Open Access Journal of Power and Energy","topic":"Energy Load and Power Forecasting","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":"Hydro One (Canada); Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Aggregate (composite); Econometrics; Flow (mathematics); Computer science; Environmental science; Operations research; Economics; Mathematics","score_opus":0.06404970176927487,"score_gpt":0.29260323371622105,"score_spread":0.22855353194694616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399568373","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":[0.96967053,0.002266035,0.021638012,0.000061104,0.0010893753,0.000052599935,0.00004719525,0.000049192626,0.005125958],"genre_scores_gemma":[0.98775107,0.00012718905,0.01189573,0.00003883202,0.00009147647,0.0000018594083,0.000009310297,0.00004531937,0.000039220457],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850315,0.000057435358,0.0006449793,0.00028225276,0.00023202147,0.00028016604],"domain_scores_gemma":[0.99884003,0.00030580396,0.00021384736,0.00029639064,0.00016793955,0.0001760099],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066874776,0.0002504204,0.0004748002,0.00019204525,0.00010364331,0.000844314,0.0007846823,0.000087927154,0.00006909912],"category_scores_gemma":[0.00015034639,0.00019054026,0.000044079676,0.0003496107,0.00012592872,0.0025393607,0.00033811384,0.0002922339,2.7149034e-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":[0.001037947,0.00025713214,0.018287102,0.0026131864,0.0037961178,0.0009571812,0.006316887,0.69437563,0.0270124,0.0017560561,0.0100524565,0.23353793],"study_design_scores_gemma":[0.001488521,0.00032660045,0.002178065,0.0030925777,0.00019560446,0.0012288238,0.00024724338,0.97639143,0.009566457,0.00055370596,0.0040405556,0.00069040584],"about_ca_topic_score_codex":0.00029378707,"about_ca_topic_score_gemma":0.00023356525,"teacher_disagreement_score":0.28201583,"about_ca_system_score_codex":0.000027682376,"about_ca_system_score_gemma":0.00006593799,"threshold_uncertainty_score":0.81417394},"labels":[],"label_agreement":null},{"id":"W4399593194","doi":"10.1080/14697688.2024.2357733","title":"Neural network approach to portfolio optimization with leverage constraints: a case study on high inflation investment","year":2024,"lang":"en","type":"article","venue":"Quantitative Finance","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Leverage (statistics); Portfolio optimization; Portfolio; Economics; Artificial neural network; Financial economics; Inflation (cosmology); Investment strategy; Investment (military); Investment portfolio; Econometrics; Computer science; Mathematical optimization; Microeconomics; Artificial intelligence; Mathematics; Profit (economics)","score_opus":0.028509595386647733,"score_gpt":0.2534234207310366,"score_spread":0.22491382534438883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399593194","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":[0.78353345,0.00019548634,0.20722367,0.000014034409,0.00033122808,0.00038845753,0.0000147769,0.00024258654,0.008056288],"genre_scores_gemma":[0.9651615,0.0000063750695,0.03445888,0.00007666413,0.00007259999,0.00008579906,0.000024362249,0.000036225083,0.00007758216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918133,0.000037573704,0.00018938546,0.00025495133,0.00012829552,0.00020847745],"domain_scores_gemma":[0.99969095,0.0000775423,0.000026535396,0.00012757913,0.00003463496,0.00004273441],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012549986,0.00017930314,0.00015017817,0.00009410262,0.00008079017,0.000071269635,0.00005119895,0.0000337667,0.000008808101],"category_scores_gemma":[0.000014116908,0.00015584,0.000021067473,0.0005398691,0.000032963875,0.00019032473,0.000012567583,0.00015014701,0.00001090897],"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":[0.000016150105,0.00004083208,0.00020191167,0.000027042764,0.000043710068,0.0006028413,0.0026781755,0.96247065,0.0000062285853,0.032419488,0.00034181436,0.0011511575],"study_design_scores_gemma":[0.000277563,0.00067953864,0.0010691991,0.00020301166,0.00002177187,0.00017225162,0.0007894317,0.99598414,0.00002800118,0.00006568403,0.00045028504,0.00025910008],"about_ca_topic_score_codex":0.000039613307,"about_ca_topic_score_gemma":0.000016762524,"teacher_disagreement_score":0.18162803,"about_ca_system_score_codex":0.000057066183,"about_ca_system_score_gemma":0.000019041592,"threshold_uncertainty_score":0.63549703},"labels":[],"label_agreement":null},{"id":"W4399729007","doi":"10.1109/syscon61195.2024.10553600","title":"Load Forecasting using GNN-LSTM Attention Mechanism with Low-Frequency Data","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Mechanism (biology); Artificial intelligence","score_opus":0.041720244287457374,"score_gpt":0.237698858170225,"score_spread":0.19597861388276763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399729007","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":[0.4594229,0.0012988704,0.4784975,0.000035627552,0.0017959839,0.00016019396,0.0000450687,0.00200178,0.056742083],"genre_scores_gemma":[0.9693388,0.000021822061,0.029718038,0.00001940466,0.00031142405,0.000004135357,0.00007952776,0.00008315958,0.00042373766],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998838,0.000010709867,0.00022605558,0.00033933032,0.0002358644,0.00035002173],"domain_scores_gemma":[0.9994042,0.000043482407,0.00001831459,0.0004245012,0.000034895755,0.00007463846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002324169,0.00020699094,0.00014291434,0.00009750667,0.00008848297,0.0001743449,0.00025727414,0.00007655163,0.00014684409],"category_scores_gemma":[0.000017515176,0.00017100181,0.000035768506,0.00032676163,0.000017399563,0.0006760731,0.00009717605,0.00019919986,0.000041189196],"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":[0.000038177248,0.00011095918,0.0016045807,0.0050269403,0.0014519233,0.002010687,0.0019296703,0.26078397,0.4007316,0.15331963,0.003982841,0.16900903],"study_design_scores_gemma":[0.00012067197,0.000023122562,0.000010751606,0.00073864934,0.000048525137,0.000175168,0.00005760558,0.9939145,0.0028838038,0.00096922397,0.0007732072,0.0002848111],"about_ca_topic_score_codex":0.00013156697,"about_ca_topic_score_gemma":0.00016028811,"teacher_disagreement_score":0.7331305,"about_ca_system_score_codex":0.0001100733,"about_ca_system_score_gemma":0.000054543594,"threshold_uncertainty_score":0.6973251},"labels":[],"label_agreement":null},{"id":"W4399828328","doi":"10.32920/26052718.v1","title":"A Concurrent CNN-RNN Approach for Multi-step Wind Power Forecasting","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"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":"University of Toronto","keywords":"Recurrent neural network; Computer science; Wind power; Power (physics); Artificial intelligence; Artificial neural network; Electrical engineering; Engineering; Physics","score_opus":0.07569993758205101,"score_gpt":0.27420560862957105,"score_spread":0.19850567104752004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399828328","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":[0.016091034,0.0053389715,0.8142525,0.000028846554,0.01061596,0.0014368894,0.00034900202,0.0024859637,0.14940082],"genre_scores_gemma":[0.85062516,0.00003466545,0.14340682,0.000041977917,0.00091474445,0.00030650359,0.00045725505,0.00032126886,0.003891615],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977479,0.000018257435,0.00060771994,0.000720876,0.00022055674,0.0006846724],"domain_scores_gemma":[0.99913263,0.00010646714,0.00007854287,0.000422163,0.00008328937,0.00017688815],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030165093,0.00066423713,0.00059035886,0.00021944438,0.000084637344,0.0001722186,0.00038279177,0.0005233703,0.00009539617],"category_scores_gemma":[0.00005806847,0.0006139995,0.00044608614,0.00014634548,0.00004121389,0.00005379004,0.0006703095,0.0011316995,0.00003402356],"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":[0.0000089305795,0.00010192337,0.00008235255,0.006647714,0.0008062247,0.000024186453,0.0019214102,0.91679585,0.00021378766,0.0052889436,0.011723235,0.056385458],"study_design_scores_gemma":[0.0003937017,0.00002949582,0.0000060320153,0.00056839956,0.000102655904,0.000017040153,0.00013731442,0.98151445,0.00040096976,0.00017469928,0.015920319,0.00073493656],"about_ca_topic_score_codex":0.00003622993,"about_ca_topic_score_gemma":0.000019047931,"teacher_disagreement_score":0.8345341,"about_ca_system_score_codex":0.00014579613,"about_ca_system_score_gemma":0.00006662393,"threshold_uncertainty_score":0.9996311},"labels":[],"label_agreement":null},{"id":"W4400006024","doi":"10.1007/978-3-031-62881-8_31","title":"Machine Learning for Sustainable Power Systems: AIoT-Optimized Smart-Grid Inverter Systems with Solar Photovoltaics","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Photovoltaics; Inverter; Photovoltaic system; Computer science; Electrical engineering; Smart grid; Grid; Solar power; Power (physics); Engineering; Physics; Geography; Voltage","score_opus":0.007261338575775149,"score_gpt":0.18286440267329346,"score_spread":0.1756030640975183,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400006024","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":[0.00018323767,0.4031582,0.48197612,0.000021510714,0.010989454,0.004078381,0.00012277928,0.0011984141,0.09827192],"genre_scores_gemma":[0.9121291,0.002459484,0.0001296236,0.00004500108,0.0023061526,0.000479275,0.0005141964,0.0007973841,0.081139766],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968893,0.000063739644,0.00094694307,0.00075485953,0.00034289862,0.0010022455],"domain_scores_gemma":[0.9983468,0.00059880095,0.00024392446,0.00042886205,0.0001852024,0.0001963952],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007091084,0.0010366444,0.0015464977,0.00039758664,0.0002191553,0.00082845805,0.00023441284,0.0012217697,0.00001189481],"category_scores_gemma":[0.000038038845,0.0008193519,0.0001952812,0.00016465306,0.00007113471,0.00014845307,0.000088499655,0.0017035722,0.0000050977355],"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":[0.00008859872,0.000004652794,0.0001177605,0.0051709614,0.00047712747,0.00023880387,0.00025486972,0.9863698,0.0000056232893,0.0064739417,0.0006419016,0.00015595982],"study_design_scores_gemma":[0.00066750735,0.0001720709,3.7652347e-7,0.0050848154,0.00015612433,0.00018510678,0.000064015374,0.77814,0.0000023041,0.00014008251,0.21459556,0.0007920483],"about_ca_topic_score_codex":0.00062649895,"about_ca_topic_score_gemma":0.00014644269,"teacher_disagreement_score":0.9119459,"about_ca_system_score_codex":0.00029446426,"about_ca_system_score_gemma":0.000051112835,"threshold_uncertainty_score":0.9994257},"labels":[],"label_agreement":null},{"id":"W4400124708","doi":"10.1007/s12667-024-00684-6","title":"Learning causality structures from electricity demand data","year":2024,"lang":"en","type":"article","venue":"Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University; Cape Breton University","funders":"","keywords":"Causality (physics); Electricity demand; Electricity; Economics; Econometrics; Natural resource economics; Electricity generation; Engineering; Power (physics); Physics","score_opus":0.0187607792430436,"score_gpt":0.22661180416681193,"score_spread":0.20785102492376833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400124708","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":[0.56362796,0.12700893,0.23943742,0.000053809683,0.0163584,0.0001451682,0.0004078794,0.006417462,0.046542972],"genre_scores_gemma":[0.9973673,0.00016755867,0.000086804655,0.000009731365,0.0011799134,0.000006417416,0.00040679943,0.00004950436,0.00072594604],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989073,0.00008266114,0.00024342352,0.00032315005,0.00017508095,0.00026834026],"domain_scores_gemma":[0.9993039,0.00016338263,0.000020801765,0.00040489342,0.000011592794,0.000095461706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021213102,0.0001769573,0.000198644,0.00007600904,0.00008587201,0.00021621751,0.00029812256,0.00011325607,0.0000509203],"category_scores_gemma":[0.00003911861,0.0001588645,0.000033484434,0.00022517449,0.000014018558,0.00020424653,0.00007482797,0.0002427175,0.000014674195],"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":[0.000003849894,0.000004256623,0.0008537153,0.00021693646,0.00032414866,0.000104167186,0.00038471608,0.959361,0.0038371296,0.011098483,0.010987933,0.012823621],"study_design_scores_gemma":[0.000054793563,0.00000957451,0.00011246063,0.00008930756,0.000021478534,0.000018487042,0.00003500506,0.73275316,0.0011209265,0.00027038917,0.26531976,0.00019468537],"about_ca_topic_score_codex":0.003479122,"about_ca_topic_score_gemma":0.00026465754,"teacher_disagreement_score":0.43373936,"about_ca_system_score_codex":0.00005987673,"about_ca_system_score_gemma":0.000025618267,"threshold_uncertainty_score":0.6478306},"labels":[],"label_agreement":null},{"id":"W4400230935","doi":"10.1109/iscas58744.2024.10558077","title":"Efficient Probabilistic Optimal Power Flow Assessment Using an Adaptive Stochastic Spectral Embedding Surrogate Model","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probabilistic logic; Computer science; Embedding; Power flow; Surrogate model; Flow (mathematics); Power (physics); Mathematical optimization; Artificial intelligence; Machine learning; Mathematics; Electric power system","score_opus":0.025888217737165906,"score_gpt":0.2743421007255228,"score_spread":0.24845388298835688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400230935","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":[0.42100403,0.000100006924,0.5733511,0.000004184459,0.0005516933,0.00011105561,0.000015240805,0.0006320988,0.0042305905],"genre_scores_gemma":[0.8921424,7.1356766e-7,0.10753236,0.0000071935706,0.00011692581,0.00001222562,0.000010025551,0.00007954063,0.00009860916],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99852085,0.000022650956,0.00028386468,0.0003767849,0.0002600166,0.00053585856],"domain_scores_gemma":[0.99950147,0.000072302784,0.000015978221,0.00020275053,0.00003719388,0.00017030364],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023101797,0.00029812404,0.00021924893,0.00015563096,0.0001038666,0.00018502791,0.00013124126,0.000081915365,0.00014468328],"category_scores_gemma":[0.000010632337,0.00027323226,0.0000985533,0.00024085914,0.00003738766,0.00016428609,0.000046761495,0.00031438112,0.000015877733],"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":[0.0000065270424,0.000034243174,0.0000010184208,0.000046068755,0.000051311647,0.000030500225,0.00065026083,0.98922056,0.002301216,0.007310819,0.000016040787,0.00033142892],"study_design_scores_gemma":[0.00012412759,0.000089777386,0.0000067021642,0.0001943439,0.000045186443,0.000033648153,0.00016483448,0.99857587,0.00017611521,0.00023196496,0.0000065683366,0.0003508598],"about_ca_topic_score_codex":0.000013754809,"about_ca_topic_score_gemma":0.000011378,"teacher_disagreement_score":0.4711384,"about_ca_system_score_codex":0.0003324852,"about_ca_system_score_gemma":0.00009543903,"threshold_uncertainty_score":0.999972},"labels":[],"label_agreement":null},{"id":"W4400276706","doi":"10.1109/icsmartgrid61824.2024.10578263","title":"Campus Electric Load Forecasting Using Recurrent Neural Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial neural network; Computer science; Recurrent neural network; Electrical load; Artificial intelligence; Electrical engineering; Engineering; Voltage","score_opus":0.02171692908841549,"score_gpt":0.2283047402310211,"score_spread":0.2065878111426056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400276706","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":[0.7366058,0.020929048,0.18628375,0.000023624927,0.0077292,0.00015335,0.0000026689668,0.002812175,0.045460362],"genre_scores_gemma":[0.9979282,0.00006181855,0.0010374387,0.000022879758,0.00070935756,0.0000046116593,0.000004991964,0.0000597516,0.00017097169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989534,0.000012837129,0.00022671047,0.00019936511,0.00014125925,0.0004664502],"domain_scores_gemma":[0.99967813,0.000087458364,0.000012724302,0.00011284779,0.00002344317,0.00008538086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013890443,0.00019256254,0.00013843212,0.0001091954,0.00007546816,0.00013989609,0.000095699,0.000081676175,0.00007814695],"category_scores_gemma":[0.000018822888,0.0001737655,0.0000856248,0.0005602038,0.000008904634,0.00017415936,0.000028615117,0.00032809647,0.000011019534],"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":[0.0000016265494,0.0000029767405,0.00010139762,0.00006157172,0.000030962172,0.000048313814,0.00008565655,0.8080931,0.00080247765,0.00037991145,0.00075096986,0.18964109],"study_design_scores_gemma":[0.00005400127,0.000018893645,0.00000977297,0.00011659455,0.000019785008,0.00012637595,0.000009157289,0.9952666,0.0005582486,0.000046645233,0.0035558615,0.00021805051],"about_ca_topic_score_codex":0.00003143885,"about_ca_topic_score_gemma":0.000026047512,"teacher_disagreement_score":0.26132235,"about_ca_system_score_codex":0.00016441106,"about_ca_system_score_gemma":0.00002350396,"threshold_uncertainty_score":0.7085951},"labels":[],"label_agreement":null},{"id":"W4400311289","doi":"10.1016/j.epsr.2024.110781","title":"Interpretable short-term load forecasting via multi-scale temporal decomposition","year":2024,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Term (time); Scale (ratio); Decomposition; Computer science; Artificial intelligence; Geography; Cartography; Ecology","score_opus":0.045008155624529554,"score_gpt":0.32418440381717584,"score_spread":0.2791762481926463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400311289","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":[0.5260812,0.044657644,0.3560026,0.00003693659,0.0060695554,0.0010885318,0.000026826043,0.0024219165,0.0636148],"genre_scores_gemma":[0.9968396,0.00007445789,0.00039727768,0.000003252258,0.0003039345,0.00013181979,0.000025302299,0.0001166149,0.002107765],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968011,0.00018105646,0.0005197433,0.00048245367,0.00083559775,0.0011800426],"domain_scores_gemma":[0.9989002,0.0003017129,0.000018358427,0.00034715934,0.00020338688,0.00022919146],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017793775,0.00028822466,0.0003210999,0.0006888318,0.00024285825,0.0005564344,0.000370122,0.00020612284,0.000074151765],"category_scores_gemma":[0.000050370025,0.00027433955,0.00012598913,0.0015574592,0.000039085346,0.0004116999,0.00009429363,0.0010375268,0.00019360709],"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":[0.00015319996,0.0003486295,0.009198625,0.0044002472,0.0010698646,0.0019513746,0.008776221,0.045068774,0.76299256,0.00061617326,0.041670036,0.12375427],"study_design_scores_gemma":[0.00014138824,0.00014924201,0.00009320447,0.00082579535,0.000010190346,0.0003262164,0.000060929404,0.97723997,0.0076723727,0.00002135123,0.013128243,0.00033109717],"about_ca_topic_score_codex":0.0002725717,"about_ca_topic_score_gemma":0.00008235687,"teacher_disagreement_score":0.93217117,"about_ca_system_score_codex":0.0007867394,"about_ca_system_score_gemma":0.00013787665,"threshold_uncertainty_score":0.99997085},"labels":[],"label_agreement":null},{"id":"W4400378602","doi":"10.5194/ems2024-72","title":"On Monthly Mean Surface Wind Speed Data Homogenization and Trend Assessment","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Wind speed; Homogenization (climate); Environmental science; Meteorology; Climatology; Statistics; Atmospheric sciences; Econometrics; Mathematics; Geography; Geology","score_opus":0.03040216894329821,"score_gpt":0.26648832274811507,"score_spread":0.23608615380481685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400378602","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":[0.70086014,0.002184318,0.0011542196,0.00013274202,0.003983801,0.00023996943,0.00092066213,0.001050905,0.28947324],"genre_scores_gemma":[0.99291533,0.00015792325,0.002838841,0.000018340672,0.00019964726,3.466808e-7,0.0020381487,0.00008369246,0.0017477362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988957,0.000018471557,0.00023227162,0.00047904052,0.00018747171,0.00018703427],"domain_scores_gemma":[0.9991342,0.000057424757,0.00002811961,0.00069104455,0.000012001327,0.00007722009],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017568958,0.0002837453,0.00023068015,0.000088354616,0.00003474677,0.00020475243,0.00027338223,0.00018794303,0.00010238647],"category_scores_gemma":[0.000008204069,0.0002632531,0.000033201755,0.000100126046,0.000014911422,0.00006199458,0.0008778371,0.00051470107,0.000016432097],"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":[0.0000017881844,0.000010007014,0.000077112134,0.0002587956,0.00013564907,0.000014297424,0.00018309285,0.98839587,0.00018201582,0.0016894073,0.005892997,0.0031589419],"study_design_scores_gemma":[0.00011643719,0.000017732948,0.00014856337,0.00031600986,0.00007970099,0.0000021802246,0.000032884287,0.99340475,0.00034705593,0.001699043,0.0034844086,0.0003512084],"about_ca_topic_score_codex":0.00009147321,"about_ca_topic_score_gemma":0.00023260395,"teacher_disagreement_score":0.2920552,"about_ca_system_score_codex":0.000063549516,"about_ca_system_score_gemma":0.000027930968,"threshold_uncertainty_score":0.99998194},"labels":[],"label_agreement":null},{"id":"W4400646754","doi":"10.1109/tnse.2024.3427672","title":"Deep Reinforcement Learning-Assisted Federated Learning for Robust Short-Term Load Forecasting in Electricity Wholesale Markets","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network Science and Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brock University","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Term (time); Electricity; Computer science; Artificial intelligence; Q-learning; Machine learning; Engineering; Electrical engineering","score_opus":0.017290220078683437,"score_gpt":0.21258778726764177,"score_spread":0.19529756718895833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400646754","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":[0.14648822,0.0005592771,0.8506161,0.000009566194,0.000903351,0.00017702012,4.339233e-7,0.000571427,0.00067460013],"genre_scores_gemma":[0.99799156,0.0002115457,0.0013981833,0.000007562928,0.00013259338,0.00007578278,0.0000031977654,0.000052948893,0.00012664922],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99827975,0.000012554575,0.00031064547,0.00036379584,0.00029252007,0.0007407597],"domain_scores_gemma":[0.99949205,0.0002101954,0.000015635307,0.00008391569,0.00006589706,0.0001322855],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00077998836,0.00025380697,0.00020495667,0.00033582904,0.00046082248,0.00031709851,0.00012081897,0.000097364056,0.00000806079],"category_scores_gemma":[0.000029863824,0.0002698222,0.00006292086,0.0014994633,0.000045244953,0.00041382565,0.0000032136097,0.0006342762,0.000001794358],"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":[0.000013059919,0.00000582793,0.000051745603,0.00012893291,0.000018774384,0.000010860976,0.0001814994,0.93424636,0.0042056395,0.00001325342,0.000014365558,0.061109684],"study_design_scores_gemma":[0.00017018299,0.00008285732,0.0002331251,0.00049885735,0.00001796846,0.000032632808,0.000029414774,0.99574,0.0022473168,0.0000019902825,0.00064784253,0.00029781464],"about_ca_topic_score_codex":0.000008313143,"about_ca_topic_score_gemma":0.00005393421,"teacher_disagreement_score":0.8515033,"about_ca_system_score_codex":0.00034573968,"about_ca_system_score_gemma":0.00007881154,"threshold_uncertainty_score":0.9999754},"labels":[],"label_agreement":null},{"id":"W4400810197","doi":"10.1109/jsyst.2024.3420237","title":"A Hybrid Traffic Flow Forecasting and Risk-Averse Decision Strategy for Hydrogen-Based Integrated Traffic and Power Networks","year":2024,"lang":"en","type":"article","venue":"IEEE Systems Journal","topic":"Energy Load and Power Forecasting","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":"University of Alberta","funders":"Canada First Research Excellence Fund","keywords":"Power flow; Computer science; Traffic flow (computer networking); Power (physics); Operations research; Engineering; Electric power system; Computer network","score_opus":0.016634581272484978,"score_gpt":0.21925767030113696,"score_spread":0.20262308902865198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400810197","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":[0.82387704,0.010115794,0.1625427,0.0000035801438,0.0029040065,0.00019760928,0.000046336143,0.0002439948,0.00006890925],"genre_scores_gemma":[0.9979842,0.00029734563,0.00090637093,0.0000047041417,0.0006567583,0.000016720176,0.000010133243,0.000093488234,0.000030293937],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843925,0.00006922615,0.00056124874,0.00027224616,0.00018771431,0.0004703116],"domain_scores_gemma":[0.9990101,0.00045647315,0.000091107155,0.00010990146,0.000072853305,0.00025960332],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008284332,0.00031816302,0.00034854628,0.00025667055,0.00028668396,0.0005742116,0.00010066389,0.00014380345,0.000010915265],"category_scores_gemma":[0.000048958613,0.00026424706,0.00013279749,0.0001870968,0.0000371542,0.00022839534,0.000007225144,0.0006051324,0.0000022743193],"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":[0.000035023855,0.00000676023,0.000024690386,0.00013439049,0.00011964182,0.00014350876,0.00016569432,0.8942117,0.00009530827,0.000005207664,0.0010577118,0.10400036],"study_design_scores_gemma":[0.00072631234,0.00017292578,0.0000076456545,0.0012357328,0.0000819906,0.00203704,0.00011973069,0.99195415,0.000089671834,0.000016702343,0.003269414,0.00028866087],"about_ca_topic_score_codex":0.000008065122,"about_ca_topic_score_gemma":0.00002731045,"teacher_disagreement_score":0.17410712,"about_ca_system_score_codex":0.00008989123,"about_ca_system_score_gemma":0.000063351574,"threshold_uncertainty_score":0.999981},"labels":[],"label_agreement":null},{"id":"W4400946390","doi":"10.1371/journal.pone.0306874","title":"Green finance growth prediction model based on time-series conditional generative adversarial networks","year":2024,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Energy Load and Power Forecasting","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":"University of Prince Edward Island","funders":"","keywords":"Series (stratigraphy); Generative grammar; Time series; Econometrics; Adversarial system; Computer science; Artificial intelligence; Economics; Machine learning; Finance; Biology","score_opus":0.014567783518829414,"score_gpt":0.17659655910151129,"score_spread":0.16202877558268186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400946390","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":[0.15926738,0.0010966428,0.778218,0.000734386,0.0014546532,0.00049611967,0.0013669614,0.0031031668,0.054262716],"genre_scores_gemma":[0.9911554,0.000043953718,0.0058547766,0.00007951093,0.0010258203,0.000045023637,0.00042748783,0.000039621216,0.0013284007],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994001,0.00001147413,0.000118619006,0.00014887266,0.00017050067,0.00015046145],"domain_scores_gemma":[0.9998061,0.00004753631,0.000010435576,0.000076111726,0.000028573897,0.00003122322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000048589853,0.000119515156,0.000110204644,0.000056857643,0.00006276408,0.000032616077,0.000050709372,0.00007696015,0.00007952247],"category_scores_gemma":[0.000009972416,0.00012312036,0.000036739908,0.00011133693,0.000023101935,0.00018670985,0.0000088019615,0.00017397323,0.000041837557],"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":[0.000022065231,0.000043745273,0.00002317921,0.000052801795,0.000084525826,0.000006510826,0.00006419869,0.99289125,0.0017743937,0.0031600522,0.001720074,0.00015717662],"study_design_scores_gemma":[0.0001489634,0.00006648692,0.000035838497,0.0002402346,0.00003839421,6.614652e-7,0.0000010020035,0.9918986,0.006498176,0.00083248876,0.00012198095,0.000117181364],"about_ca_topic_score_codex":0.0000037436494,"about_ca_topic_score_gemma":0.0000032806247,"teacher_disagreement_score":0.831888,"about_ca_system_score_codex":0.000052098923,"about_ca_system_score_gemma":0.000022719534,"threshold_uncertainty_score":0.5020703},"labels":[],"label_agreement":null},{"id":"W4401155163","doi":"10.1016/j.eswa.2024.124900","title":"Robust drought forecasting in Eastern Canada: Leveraging EMD-TVF and ensemble deep RVFL for SPEI index forecasting","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Computer science; Index (typography); Ensemble forecasting; Probabilistic forecasting; Artificial intelligence; Weather forecasting; Meteorology; Geography","score_opus":0.033017089358276006,"score_gpt":0.21492470522490223,"score_spread":0.18190761586662624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401155163","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":[0.07338257,0.012735884,0.9051012,0.00013098663,0.00066536,0.0013473266,0.000031848074,0.00048638807,0.0061184755],"genre_scores_gemma":[0.99298733,0.000025140345,0.004181591,0.000026980688,0.000606295,0.0016572415,0.000041221334,0.000110782996,0.00036342314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984525,0.000016142701,0.00040458274,0.0004285979,0.00018907334,0.00050912483],"domain_scores_gemma":[0.9992484,0.00027232067,0.000055368615,0.0002363324,0.000052350766,0.00013521538],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001976352,0.00029157428,0.000298619,0.00017043354,0.00020553097,0.00019686116,0.00014687655,0.00009147912,0.0000033593558],"category_scores_gemma":[0.000016141583,0.00027121554,0.000036052406,0.00044522708,0.000025011337,0.00021316794,0.000034909484,0.00020697531,0.0000021762705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020876536,0.000024295226,0.006195702,0.0019390911,0.00019848044,0.00009273762,0.0061834375,0.9388741,0.0006453089,0.003095742,0.002109303,0.04062089],"study_design_scores_gemma":[0.00027324265,0.000014669485,0.000048175236,0.00076609873,0.000012128556,0.00020580766,0.001343767,0.9543965,0.00015417994,0.000033052605,0.04241569,0.0003366784],"about_ca_topic_score_codex":0.027214397,"about_ca_topic_score_gemma":0.13408093,"teacher_disagreement_score":0.9196048,"about_ca_system_score_codex":0.0002734679,"about_ca_system_score_gemma":0.00012235371,"threshold_uncertainty_score":0.999974},"labels":[],"label_agreement":null},{"id":"W4401206457","doi":"10.2316/j.2024.203-0530","title":"A NEW DESIGN OF VMD-BAIPSO-GRU POWER FORECASTING ALGORITHM, 1-10.","year":2024,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Power (physics); Computer science; Algorithm; Physics","score_opus":0.015557502724807553,"score_gpt":0.22177338389295756,"score_spread":0.20621588116815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401206457","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":[0.008725405,0.024274994,0.93586856,0.000090248395,0.0151849445,0.000037312442,0.000027786073,0.00011484931,0.015675886],"genre_scores_gemma":[0.9930716,0.00023744932,0.0039437683,0.000015316871,0.00083570386,0.0000016742713,0.000003628896,0.000042507472,0.0018483344],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99856573,0.00003500586,0.00065877644,0.00012036378,0.00043536734,0.00018475036],"domain_scores_gemma":[0.99924475,0.00018027172,0.00013566518,0.000076781485,0.00022544122,0.00013711592],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034646605,0.0001849984,0.00028482606,0.00035003896,0.00002238292,0.00015604863,0.0002542765,0.000091746595,0.00013983315],"category_scores_gemma":[0.000034419496,0.00015166684,0.00013676423,0.00013690235,0.000023073397,0.00032434083,0.000034303393,0.00016027433,0.0000037133611],"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":[0.00013756928,0.000057118345,0.00020419262,0.00019886774,0.0030034818,0.0012953337,0.003997132,0.70708483,0.006225256,0.012523087,0.043845136,0.22142798],"study_design_scores_gemma":[0.00086114294,0.00037611363,0.000055271586,0.0026953602,0.000074926276,0.0037394052,0.00036846584,0.7206759,0.0023615076,0.00060659455,0.2677447,0.00044064116],"about_ca_topic_score_codex":0.00008994339,"about_ca_topic_score_gemma":0.0000020845669,"teacher_disagreement_score":0.9843462,"about_ca_system_score_codex":0.000068754765,"about_ca_system_score_gemma":0.00007757409,"threshold_uncertainty_score":0.61847943},"labels":[],"label_agreement":null},{"id":"W4401442804","doi":"10.1038/s41598-024-69309-3","title":"Publisher Correction: Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm","year":2024,"lang":"en","type":"erratum","venue":"Scientific Reports","topic":"Energy Load and Power Forecasting","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 Prince Edward Island","funders":"","keywords":"Convolutional neural network; Long short term memory; Computer science; Feature selection; Feature (linguistics); Artificial intelligence; Selection (genetic algorithm); Term (time); Artificial neural network; Machine learning; Pattern recognition (psychology); Data mining; Algorithm; Recurrent neural network","score_opus":0.022245624348266577,"score_gpt":0.24024266939697134,"score_spread":0.21799704504870476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401442804","genre_codex":"editorial","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":[0.14113526,0.015258473,0.15826443,0.00003200816,0.67375094,0.0017755842,0.00029390253,0.0013136014,0.008175807],"genre_scores_gemma":[0.71066123,0.00009032939,0.012364128,0.000032484862,0.0040164734,0.00019615392,0.012510384,0.00065491773,0.25947392],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99480164,0.00011278467,0.0012284145,0.0016381124,0.0010356293,0.001183413],"domain_scores_gemma":[0.9982865,0.00007985031,0.00048602757,0.0005141989,0.0003859549,0.0002474899],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0012272397,0.0007700222,0.0009375407,0.00062392006,0.00042223572,0.00059005525,0.00026653768,0.0007616641,0.00003748679],"category_scores_gemma":[0.00015274399,0.00084918167,0.00035107002,0.0018291839,0.00029406085,0.0009542141,0.00014236404,0.0025861189,0.0000015235385],"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":[0.0000088074785,0.000054836342,0.00022317612,0.00015773483,0.00009817596,0.00014735524,0.00007969591,0.27516773,0.005480362,3.6759582e-7,0.7094462,0.009135533],"study_design_scores_gemma":[0.00018444029,0.000031717056,0.000018722181,0.0006567596,0.00012194746,0.0008621533,0.000015811567,0.9869934,0.0035599081,0.00005371752,0.0067595043,0.0007419449],"about_ca_topic_score_codex":0.00020373061,"about_ca_topic_score_gemma":0.0003779779,"teacher_disagreement_score":0.7118256,"about_ca_system_score_codex":0.0009847287,"about_ca_system_score_gemma":0.00061992236,"threshold_uncertainty_score":0.999715},"labels":[],"label_agreement":null},{"id":"W4401539250","doi":"10.1109/icphm61352.2024.10626498","title":"VoltaVistaMan: Energy Dynamics Intelligent Predictive Analysis Utilizing Bayesian Hyper-Tuned Neural Networks – A Case Study on Switzerland's National Electricity Demand","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Windsor","funders":"","keywords":"Bayesian probability; Computer science; Artificial neural network; Electricity; Dynamics (music); Energy (signal processing); Artificial intelligence; Machine learning; Engineering; Statistics; Electrical engineering; Mathematics","score_opus":0.011801347375571609,"score_gpt":0.23744327137849372,"score_spread":0.2256419240029221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401539250","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":[0.3463933,0.00045189515,0.6451261,0.00001798197,0.0004100859,0.000120137134,0.000017451997,0.00066832994,0.0067947037],"genre_scores_gemma":[0.99921185,0.000028406792,0.00008521299,0.000058451573,0.0002448444,0.000035004767,0.00008500461,0.000050043327,0.00020116314],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983764,0.00006839222,0.00039985313,0.0004187754,0.00034247583,0.00039406924],"domain_scores_gemma":[0.9992551,0.0002982384,0.00002959622,0.00017707876,0.00007374859,0.00016622219],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00028289517,0.00031056724,0.00030779158,0.00057815085,0.00016605762,0.0001980424,0.00012801061,0.00011296004,0.00006568733],"category_scores_gemma":[0.00003075526,0.00027388262,0.00019960356,0.0016432956,0.000019906736,0.00014154681,0.000040375726,0.0003416954,0.0000020817408],"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":[0.000022543814,0.00008587913,0.0038994276,0.000015845506,0.0012012774,0.0010448042,0.00047535458,0.97990936,0.000009203858,0.002069422,0.00012521505,0.011141642],"study_design_scores_gemma":[0.00011767142,0.00020526254,0.0001968467,0.000017663173,0.00036590823,0.0002426194,0.00089096784,0.9973494,0.00009058039,0.00009006876,0.00015369113,0.00027934002],"about_ca_topic_score_codex":0.0005167033,"about_ca_topic_score_gemma":0.00406358,"teacher_disagreement_score":0.65281856,"about_ca_system_score_codex":0.00036949894,"about_ca_system_score_gemma":0.000028734445,"threshold_uncertainty_score":0.99997133},"labels":[],"label_agreement":null},{"id":"W4401693650","doi":"10.1109/ictem60690.2024.10631923","title":"Residential Electricity Demand Forecasting Employing a Highly Accurate BiLSTM Intelligent Model","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Demand forecasting; Electricity demand; Electricity; Computer science; Electricity generation; Operations research; Engineering; Electrical engineering; Power (physics)","score_opus":0.03518947219633925,"score_gpt":0.24721890785802267,"score_spread":0.21202943566168342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401693650","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":[0.3022942,0.001961868,0.67190915,0.00005618445,0.0008343239,0.00010665789,0.00000556429,0.0018884449,0.020943588],"genre_scores_gemma":[0.9949328,0.00015118487,0.0033994846,0.00004576745,0.000300493,0.000017182998,0.000009320857,0.000075845026,0.0010679716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864054,0.000017494867,0.0003656324,0.0002898561,0.00019047923,0.0004960175],"domain_scores_gemma":[0.99951905,0.00013441785,0.00001851638,0.00016101063,0.000028227703,0.00013878554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026684752,0.00023851855,0.00018493038,0.00024137096,0.00012387898,0.00026854643,0.00016490754,0.00010200983,0.000079859325],"category_scores_gemma":[0.000058389865,0.00021258589,0.00011347681,0.00047587702,0.00001704255,0.00033756372,0.00005940792,0.0003044367,0.000044603574],"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":[0.0000074357795,0.000007073219,0.000067376146,0.0002117793,0.00008793913,0.00007154306,0.0005782618,0.9691137,0.0068101767,0.003866739,0.003071991,0.016105978],"study_design_scores_gemma":[0.00006289463,0.000018305516,0.0000043872146,0.00013507018,0.000027154907,0.000040996154,0.000019257077,0.9635708,0.03228385,0.0018131731,0.0017601823,0.00026397407],"about_ca_topic_score_codex":0.00006030382,"about_ca_topic_score_gemma":0.000119195385,"teacher_disagreement_score":0.6926386,"about_ca_system_score_codex":0.00011057454,"about_ca_system_score_gemma":0.000049810646,"threshold_uncertainty_score":0.8669001},"labels":[],"label_agreement":null},{"id":"W4401694231","doi":"10.1109/isit57864.2024.10619471","title":"An Information-Theoretic Framework for Out-of-Distribution Generalization","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 British Columbia","funders":"","keywords":"Generalization; Computer science; Theoretical computer science; Mathematics","score_opus":0.009523567178132675,"score_gpt":0.24463535638806427,"score_spread":0.2351117892099316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401694231","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":[0.010074871,0.00010259815,0.9854915,0.000017307168,0.0013010114,0.000057289362,0.000053244323,0.00036825697,0.0025339061],"genre_scores_gemma":[0.9834493,0.000021187554,0.015667327,0.000018780096,0.00016631374,0.000013294759,0.00063104054,0.000010401265,0.000022391394],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99968237,0.000003712005,0.00014311213,0.000039378865,0.00004817655,0.00008323927],"domain_scores_gemma":[0.9998245,0.00004223515,0.0000089293235,0.0000712411,0.000029203933,0.000023847724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007156743,0.000054472435,0.000049543287,0.000034981553,0.000020639349,0.00004835889,0.00004140577,0.00005912823,0.00005756549],"category_scores_gemma":[0.000024078072,0.000049736627,0.000027983615,0.00009624093,0.000008933866,0.00033285326,0.000002881353,0.000037830196,0.000011731889],"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":[0.0000014375274,0.0000021514068,0.0000175536,0.00018097856,0.000010577429,7.711886e-8,0.000456874,0.096131235,0.00024574072,0.89147776,0.0012530275,0.010222609],"study_design_scores_gemma":[0.000038307546,0.000027302125,0.000015606056,0.0000746187,0.000010055064,5.641648e-7,0.000035355002,0.9231938,0.0093036685,0.027079664,0.04013504,0.00008601916],"about_ca_topic_score_codex":0.0000017740042,"about_ca_topic_score_gemma":0.0000016625838,"teacher_disagreement_score":0.97337437,"about_ca_system_score_codex":0.000017340537,"about_ca_system_score_gemma":0.000007159336,"threshold_uncertainty_score":0.20282008},"labels":[],"label_agreement":null},{"id":"W4401808903","doi":"10.3390/jrfm17090380","title":"Improving Volatility Forecasting: A Study through Hybrid Deep Learning Methods with WGAN","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"Volatility (finance); Artificial intelligence; Econometrics; Computer science; Machine learning; Economics","score_opus":0.011636443452184092,"score_gpt":0.24057284025506173,"score_spread":0.22893639680287764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401808903","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":[0.4013025,0.002472537,0.5941875,0.0000030114331,0.0004599783,0.00008827491,9.3054655e-7,0.00006197121,0.0014232813],"genre_scores_gemma":[0.95424557,0.00037546,0.04505127,0.0000053806048,0.0002531535,0.0000039472807,4.9082564e-7,0.00002680155,0.000037948885],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990431,0.00006741599,0.00035673557,0.00014915237,0.00017219414,0.00021138706],"domain_scores_gemma":[0.9996206,0.00010704365,0.00009797123,0.000082070546,0.000037200884,0.000055095374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088849437,0.00016464395,0.00024541884,0.00013548214,0.0001367892,0.00012206741,0.000091248585,0.000026668538,0.000006456669],"category_scores_gemma":[0.00006801721,0.00012555512,0.00006853004,0.00022445909,0.000020444608,0.00027405334,0.00005572665,0.0004720479,6.047352e-7],"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":[0.00003865526,0.000028488475,0.004440706,0.00019978303,0.000087782195,0.00039195502,0.0027029647,0.016200507,0.000012121942,0.00023319753,0.000026310747,0.97563756],"study_design_scores_gemma":[0.002118435,0.002229143,0.021239636,0.0010640343,0.0011126148,0.00038246313,0.0039699576,0.82391655,0.00036156384,0.0037020305,0.13905218,0.0008513668],"about_ca_topic_score_codex":0.000028785877,"about_ca_topic_score_gemma":0.00002763876,"teacher_disagreement_score":0.97478616,"about_ca_system_score_codex":0.000042566033,"about_ca_system_score_gemma":0.0000104316805,"threshold_uncertainty_score":0.51199895},"labels":[],"label_agreement":null},{"id":"W4401877224","doi":"10.36227/techrxiv.172469941.17523365/v1","title":"From C.elegans to Liquid Neural Networks: A Robust Wind Power Multi-Time Scale Prediction Framework","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Artificial neural network; Scale (ratio); Wind power; Computer science; Artificial intelligence; Engineering; Electrical engineering; Geography; Cartography","score_opus":0.011687175811230613,"score_gpt":0.21581921489430372,"score_spread":0.20413203908307312,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401877224","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":[0.59813786,0.0025628111,0.36219487,0.00025743252,0.018604267,0.0006238785,0.0005625874,0.0047274902,0.012328806],"genre_scores_gemma":[0.96315044,0.00004491128,0.02996353,0.00022799846,0.003821742,0.00006532372,0.00037932524,0.0002927751,0.0020539581],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978193,0.000036292517,0.0005345013,0.00075833936,0.00026025056,0.00059130415],"domain_scores_gemma":[0.998817,0.000119859054,0.000043077136,0.0006672544,0.00005039191,0.00030237058],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013937631,0.00060593133,0.00049930403,0.00019754235,0.00006949054,0.00029069505,0.0004070193,0.0009633237,0.0009168988],"category_scores_gemma":[0.000035231773,0.00059996225,0.0002603684,0.00030780578,0.00002699906,0.00008994456,0.00077607273,0.0020749934,0.0003517658],"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":[0.000024394318,0.000020963978,0.000060917475,0.000064124164,0.00017468352,0.000021818554,0.0011759361,0.9909595,0.00031073616,0.000020044536,0.00648914,0.00067768776],"study_design_scores_gemma":[0.00010752316,0.0000694194,0.00013417164,0.0011601237,0.00008222876,0.000005458588,0.000060308223,0.9944822,0.0003797391,0.00020176127,0.0027164088,0.00060061313],"about_ca_topic_score_codex":0.00026401874,"about_ca_topic_score_gemma":0.00008848259,"teacher_disagreement_score":0.3650126,"about_ca_system_score_codex":0.00014672322,"about_ca_system_score_gemma":0.000026117808,"threshold_uncertainty_score":0.9999964},"labels":[],"label_agreement":null},{"id":"W4401879017","doi":"10.1109/compsac61105.2024.00276","title":"Neural Network Fuzzy Electricity Demand Forecasts Based on Fuzzy Inputs","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Thompson Rivers University; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fuzzy logic; Computer science; Artificial neural network; Electricity; Neuro-fuzzy; Electricity demand; Fuzzy control system; Artificial intelligence; Electricity generation; Power (physics); Engineering; Electrical engineering","score_opus":0.010932051261626765,"score_gpt":0.21200927458311974,"score_spread":0.20107722332149297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401879017","genre_codex":"other","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":[0.3394996,0.0024625387,0.04304717,0.0005143893,0.0045253737,0.0002638147,0.000011899537,0.004326417,0.60534877],"genre_scores_gemma":[0.99726355,0.000018686485,0.00079591246,0.00051703944,0.000747024,0.000012903923,0.000018113255,0.000058165864,0.0005685984],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988815,0.000024188652,0.00020064309,0.00023397351,0.00016703823,0.00049264793],"domain_scores_gemma":[0.9994816,0.00018271423,0.000010485099,0.00017526245,0.000013114304,0.00013678819],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001721196,0.00022286738,0.0001647842,0.00012399971,0.00008009534,0.00011047811,0.00011949674,0.00010330792,0.00011712609],"category_scores_gemma":[0.000018938172,0.00019023246,0.00009949642,0.00054279587,0.000013620503,0.00011265975,0.000017864733,0.0002982522,0.00007943032],"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":[0.000010355901,0.0000080026,0.0003069611,0.00008032106,0.000025932008,0.000053298423,0.000036414138,0.9564927,0.00019303155,0.0031670697,0.016931012,0.022694867],"study_design_scores_gemma":[0.00014131574,0.00008001579,0.00020766232,0.00009127823,0.000014166495,0.000013304826,0.0000011534238,0.9797111,0.0015928209,0.00097379234,0.016925467,0.0002478893],"about_ca_topic_score_codex":0.000011780571,"about_ca_topic_score_gemma":0.000057092737,"teacher_disagreement_score":0.65776396,"about_ca_system_score_codex":0.0000635641,"about_ca_system_score_gemma":0.000021679913,"threshold_uncertainty_score":0.77574545},"labels":[],"label_agreement":null},{"id":"W4402041245","doi":"10.1016/j.renene.2024.121263","title":"Prediction of long-term photovoltaic power generation in the context of climate change","year":2024,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"Science and Technology Project of State Grid; National Key Research and Development Program of China; State Grid Corporation of China","keywords":"Term (time); Photovoltaic system; Context (archaeology); Climate change; Environmental science; Meteorology; Engineering; Electrical engineering; Geography; Geology; Physics","score_opus":0.027087508590089418,"score_gpt":0.22087884395901972,"score_spread":0.19379133536893028,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402041245","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":[0.9728685,0.008819851,0.002950731,0.000015567966,0.0012481546,0.000099406185,0.00006809145,0.00014471244,0.013784959],"genre_scores_gemma":[0.9977422,0.0017945933,0.00003171534,0.00002574741,0.00020199557,0.00003669411,0.000077338154,0.000022562126,0.000067199624],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932,0.000027608987,0.0002541572,0.000109821514,0.0001259644,0.00016249911],"domain_scores_gemma":[0.9997484,0.000037942253,0.00002777281,0.0001486596,0.000019043855,0.000018201305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015975795,0.000100752455,0.00013323192,0.00013043942,0.00001854068,0.000017948165,0.00008722669,0.00007101764,0.000044112272],"category_scores_gemma":[0.0000053103167,0.00007978283,0.000052893065,0.00027590577,0.0000183351,0.00013947938,0.000014045201,0.000055503155,0.000001041261],"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":[0.000014167145,0.00007158303,0.006236896,0.00057300634,0.000095963536,0.00003893899,0.0043561,0.5162018,0.43300006,0.005682001,0.0012284637,0.03250105],"study_design_scores_gemma":[0.0003684368,0.00015101799,0.0036638575,0.00070550904,0.000038907205,0.000029104205,0.0002096493,0.516381,0.46972027,0.000085744265,0.008425667,0.00022081748],"about_ca_topic_score_codex":0.0012373851,"about_ca_topic_score_gemma":0.001698435,"teacher_disagreement_score":0.03672022,"about_ca_system_score_codex":0.000028167715,"about_ca_system_score_gemma":0.0000094618845,"threshold_uncertainty_score":0.32534495},"labels":[],"label_agreement":null},{"id":"W4402121887","doi":"10.1016/j.ijepes.2024.110206","title":"Day-Ahead electricity price forecasting using a CNN-BiLSTM model in conjunction with autoregressive modeling and hyperparameter optimization","year":2024,"lang":"en","type":"article","venue":"International Journal of Electrical Power & Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Particle swarm optimization; Hyperparameter; Computer science; Electricity market; Electricity; Autoregressive model; Mean squared error; Electricity price forecasting; Convolutional neural network; Autoregressive integrated moving average; Random forest; Artificial intelligence; Machine learning; Econometrics; Time series; Economics; Engineering; Statistics; Mathematics","score_opus":0.01866427074617038,"score_gpt":0.22981138428962108,"score_spread":0.2111471135434507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402121887","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":[0.23348661,0.0039618225,0.76061445,0.000020429228,0.0010176204,0.000050255952,0.0000026002633,0.00006429501,0.0007819108],"genre_scores_gemma":[0.99559385,0.00015611606,0.0037744972,0.0000256993,0.00034629827,0.000005963423,0.0000050173885,0.000053334592,0.000039228078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818534,0.000063929,0.0006953407,0.00022334632,0.0005180554,0.00031400152],"domain_scores_gemma":[0.99914324,0.00018458192,0.00015942921,0.000073033065,0.00033172866,0.00010799023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038148309,0.00023594215,0.00032521307,0.0007534247,0.000044287008,0.00025318502,0.00018248329,0.00013653873,0.0000042781885],"category_scores_gemma":[0.00008699018,0.00019254022,0.00007878202,0.00042609984,0.00002119482,0.0006265965,0.000022597773,0.00041326167,2.5987157e-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":[0.00007150473,0.000023059956,0.00018318328,0.000017896544,0.00018223426,0.00015335878,0.00019420982,0.99496657,0.0013810595,0.0011512619,0.000024859852,0.0016508076],"study_design_scores_gemma":[0.00040179823,0.000121695855,0.000004112949,0.00070332276,0.00003419996,0.0011040819,0.00002067557,0.9968054,0.00028810062,0.00015701559,0.00014745121,0.00021211265],"about_ca_topic_score_codex":0.00010796742,"about_ca_topic_score_gemma":0.000009423569,"teacher_disagreement_score":0.76210725,"about_ca_system_score_codex":0.00050331105,"about_ca_system_score_gemma":0.0001269979,"threshold_uncertainty_score":0.7851562},"labels":[],"label_agreement":null},{"id":"W4402156730","doi":"10.1109/icc51166.2024.10622466","title":"Privacy-preserving, Lightweight, and Decentralized Load Forecasting in Smart Grid AMI Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Smart grid; Computer science; Grid; Computer network; Distributed computing; Computer security; Engineering; Electrical engineering","score_opus":0.014338109895827875,"score_gpt":0.2118249829569698,"score_spread":0.19748687306114193,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402156730","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":[0.8725864,0.020744529,0.012003141,0.00017826067,0.0028244045,0.00021246006,0.0000051303764,0.0015101061,0.089935556],"genre_scores_gemma":[0.9965368,0.00083311077,0.0017571734,0.000041567248,0.00030168614,0.000014581979,0.000011944426,0.000059742764,0.00044341607],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988072,0.000019296433,0.00029834764,0.0002562864,0.00013460535,0.00048428078],"domain_scores_gemma":[0.9995195,0.00015119003,0.000012530359,0.00018657441,0.000015670757,0.000114506955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002221039,0.0002138081,0.00020033198,0.00011476865,0.000047169346,0.00017168718,0.00017132954,0.00011404562,0.00018571626],"category_scores_gemma":[0.00004891259,0.00018877118,0.000050316026,0.00034656504,0.000020385538,0.00026376682,0.00013849064,0.00028767195,0.0000124576945],"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":[0.00007084573,0.00007670804,0.0848263,0.0020208273,0.0004551435,0.0011584357,0.0057745404,0.63781077,0.0020443115,0.014356302,0.077197954,0.17420787],"study_design_scores_gemma":[0.00024513385,0.000013079354,0.00079014286,0.00041638882,0.000010748261,0.000035449266,0.000018081126,0.91872126,0.00059962436,0.0002660337,0.07864093,0.0002431055],"about_ca_topic_score_codex":0.00011348907,"about_ca_topic_score_gemma":0.0005167011,"teacher_disagreement_score":0.28091052,"about_ca_system_score_codex":0.00008013472,"about_ca_system_score_gemma":0.000018602175,"threshold_uncertainty_score":0.76978654},"labels":[],"label_agreement":null},{"id":"W4402188009","doi":"10.32920/26866597","title":"Machine Learning Prediction of Short-Term Solar PV and Wind Farm Power Generation in Ontario","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Term (time); Meteorology; Environmental science; Wind power; Photovoltaic system; Solar power; Solar wind; Power (physics); Engineering; Electrical engineering; Physics; Astronomy","score_opus":0.018556417870424263,"score_gpt":0.2079563449387485,"score_spread":0.18939992706832426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402188009","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":[0.9759322,0.0013329682,0.0011658735,0.0000067482356,0.0010230406,0.000112066235,0.000018323335,0.00017624057,0.020232536],"genre_scores_gemma":[0.99783754,0.00013996515,0.0005180465,0.0000035657586,0.000110969864,0.0000067436854,0.0002897729,0.000041413812,0.001051989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991176,0.000017393379,0.0003404744,0.00025552395,0.000118962256,0.00015001652],"domain_scores_gemma":[0.99977666,0.000012297903,0.000025723624,0.00012777744,0.000018919436,0.000038640017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017118292,0.00021031388,0.00023269805,0.0001935789,0.000022382927,0.00005644022,0.000058918118,0.00024968,0.00012180327],"category_scores_gemma":[0.0000069116622,0.00020930525,0.000052448693,0.000055252407,0.000013363575,0.000042029817,0.0001884216,0.0010811288,0.0000019027019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005055819,0.000013577851,0.0687145,0.00026256646,0.000098156954,0.000014116617,0.0027950837,0.91248274,0.010500566,0.0003745957,0.00003779903,0.004701249],"study_design_scores_gemma":[0.00021428135,0.00006783963,0.023589952,0.0005173507,0.00006268428,0.000017741937,0.00003925851,0.9663697,0.0052932138,0.00024380499,0.0032204087,0.00036378903],"about_ca_topic_score_codex":0.005800377,"about_ca_topic_score_gemma":0.076354675,"teacher_disagreement_score":0.0705543,"about_ca_system_score_codex":0.00014163918,"about_ca_system_score_gemma":0.000037188252,"threshold_uncertainty_score":0.9404994},"labels":[],"label_agreement":null},{"id":"W4402197611","doi":"10.32920/26866597.v1","title":"Machine Learning Prediction of Short-Term Solar PV and Wind Farm Power Generation in Ontario","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Term (time); Environmental science; Photovoltaic system; Wind power; Solar power; Meteorology; Solar wind; Power (physics); Computer science; Engineering; Electrical engineering; Physics; Astronomy; Thermodynamics","score_opus":0.018556417870424263,"score_gpt":0.2079563449387485,"score_spread":0.18939992706832426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402197611","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":[0.9759322,0.0013329682,0.0011658735,0.0000067482356,0.0010230406,0.000112066235,0.000018323335,0.00017624057,0.020232536],"genre_scores_gemma":[0.99783754,0.00013996515,0.0005180465,0.0000035657586,0.000110969864,0.0000067436854,0.0002897729,0.000041413812,0.001051989],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991176,0.000017393379,0.0003404744,0.00025552395,0.000118962256,0.00015001652],"domain_scores_gemma":[0.99977666,0.000012297903,0.000025723624,0.00012777744,0.000018919436,0.000038640017],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017118292,0.00021031388,0.00023269805,0.0001935789,0.000022382927,0.00005644022,0.000058918118,0.00024968,0.00012180327],"category_scores_gemma":[0.0000069116622,0.00020930525,0.000052448693,0.000055252407,0.000013363575,0.000042029817,0.0001884216,0.0010811288,0.0000019027019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005055819,0.000013577851,0.0687145,0.00026256646,0.000098156954,0.000014116617,0.0027950837,0.91248274,0.010500566,0.0003745957,0.00003779903,0.004701249],"study_design_scores_gemma":[0.00021428135,0.00006783963,0.023589952,0.0005173507,0.00006268428,0.000017741937,0.00003925851,0.9663697,0.0052932138,0.00024380499,0.0032204087,0.00036378903],"about_ca_topic_score_codex":0.005800377,"about_ca_topic_score_gemma":0.076354675,"teacher_disagreement_score":0.0705543,"about_ca_system_score_codex":0.00014163918,"about_ca_system_score_gemma":0.000037188252,"threshold_uncertainty_score":0.9404994},"labels":[],"label_agreement":null},{"id":"W4402263682","doi":"10.23919/acc60939.2024.10644570","title":"Developing a Time-Efficient Model for Solid Oxide Fuel Cells Using Self-Supervised Convolutional Autoencoder and Stateful LSTM Network","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"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; Cummins Incorporated","keywords":"Stateful firewall; Autoencoder; Computer science; Convolutional neural network; Artificial intelligence; Deep learning; Computer network","score_opus":0.021718119605640542,"score_gpt":0.23733073732148696,"score_spread":0.21561261771584642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402263682","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12292152,0.0012558209,0.8732198,0.000027026987,0.00040355284,0.00016555226,0.000025018719,0.0007608218,0.0012208831],"genre_scores_gemma":[0.4836637,0.00005762969,0.5152286,0.00010472953,0.00020272179,0.000023184017,0.000024616611,0.000079898084,0.00061486376],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988886,0.000009553764,0.0002672686,0.000250892,0.00012374479,0.0004599143],"domain_scores_gemma":[0.9996473,0.000116154646,0.00001362241,0.000088563975,0.000043807515,0.00009053727],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001945188,0.00020668334,0.0001834277,0.000076129865,0.00011848216,0.00011100284,0.00007163875,0.00008523019,0.000027858396],"category_scores_gemma":[0.000004533519,0.00019720233,0.00006159469,0.0001681202,0.000020596215,0.00011134979,0.000047425056,0.000109317436,0.000013517453],"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":[0.0000036407764,0.0000053713693,0.0000063332923,0.00026595857,0.00007294343,0.0000032108683,0.0004547596,0.9888434,0.0039859903,0.005284968,0.00077201583,0.00030143003],"study_design_scores_gemma":[0.00018005213,0.0000073694514,0.0000049035943,0.0001711203,0.000032245593,0.000012762417,0.00001992636,0.9956802,0.001086481,0.0009146164,0.0016053076,0.00028497283],"about_ca_topic_score_codex":0.000007294608,"about_ca_topic_score_gemma":0.000006679672,"teacher_disagreement_score":0.36074218,"about_ca_system_score_codex":0.00013639645,"about_ca_system_score_gemma":0.00012064512,"threshold_uncertainty_score":0.80416775},"labels":[],"label_agreement":null},{"id":"W4402277154","doi":"10.3390/en17174457","title":"Short-Term Campus Load Forecasting Using CNN-Based Encoder–Decoder Network with Attention","year":2024,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","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":"Memorial University of Newfoundland","funders":"","keywords":"Term (time); Encoder; Computer science; Real-time computing; Artificial intelligence","score_opus":0.02056230892530006,"score_gpt":0.22412519987706744,"score_spread":0.20356289095176738,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402277154","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":[0.94430643,0.0035577957,0.04345439,0.00001324942,0.0015482954,0.00006891328,0.000005329991,0.0010686925,0.0059768753],"genre_scores_gemma":[0.99050313,0.000026088283,0.0082763545,0.000019211737,0.0007743945,0.000017209693,0.000028756314,0.00010097568,0.00025385295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987162,0.000020465875,0.000252179,0.0002793126,0.00024360664,0.00048825404],"domain_scores_gemma":[0.99956167,0.00010825112,0.0000223363,0.00019257201,0.000041676885,0.0000735162],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016572376,0.0002771384,0.00020689974,0.00010001925,0.00015757392,0.0002032575,0.00011504096,0.00010199779,0.0000403813],"category_scores_gemma":[0.000009466768,0.0002404064,0.00009330042,0.0003546537,0.00004545155,0.00026022235,0.000027807379,0.00021338029,0.000008842517],"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":[0.0000074832597,0.0000046225896,0.002926368,0.00015099118,0.00007226037,0.0000972523,0.00016054302,0.9813571,0.0021817614,0.00026391365,0.00032840026,0.01244933],"study_design_scores_gemma":[0.00013413612,0.000030668307,0.00041850732,0.0009616511,0.00006323819,0.000055449596,0.0000508419,0.9925776,0.002533505,0.000084509076,0.0027051473,0.00038474525],"about_ca_topic_score_codex":0.00003441139,"about_ca_topic_score_gemma":0.00014656292,"teacher_disagreement_score":0.0461967,"about_ca_system_score_codex":0.00014378005,"about_ca_system_score_gemma":0.00006258148,"threshold_uncertainty_score":0.9803488},"labels":[],"label_agreement":null},{"id":"W4402303170","doi":"10.1109/icesep62218.2024.10651689","title":"A weighted integrated forecasting approach based on VMD decomposition reconstruction for high energy-consuming loads","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"McMaster University","funders":"","keywords":"Decomposition; Computer science; Energy (signal processing); Artificial intelligence; Mathematics; Statistics; Chemistry","score_opus":0.01430451697102185,"score_gpt":0.21162208269079066,"score_spread":0.19731756571976883,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402303170","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":[0.041199297,0.00019586379,0.92075366,0.000043350898,0.0017287811,0.00015427383,0.000027781985,0.0016286852,0.03426834],"genre_scores_gemma":[0.91234106,0.0000069504904,0.086462565,0.00006610999,0.00027088242,0.00010837749,0.00034025405,0.000076998826,0.00032682504],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988636,0.000022544416,0.00033428895,0.00032654533,0.000119840784,0.00033320623],"domain_scores_gemma":[0.99944174,0.00024232225,0.000027068618,0.00014320044,0.000068151225,0.0000775427],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017656256,0.00026195994,0.0002007742,0.0003285036,0.00012831297,0.00015506624,0.00008621322,0.00015511733,0.00008865472],"category_scores_gemma":[0.000023868848,0.00023365587,0.00011351066,0.00040893353,0.000024158702,0.00020517103,0.000009094261,0.00019014072,0.0000052993023],"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":[0.000083540894,0.000050052677,0.000053626307,0.00058412936,0.0001766791,0.000014776194,0.00013685496,0.14264035,0.008263317,0.058737155,0.0021539007,0.7871056],"study_design_scores_gemma":[0.0003154106,0.000068363894,0.0000045393094,0.0004651282,0.000034549576,0.000049676306,0.00004279981,0.9630057,0.03173896,0.0009485116,0.0030425761,0.000283793],"about_ca_topic_score_codex":0.000045442783,"about_ca_topic_score_gemma":0.000020725683,"teacher_disagreement_score":0.87114173,"about_ca_system_score_codex":0.00015173799,"about_ca_system_score_gemma":0.000032928696,"threshold_uncertainty_score":0.95282096},"labels":[],"label_agreement":null},{"id":"W4402313467","doi":"10.3390/s24175802","title":"Affinity-Driven Transfer Learning for Load Forecasting","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Load and Power Forecasting","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":"Computer science; Machine learning; Operationalization; Transfer of learning; Artificial intelligence; Robustness (evolution); Task (project management); Similarity (geometry); Data mining; Engineering","score_opus":0.023751216942040163,"score_gpt":0.22417751458937696,"score_spread":0.2004262976473368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402313467","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":[0.9565164,0.0007894261,0.010707577,0.000049036633,0.0012532021,0.00012965496,0.000011637534,0.0014253187,0.02911775],"genre_scores_gemma":[0.9968596,0.000035488043,0.0013069093,0.000011712257,0.0004068076,0.000015245031,0.0000139528065,0.00008096105,0.0012692909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920017,0.000013476733,0.00016942418,0.00017631204,0.000111024914,0.0003295906],"domain_scores_gemma":[0.999595,0.00023531925,0.000004602721,0.000076054646,0.000026622876,0.000062397754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014192029,0.00015518848,0.00013683274,0.0000777474,0.00009672489,0.00007712854,0.000067356406,0.000081985556,0.0000446553],"category_scores_gemma":[0.00005443574,0.00015366789,0.00012087745,0.00016543691,0.000016785634,0.000081752165,0.000007920088,0.00025847557,0.0000418929],"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":[0.0000058388227,0.0000030130236,0.00006641206,0.0002943794,0.00006038368,0.00002736859,0.0017040648,0.9622518,0.0031589423,0.0010894174,0.00040624075,0.030932186],"study_design_scores_gemma":[0.0001342945,0.000035716468,0.0000116263645,0.00019046188,0.00002344141,0.000023113626,0.000110488814,0.86265665,0.005391859,0.00010286906,0.13111414,0.00020531852],"about_ca_topic_score_codex":0.0000070457786,"about_ca_topic_score_gemma":0.000026603531,"teacher_disagreement_score":0.1307079,"about_ca_system_score_codex":0.000055421515,"about_ca_system_score_gemma":0.000019782781,"threshold_uncertainty_score":0.6266394},"labels":[],"label_agreement":null},{"id":"W4402473806","doi":"10.1109/ccece59415.2024.10667334","title":"An Updated Review and Comparison of Wind Power Ramp Detection Techniques","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Calgary; University of Guelph","funders":"","keywords":"Wind power; Computer science; Electrical engineering; Engineering","score_opus":0.0114210641808669,"score_gpt":0.2683386943809991,"score_spread":0.2569176302001322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402473806","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":[0.62905276,0.16084054,0.064681046,0.00015042095,0.0009815095,0.0005608748,0.00001841117,0.0059358357,0.13777858],"genre_scores_gemma":[0.99834275,0.00059087056,0.00094132876,0.00002379325,0.000015902919,0.0000017203203,0.000008017566,0.000014011362,0.00006161293],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996726,0.000008298465,0.00013879452,0.00007464242,0.000038780283,0.00006686624],"domain_scores_gemma":[0.99986845,0.000008591107,0.000007904302,0.00007709049,0.000011999427,0.00002597165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000080948405,0.0000648823,0.00012366078,0.000045723817,0.000011965679,0.000017106951,0.000030526167,0.000039102255,0.00014391627],"category_scores_gemma":[0.0000024203275,0.00005453287,0.000016711352,0.0001264467,0.000010582793,0.00010498868,0.0000064676265,0.00007681335,0.0000028508873],"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":[0.000006193548,0.000049546958,0.0007108982,0.004412093,0.00016419461,0.0000070210945,0.000789957,0.0013937298,0.42302254,0.0023991293,0.012768678,0.55427605],"study_design_scores_gemma":[0.000050786457,0.0001658335,0.00018845324,0.0012115653,0.00008322547,0.000021530868,0.000046489462,0.05629495,0.7965051,0.000100614416,0.14506997,0.00026150182],"about_ca_topic_score_codex":0.000009913414,"about_ca_topic_score_gemma":0.0000131072175,"teacher_disagreement_score":0.5540145,"about_ca_system_score_codex":0.0000074613486,"about_ca_system_score_gemma":0.0000022562983,"threshold_uncertainty_score":0.22237858},"labels":[],"label_agreement":null},{"id":"W4402474017","doi":"10.1109/ccece59415.2024.10667188","title":"Evaluating Solar Power Forecasting Robustness: A Comparative Analysis of XGBoost, RNN, KNN, RF, and LSTM with emphasis on Lagged Steps, Sensitivity, and Cross-Validation Techniques","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Robustness (evolution); Computer science; Sensitivity (control systems); Artificial intelligence; Machine learning; Engineering; Electronic engineering","score_opus":0.05150315047417743,"score_gpt":0.3243223910168797,"score_spread":0.2728192405427023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402474017","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":[0.95872986,0.000393356,0.034984358,0.000015741272,0.000048869642,0.00017125998,0.000030118741,0.00038323042,0.0052432115],"genre_scores_gemma":[0.98603785,0.000028959772,0.013751392,0.000013168167,0.000031865482,0.00001471812,0.000030490273,0.000031883745,0.000059687656],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987299,0.00007569962,0.00034358428,0.00034750893,0.00026563765,0.00023767311],"domain_scores_gemma":[0.99910223,0.00047520423,0.00006808946,0.00015839751,0.00012868884,0.00006741232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074390793,0.0002627459,0.0004493028,0.0004571341,0.00012119144,0.00021928853,0.000041927775,0.0000961328,0.000030436606],"category_scores_gemma":[0.000039182094,0.00021508188,0.00006794352,0.0008013977,0.00009150777,0.00033632966,0.00004862416,0.0001878787,5.404196e-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":[0.000109396125,0.000048583588,0.0126526,0.00043363444,0.002454698,0.000057444584,0.0050062793,0.87214893,0.03427129,0.0007172097,0.000063774656,0.07203614],"study_design_scores_gemma":[0.00019252018,0.00023115672,0.0022825112,0.00048835704,0.00039289272,0.000030686228,0.00026203148,0.9261348,0.06953291,0.000009959299,0.00015526489,0.00028688725],"about_ca_topic_score_codex":0.000061085695,"about_ca_topic_score_gemma":0.00024465262,"teacher_disagreement_score":0.071749255,"about_ca_system_score_codex":0.000044352484,"about_ca_system_score_gemma":0.000019360785,"threshold_uncertainty_score":0.8770785},"labels":[],"label_agreement":null},{"id":"W4402474571","doi":"10.1109/ccece59415.2024.10667087","title":"A Review of Machine Learning Approaches for Forecasting Aggregated Power Demand of Thermostatically Controlled Loads","year":2024,"lang":"en","type":"review","venue":"","topic":"Energy Load and Power Forecasting","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":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Demand forecasting; Power demand; On demand; Power (physics); Machine learning; Artificial intelligence; Engineering; Operations research; Power consumption; Multimedia","score_opus":0.06597821535662925,"score_gpt":0.2808538051568175,"score_spread":0.21487558980018828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402474571","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0000013661089,0.9780409,0.0065773106,0.0000054933034,0.00021484634,0.0014620539,0.00008383141,0.00019727781,0.0134169515],"genre_scores_gemma":[0.00012435729,0.9960328,0.0026094485,0.000013667181,0.00005441179,0.0002503521,0.0002161892,0.0001901295,0.0005086516],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9971814,0.00012237279,0.0018060545,0.00032137646,0.00019635305,0.00037244527],"domain_scores_gemma":[0.99804646,0.0010039574,0.00049921084,0.00026165834,0.00010628612,0.00008244612],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010400843,0.0006172755,0.00386247,0.00022556687,0.00003408812,0.000022558212,0.00026758778,0.0002620897,0.000118445416],"category_scores_gemma":[0.00064127694,0.00040351786,0.0012441708,0.0004665133,0.000052957483,0.000051211166,0.00006983327,0.0004905057,0.0000052198498],"study_design_candidate":"systematic_review","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":[0.00001303276,0.000015170325,2.3999357e-7,0.48378846,0.0010513742,0.000003410142,0.00005312933,0.0005923269,0.0000011568673,0.000726771,0.00029292714,0.513462],"study_design_scores_gemma":[0.00066400896,0.00012643551,1.0013442e-8,0.38709003,0.003143446,0.000040572268,0.000009170277,0.0988508,0.000008389019,0.0000570203,0.50958246,0.00042764086],"about_ca_topic_score_codex":0.000004623185,"about_ca_topic_score_gemma":0.000003337371,"teacher_disagreement_score":0.51303434,"about_ca_system_score_codex":0.0000388994,"about_ca_system_score_gemma":0.000090519214,"threshold_uncertainty_score":0.9998417},"labels":[],"label_agreement":null},{"id":"W4402475613","doi":"10.1109/ccece59415.2024.10667290","title":"Normalizing Flows-based Probabilistic Learning-Aided Distribution System State Estimation","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Saskatchewan","funders":"","keywords":"Computer science; Probabilistic logic; Estimation; Artificial intelligence; State (computer science); Machine learning; Algorithm; Engineering","score_opus":0.007168433234018637,"score_gpt":0.20138411647561105,"score_spread":0.1942156832415924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402475613","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":[0.15993261,0.00031236952,0.8283529,0.000022101887,0.00083791657,0.0001219812,0.000020472524,0.0037272158,0.006672381],"genre_scores_gemma":[0.99791044,0.0000028640072,0.001506237,0.0000033976064,0.000055457207,0.00002414841,0.00026739607,0.00003249709,0.00019757495],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925035,0.000023508874,0.00022093578,0.00014511017,0.00013875161,0.00022134448],"domain_scores_gemma":[0.9997284,0.00008758162,0.000013409392,0.000089449255,0.000025789881,0.00005536481],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020470048,0.00013550851,0.00010715674,0.000057726193,0.000077384924,0.00015757703,0.000057573623,0.000046610785,0.00003068409],"category_scores_gemma":[0.000041297564,0.00012359823,0.000048452297,0.000237414,0.000009696144,0.00017236086,0.000010259142,0.00016475368,0.000106360145],"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":[0.0000029820924,0.0000025504573,0.000029569072,0.00090485864,0.00001412397,0.000012883979,0.000072684765,0.9873796,0.00055090484,0.0032381653,0.0002231108,0.007568549],"study_design_scores_gemma":[0.00007819632,0.000020417705,0.00004907348,0.0004522364,0.000016346743,0.000009290436,0.000021889351,0.99256325,0.002647012,0.000056758086,0.0039337,0.00015182505],"about_ca_topic_score_codex":0.000034635596,"about_ca_topic_score_gemma":0.000019329376,"teacher_disagreement_score":0.8379778,"about_ca_system_score_codex":0.00019661992,"about_ca_system_score_gemma":0.000022466771,"threshold_uncertainty_score":0.50401896},"labels":[],"label_agreement":null},{"id":"W4402477145","doi":"10.11159/icert24.102","title":"Hourly Hydropower Production Forecasting with Machine Learning: A Case Study in Linköping, Sweden","year":2024,"lang":"en","type":"article","venue":"Proceedings of the World Congress on New Technologies","topic":"Energy Load and Power Forecasting","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":"Hydropower; Ping (video games); Link (geometry); Computer science; Production (economics); Artificial intelligence; Machine learning; Engineering; Electrical engineering; Computer network; Economics","score_opus":0.020831930614671142,"score_gpt":0.2393109833069367,"score_spread":0.21847905269226556,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402477145","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":[0.99151695,0.00093705946,0.000005058188,0.00060518953,0.00066480826,0.00041160683,0.000001379554,0.0023238452,0.0035340874],"genre_scores_gemma":[0.9964393,0.00003346189,0.0003821879,0.0000034224222,0.0000635245,0.000047072986,4.6667157e-7,0.00006093618,0.0029696445],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99881,0.000004903547,0.0002987696,0.00034947725,0.00022686819,0.00031000728],"domain_scores_gemma":[0.9996229,0.00006119568,0.000091479946,0.00015555033,0.000044054425,0.000024864083],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023451909,0.00026996818,0.000257818,0.0006060006,0.000103199025,0.00012647465,0.00037599044,0.000084836734,0.000003629978],"category_scores_gemma":[0.00020502254,0.00017952635,0.000053652642,0.0014337589,0.00010015779,0.00025646252,0.00015237377,0.0008823484,0.0000017539408],"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":[0.00036449803,0.00054607063,0.39401242,0.002413382,0.0010636221,0.002492477,0.011182585,0.1336107,0.014405279,0.0051279217,0.009478974,0.4253021],"study_design_scores_gemma":[0.0031263384,0.003032009,0.0008288447,0.019390285,0.00045201497,0.006520832,0.040255394,0.36011845,0.5224893,0.0038170032,0.037005797,0.0029637218],"about_ca_topic_score_codex":0.00016396839,"about_ca_topic_score_gemma":0.0013030043,"teacher_disagreement_score":0.508084,"about_ca_system_score_codex":0.00008126836,"about_ca_system_score_gemma":0.000018434486,"threshold_uncertainty_score":0.7320872},"labels":[],"label_agreement":null},{"id":"W4402567580","doi":"10.1016/j.enbuild.2024.114762","title":"Personalized federated learning for buildings energy consumption forecasting","year":2024,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":24,"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","funders":"","keywords":"Energy consumption; Consumption (sociology); Architectural engineering; Energy (signal processing); Computer science; Environmental science; Environmental economics; Engineering; Meteorology; Geography; Economics; Electrical engineering","score_opus":0.018124383790067533,"score_gpt":0.2246853110644772,"score_spread":0.20656092727440967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402567580","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":[0.4617464,0.019153273,0.50798476,0.00013128723,0.0016587068,0.000088330664,0.000013512652,0.0020546464,0.007169068],"genre_scores_gemma":[0.9919012,0.0009896159,0.0030818933,0.000095993884,0.00046715836,0.000059028054,0.0000654483,0.00009277001,0.0032468531],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998768,0.000021602571,0.00027033992,0.00036665378,0.00012741829,0.00044601317],"domain_scores_gemma":[0.9994631,0.00028027134,0.00003431804,0.00006243328,0.00004115515,0.00011875222],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021309062,0.00028707326,0.00024561217,0.00019078012,0.0003587484,0.00032444624,0.000080427715,0.00017319573,0.000094773866],"category_scores_gemma":[0.00004785268,0.00028415647,0.00011323962,0.0002133991,0.0000658026,0.00029036764,0.000036157184,0.00017186251,0.000002288382],"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":[0.000091836075,0.00001934526,0.00036582645,0.00085557223,0.00040950286,0.00006258936,0.0010596776,0.049608868,0.08053082,0.29522344,0.007927141,0.5638454],"study_design_scores_gemma":[0.0003203676,0.000053355645,0.000005473837,0.0003557391,0.00003851788,0.000087653825,0.000041281968,0.47980633,0.016398298,0.00078279444,0.50177014,0.0003400294],"about_ca_topic_score_codex":0.000054144468,"about_ca_topic_score_gemma":0.000021926466,"teacher_disagreement_score":0.56350535,"about_ca_system_score_codex":0.00005448853,"about_ca_system_score_gemma":0.000014189696,"threshold_uncertainty_score":0.9999611},"labels":[],"label_agreement":null},{"id":"W4402593217","doi":"10.1109/apcit62007.2024.10673558","title":"Time Series Forecasting For Energy Consumption Using XGBoost and LSTM","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Horizon College and Seminary","funders":"","keywords":"Series (stratigraphy); Computer science; Energy consumption; Time series; Energy (signal processing); Artificial intelligence; Consumption (sociology); Machine learning; Statistics; Mathematics; Engineering; Electrical engineering","score_opus":0.027658792462330994,"score_gpt":0.2240441778160953,"score_spread":0.19638538535376432,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402593217","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":[0.6763494,0.0067469613,0.28820577,0.000080150196,0.0017157701,0.00017508924,0.000057914065,0.002275716,0.024393208],"genre_scores_gemma":[0.98134667,0.00008830804,0.015528561,0.000036217367,0.00032381894,0.0000142165945,0.000027811506,0.00006608281,0.002568332],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950856,0.0000044121266,0.00012865367,0.00012803853,0.000044148175,0.0001861758],"domain_scores_gemma":[0.9997929,0.00009089244,0.0000075329176,0.000052791915,0.000012939179,0.00004294336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007106339,0.000113951835,0.00009841588,0.00007031442,0.00007217515,0.000095171614,0.000028547296,0.000054522217,0.00009120227],"category_scores_gemma":[0.000010251761,0.00010729665,0.00003278346,0.00006433056,0.000028752855,0.00024019898,0.000017498678,0.00004388359,0.000005717413],"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":[0.000073035466,0.000022573957,0.000789126,0.0042160493,0.0006306853,0.00009704403,0.0023308177,0.20693457,0.23822905,0.10156606,0.00843201,0.43667898],"study_design_scores_gemma":[0.000072837654,0.000018332934,0.0000062093695,0.00015963291,0.00001943727,0.00009197907,0.000013920609,0.966277,0.009593674,0.0003335416,0.023258492,0.00015494235],"about_ca_topic_score_codex":0.000008940651,"about_ca_topic_score_gemma":0.000016729982,"teacher_disagreement_score":0.75934243,"about_ca_system_score_codex":0.00002316725,"about_ca_system_score_gemma":0.0000074462614,"threshold_uncertainty_score":0.43754303},"labels":[],"label_agreement":null},{"id":"W4402742702","doi":"10.1109/access.2024.3465229","title":"Uncertainty Quantification in Load Forecasting for Smart Grids Using Non-Parametric Statistics","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Collège Shawinigan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Parametric statistics; Data mining; Statistics; Mathematics","score_opus":0.079766405715797,"score_gpt":0.32313394474327234,"score_spread":0.24336753902747532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402742702","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":[0.427426,0.00038632817,0.5685625,0.0000067202645,0.002539044,0.0001838276,0.00008315847,0.00014949946,0.0006629208],"genre_scores_gemma":[0.9923386,0.000029216237,0.007155971,0.000012682946,0.0002717106,0.000041856616,0.000045454806,0.000055849763,0.000048677033],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989159,0.000011933292,0.00034347796,0.00023960051,0.00015335246,0.00033572843],"domain_scores_gemma":[0.99928486,0.00041098232,0.000033201184,0.00015023448,0.0000700067,0.000050692837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033435313,0.00016509293,0.00017560113,0.0003148812,0.000064984815,0.00027774114,0.00020493215,0.00008706473,0.000008783639],"category_scores_gemma":[0.00012047738,0.00017097853,0.000046743644,0.00092138984,0.00001863611,0.00037668197,0.000020123578,0.00017335013,0.0000066734997],"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":[0.000008415094,0.000010079608,0.0010332705,0.0005494483,0.000025381896,0.00001570332,0.00018929921,0.96198577,0.0022199673,0.0004107004,0.00095456594,0.03259738],"study_design_scores_gemma":[0.00015846158,0.000014354853,0.00022241753,0.0002693458,0.000020872332,0.000007496364,0.000015973686,0.9903414,0.005971321,0.0008158732,0.0019520017,0.00021048485],"about_ca_topic_score_codex":0.0003636983,"about_ca_topic_score_gemma":0.0003316619,"teacher_disagreement_score":0.56491256,"about_ca_system_score_codex":0.00024016453,"about_ca_system_score_gemma":0.00007156567,"threshold_uncertainty_score":0.6972302},"labels":[],"label_agreement":null},{"id":"W4402765719","doi":"10.3390/en17184714","title":"Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables","year":2024,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"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","funders":"","keywords":"Energy consumption; Computer science; Machine learning; Energy (signal processing); Environmental science; Artificial intelligence; Meteorology; Engineering; Statistics; Mathematics; Geography; Electrical engineering","score_opus":0.006972929358970711,"score_gpt":0.21201936812361324,"score_spread":0.20504643876464254,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402765719","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":[0.057821576,0.004612637,0.93539995,0.000021562455,0.00010732366,0.00005268116,0.000020714757,0.0010564226,0.00090711366],"genre_scores_gemma":[0.88886166,0.0013235823,0.10866913,0.000010504429,0.00004792103,0.00008679892,0.00033316333,0.000029774586,0.00063749606],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994508,0.000021391877,0.00013910924,0.00018102789,0.00006511003,0.00014258618],"domain_scores_gemma":[0.99961114,0.00023796022,0.00002407789,0.000069604495,0.000024442668,0.000032778527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000085167856,0.0001341693,0.00016267084,0.00020608683,0.00014081904,0.00006219989,0.000036110334,0.00008015268,0.000060153663],"category_scores_gemma":[0.000023467019,0.00010769653,0.00004602029,0.0003023821,0.000037697788,0.00015783172,0.000021498987,0.00007765402,4.0869597e-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":[0.000015253553,0.0000027188917,0.0011602159,0.000035941703,0.00022391437,0.0000021287797,0.00008301919,0.9853087,0.0015556391,0.0048176534,0.000023256744,0.0067715254],"study_design_scores_gemma":[0.000096533215,0.000062301915,0.00009070189,0.000045414472,0.00027495102,0.000008906085,0.00001629507,0.981572,0.0073736645,0.0000883361,0.010213142,0.00015775149],"about_ca_topic_score_codex":0.000026779335,"about_ca_topic_score_gemma":0.00014079937,"teacher_disagreement_score":0.83104,"about_ca_system_score_codex":0.00003762732,"about_ca_system_score_gemma":0.0000056658255,"threshold_uncertainty_score":0.4391737},"labels":[],"label_agreement":null},{"id":"W4402769939","doi":"10.1115/es2024-124369","title":"Efficiency-Driven Supervised Learning Regressors in Power Modeling and Optimization of Vertical Axis Wind Turbines","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Simon Fraser University","funders":"","keywords":"Wind power; Computer science; Vertical axis; Horizontal axis; Power (physics); Marine engineering; Artificial intelligence; Engineering; Engineering drawing; Structural engineering; Electrical engineering","score_opus":0.010469470311893554,"score_gpt":0.21105905293372632,"score_spread":0.20058958262183277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402769939","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":[0.89788896,0.00087545504,0.0969679,0.000020819674,0.00013308862,0.000032982534,5.1080553e-7,0.00016521357,0.0039150454],"genre_scores_gemma":[0.9968901,0.00007564865,0.0029528204,0.0000033238725,0.000014527273,0.0000010284779,0.0000036477554,0.000020396577,0.000038506227],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946856,0.000012447622,0.00017599166,0.000121652534,0.000081263825,0.00014004974],"domain_scores_gemma":[0.9998438,0.00005362771,0.0000032201551,0.000051977706,0.00001477305,0.00003263514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008582058,0.00008745379,0.00011072297,0.00012749563,0.00001879139,0.00002838538,0.000040632913,0.00005859558,0.00006105804],"category_scores_gemma":[0.000037751717,0.00007687005,0.000022718054,0.00021091096,0.000017092432,0.00011853051,0.000020563522,0.0001329044,0.000001426267],"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":[0.0000030295569,0.000004389211,0.00059795676,0.0000652465,0.000007275153,0.000004646375,0.00082234945,0.995878,0.0015529316,0.0003852499,0.0000031539696,0.00067576615],"study_design_scores_gemma":[0.000109731875,0.000021793785,0.000033703007,0.00020441615,0.0000049805517,0.0000026726364,0.0001228122,0.9985547,0.00082089857,0.000013813217,0.000021561526,0.0000889115],"about_ca_topic_score_codex":0.000020173675,"about_ca_topic_score_gemma":0.0000049321134,"teacher_disagreement_score":0.09900112,"about_ca_system_score_codex":0.000014387533,"about_ca_system_score_gemma":0.00000746306,"threshold_uncertainty_score":0.31346697},"labels":[],"label_agreement":null},{"id":"W4402945037","doi":"10.1016/j.apenergy.2024.124527","title":"Unified carbon emissions and market prices forecasts of the power grid","year":2024,"lang":"en","type":"article","venue":"Applied Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Vedecká Grantová Agentúra MŠVVaŠ SR a SAV; Agentúra na Podporu Výskumu a Vývoja; European Commission; St. Thomas University","keywords":"Power grid; Grid; Environmental science; Carbon market; Environmental economics; Greenhouse gas; Carbon fibers; Economics; Power (physics); Natural resource economics; Business; Computer science; Mathematics","score_opus":0.005868094616551806,"score_gpt":0.18319571300264884,"score_spread":0.17732761838609704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402945037","genre_codex":"other","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":[0.3626733,0.0017653691,0.00025544077,0.00004740003,0.0012125685,0.00004653019,0.0000098840155,0.00026090187,0.6337286],"genre_scores_gemma":[0.99877894,0.000117575735,0.00014510789,0.000020994374,0.00009855571,0.00001306849,0.0000032848047,0.00003178431,0.00079070195],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.99944866,0.0000064369415,0.00014579829,0.00013252017,0.00010086543,0.00016572369],"domain_scores_gemma":[0.99968004,0.00007658826,0.000016160113,0.0001702384,0.0000072544553,0.000049729984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006331876,0.00012337448,0.00011309483,0.000052411826,0.00003669723,0.000022343836,0.00011019068,0.000071207854,0.000056147688],"category_scores_gemma":[0.0000046638784,0.00008597071,0.000037360664,0.00022406457,0.000037015558,0.000026201778,0.00005845622,0.00009750038,5.7728005e-7],"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":[0.000053301643,0.00006230563,0.00040924762,0.000851589,0.0005673557,0.000030424058,0.0028381476,0.056482088,0.16005926,0.62185866,0.030176343,0.12661129],"study_design_scores_gemma":[0.00043409973,0.000049730163,0.0015803124,0.0005405683,0.00009906284,0.000040759583,0.00025972375,0.13559471,0.17479739,0.006664692,0.6791974,0.0007415721],"about_ca_topic_score_codex":0.00004711685,"about_ca_topic_score_gemma":0.000033084714,"teacher_disagreement_score":0.649021,"about_ca_system_score_codex":0.000015411631,"about_ca_system_score_gemma":0.000013622493,"threshold_uncertainty_score":0.35057837},"labels":[],"label_agreement":null},{"id":"W4403052777","doi":"10.2139/ssrn.4974855","title":"Kolmogorov-Arnold Recurrent Network for Short Term Load Forecasting Across Diverse Consumers","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Term (time); Computer science; Economics; Physics","score_opus":0.02398838820127136,"score_gpt":0.2650017553918405,"score_spread":0.24101336719056915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403052777","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":[0.78967464,0.12612915,0.03928934,0.00018110186,0.03328114,0.0014007691,0.0004914976,0.0015249716,0.008027363],"genre_scores_gemma":[0.98371905,0.0100634685,0.000949176,0.00001869014,0.00387638,0.00009838138,0.00012177518,0.0002772155,0.0008758601],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9929073,0.000046801164,0.0008435483,0.00060212,0.0004545319,0.0051457374],"domain_scores_gemma":[0.9988855,0.00015995951,0.00018073234,0.00037048376,0.00016650929,0.00023678645],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0023466588,0.0007541931,0.0006848044,0.00012672131,0.00044822632,0.0004015798,0.0006733492,0.0005275669,0.000018008719],"category_scores_gemma":[0.0000824388,0.0007499332,0.00069459225,0.00021475111,0.00007512331,0.00011787041,0.00060053903,0.008090278,0.000018689025],"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":[0.00019077401,0.000056078556,0.002827698,0.0014961273,0.0038711336,0.000084659245,0.0018622399,0.27111447,0.00015404391,0.00831124,0.0047703763,0.7052612],"study_design_scores_gemma":[0.0029137237,0.0011167987,0.00019079287,0.010439852,0.002168347,0.0039581987,0.0037366697,0.48598176,0.0005693637,0.42028606,0.06265947,0.00597896],"about_ca_topic_score_codex":0.000039222607,"about_ca_topic_score_gemma":0.0025699828,"teacher_disagreement_score":0.6992822,"about_ca_system_score_codex":0.0032385937,"about_ca_system_score_gemma":0.0017285486,"threshold_uncertainty_score":0.99949515},"labels":[],"label_agreement":null},{"id":"W4403098533","doi":"10.70543/10.21203/rs.3.rs-1736061/v1","title":"10.70543/10.21203/rs.3.rs-1736061/v1","year":2000,"lang":"en","type":"paratext","venue":"Time to knit","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Electricity; Electricity generation; Dynamics (music); Environmental science; Engineering; Physics; Electrical engineering; Thermodynamics; Power (physics)","score_opus":0.007325382471396825,"score_gpt":0.1774478743952475,"score_spread":0.17012249192385068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403098533","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000043659176,0.0021528364,0.000008429485,0.000027968665,0.0002833167,0.00021775316,0.0002942513,0.00058056583,0.99639124],"genre_scores_gemma":[0.000040722196,0.0000258182,0.0001484919,0.000041659092,0.0017515247,0.000062010666,0.0008999115,0.00031182778,0.99671805],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976654,0.00004310092,0.000541575,0.00053606444,0.0003476844,0.00086615124],"domain_scores_gemma":[0.9987279,0.000101870326,0.00007340638,0.0006911431,0.000060879403,0.00034477262],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00013222605,0.00075650495,0.0007472363,0.00035518204,0.00011781607,0.0001469216,0.0006218983,0.00061645254,0.9948465],"category_scores_gemma":[0.000023054132,0.0008183976,0.00024506846,0.0004210187,0.000038044396,0.00012725456,0.00010722411,0.0006461583,0.99595886],"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":[0.000023484563,0.000014561506,1.028514e-8,0.000106679145,0.0001021018,0.00001779033,0.00002665453,0.050063096,0.000040513958,7.318632e-7,0.81873095,0.13087341],"study_design_scores_gemma":[0.00022030137,0.00009245005,6.9229884e-7,0.00035540177,0.000056651686,0.000023011904,7.2566536e-7,0.0025224877,0.00023474466,0.0000016373581,0.9955662,0.0009257147],"about_ca_topic_score_codex":0.000038558126,"about_ca_topic_score_gemma":5.8292846e-7,"teacher_disagreement_score":0.17683521,"about_ca_system_score_codex":0.00015399879,"about_ca_system_score_gemma":0.00006939425,"threshold_uncertainty_score":0.99942666},"labels":[],"label_agreement":null},{"id":"W4403098924","doi":"10.70543/10.21203/","title":"10.70543/10.21203/","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Electricity; Dynamics (music); Electricity generation; Economics; Environmental science; Econometrics; Engineering; Sociology; Physics; Power (physics); Electrical engineering; Thermodynamics","score_opus":0.004856035642511857,"score_gpt":0.1527229520429593,"score_spread":0.14786691640044744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403098924","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0032104726,0.000103131264,0.0000027383273,0.000024527038,0.000010323237,0.00003920495,0.000006384176,0.00045775058,0.9961455],"genre_scores_gemma":[0.0024369257,5.850734e-7,0.00015090899,0.00001952007,0.00016941092,0.0000073682995,0.000012176668,0.000036278918,0.9971668],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994525,0.000007020304,0.00011345164,0.00010865884,0.000083196035,0.00023522973],"domain_scores_gemma":[0.99968797,0.000022169914,0.000005472288,0.00017153863,0.000010851926,0.0001019833],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000050622515,0.000113333226,0.00010413976,0.000044443153,0.000035971323,0.000025890833,0.0001187901,0.0000479644,0.98976415],"category_scores_gemma":[0.000008342878,0.0001178547,0.000037525104,0.00014044266,0.000008130292,0.00006501874,0.000012883242,0.00006987401,0.9611797],"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":[0.00001012757,0.000007463501,1.6373637e-7,0.000008932432,0.00001572615,0.0000067495753,0.000026888909,0.03940655,0.00018464551,0.000002494061,0.21280211,0.74752814],"study_design_scores_gemma":[0.0000870539,0.000030919193,0.000008114245,0.00002007304,0.000005493962,0.0000057590546,2.5410802e-7,0.007604299,0.00038133064,0.0000026490986,0.9917006,0.00015346453],"about_ca_topic_score_codex":0.000006223638,"about_ca_topic_score_gemma":1.3872103e-7,"teacher_disagreement_score":0.7788985,"about_ca_system_score_codex":0.000020104788,"about_ca_system_score_gemma":0.0000045786123,"threshold_uncertainty_score":0.48059753},"labels":[],"label_agreement":null},{"id":"W4403123449","doi":"10.1109/access.2024.3474569","title":"Peak Shaving Impact on Load Forecasting: A Strategy for Mitigation","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":13,"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 New Brunswick","funders":"National Council for Forest Research and Development; Natural Sciences and Engineering Research Council of Canada","keywords":"Peaking power plant; Computer science; Load management; Engineering; Renewable energy; Electrical engineering; Distributed generation","score_opus":0.057741896213919675,"score_gpt":0.3236270000103705,"score_spread":0.2658851037964508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403123449","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":[0.952814,0.0006611931,0.02356509,0.000024584873,0.0019971272,0.00020842018,0.00005698926,0.0007839579,0.019888656],"genre_scores_gemma":[0.9988648,0.000011995084,0.00012433874,0.000022354692,0.0006768041,0.000049024766,0.000025506506,0.00006271621,0.00016244374],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991587,0.0000074757068,0.00018677725,0.0001986375,0.00014137464,0.0003070705],"domain_scores_gemma":[0.9995629,0.00017870916,0.000019012683,0.00012757209,0.00003889247,0.00007288483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001434657,0.00018320116,0.0001322042,0.00010423341,0.0000678738,0.0004134121,0.00018730038,0.0000779551,0.000053348314],"category_scores_gemma":[0.000026228998,0.0001569314,0.0001205549,0.00021946873,0.000011394824,0.00047631527,0.000012047249,0.00015261218,0.000017718776],"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":[0.00002156125,0.000012840264,0.00023023668,0.00043972794,0.00009793845,0.000029691564,0.0003465354,0.8878842,0.0053590164,0.0008271227,0.0071861683,0.09756496],"study_design_scores_gemma":[0.00020643689,0.000118476906,0.00046940075,0.00055110495,0.000029027447,0.000020656264,0.000016788716,0.97173274,0.021697387,0.0018313121,0.0030161475,0.0003105243],"about_ca_topic_score_codex":0.0000393424,"about_ca_topic_score_gemma":0.000039083334,"teacher_disagreement_score":0.09725443,"about_ca_system_score_codex":0.0001460867,"about_ca_system_score_gemma":0.000048232723,"threshold_uncertainty_score":0.63994765},"labels":[],"label_agreement":null},{"id":"W4403170651","doi":"10.70543/yevj4628/10.21203/rs.3.rs-1736061/v1","title":"10.70543/YEVJ4628/10.21203/rs.3.rs-1736061/v1","year":2000,"lang":"en","type":"paratext","venue":"Time to knit","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Electricity; Electricity generation; Dynamics (music); Environmental science; Engineering; Physics; Electrical engineering; Thermodynamics; Power (physics)","score_opus":0.007325382471396825,"score_gpt":0.1774478743952475,"score_spread":0.17012249192385068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403170651","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00005630913,0.0023722185,0.000011411103,0.000030699674,0.00033961263,0.00026306842,0.00035590015,0.0006658291,0.9959049],"genre_scores_gemma":[0.000060051974,0.000034471734,0.00018158536,0.000050547445,0.0019710287,0.00007286385,0.0010449598,0.00036126454,0.9962232],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9972361,0.000053284537,0.00064436527,0.00063484005,0.00041503657,0.0010163902],"domain_scores_gemma":[0.99849766,0.00012251742,0.00009166001,0.0008029322,0.00007486211,0.0004103792],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00016059195,0.0008919155,0.0008786105,0.00042316504,0.00014316253,0.00017736164,0.0007339885,0.0007211346,0.9940646],"category_scores_gemma":[0.000027999156,0.0009663807,0.0002935447,0.00049588404,0.000050289902,0.00015470416,0.00009965743,0.00075955107,0.9952184],"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":[0.000029665945,0.000018840108,1.7160572e-8,0.00012757015,0.00012631521,0.000022797276,0.000031645777,0.046312418,0.0000725459,0.0000010395544,0.8265698,0.12668735],"study_design_scores_gemma":[0.00026572097,0.000109254885,8.4968224e-7,0.00041399244,0.00007001099,0.000029939889,9.665166e-7,0.002334123,0.0003918335,0.000002163063,0.99529094,0.0010901823],"about_ca_topic_score_codex":0.000045813653,"about_ca_topic_score_gemma":7.559829e-7,"teacher_disagreement_score":0.16872117,"about_ca_system_score_codex":0.00018555587,"about_ca_system_score_gemma":0.00008663178,"threshold_uncertainty_score":0.99927866},"labels":[],"label_agreement":null},{"id":"W4403170652","doi":"10.70543/yevj4628/10.21203/","title":"10.70543/YEVJ4628/10.21203/","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Electricity; Dynamics (music); Electricity generation; Computer science; Engineering; Physics; Power (physics); Electrical engineering","score_opus":0.004856035642511857,"score_gpt":0.1527229520429593,"score_spread":0.14786691640044744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403170652","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0038335165,0.00012822427,0.0000040503924,0.000027735034,0.000014263991,0.000051684106,0.000009004708,0.000541167,0.99539036],"genre_scores_gemma":[0.003423987,9.30226e-7,0.00019070585,0.000025196725,0.00021053746,0.000009561814,0.000016660879,0.000046301542,0.9960761],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99930155,0.000009561989,0.00014623317,0.00013949827,0.0001071809,0.00029596398],"domain_scores_gemma":[0.99960476,0.000028983142,0.000007736241,0.00021401048,0.000014621162,0.00012986906],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00006567164,0.00014599654,0.0001340678,0.000058171856,0.000046979603,0.000034125256,0.00015203425,0.000062573214,0.9878398],"category_scores_gemma":[0.00001089983,0.00015228313,0.0000492272,0.00017607413,0.000011770706,0.00008354885,0.000012755722,0.0000908655,0.95511186],"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":[0.000014619771,0.000011040166,2.956534e-7,0.000012783137,0.000022908007,0.000009995765,0.00003562945,0.038925536,0.000368407,0.0000038819235,0.21182255,0.7487723],"study_design_scores_gemma":[0.000112243564,0.000039035298,0.000009780562,0.00002642677,0.0000076184947,0.000008179627,3.7308624e-7,0.006787916,0.0006765423,0.0000036419765,0.9921312,0.00019707781],"about_ca_topic_score_codex":0.000008232568,"about_ca_topic_score_gemma":2.0218492e-7,"teacher_disagreement_score":0.7803086,"about_ca_system_score_codex":0.000026732825,"about_ca_system_score_gemma":0.0000064746964,"threshold_uncertainty_score":0.6209926},"labels":[],"label_agreement":null},{"id":"W4403320899","doi":"10.1139/cjp-2023-0283","title":"New method to model the wind speed distribution: method of decile","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Physics","topic":"Energy Load and Power Forecasting","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":"Physics; Decile; Wind speed; Distribution (mathematics); Statistical physics; Applied mathematics; Meteorology; Statistics; Mathematical analysis","score_opus":0.02301388799217059,"score_gpt":0.2697346865063013,"score_spread":0.2467207985141307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403320899","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":[0.0026989619,0.00095174694,0.99383956,0.00027623467,0.00068964914,0.000027405153,0.00007580745,0.000010229003,0.0014304124],"genre_scores_gemma":[0.7748912,0.000015853877,0.22240806,0.00012683509,0.0017978675,2.6281072e-7,0.000010394839,0.00005669386,0.0006928702],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994313,0.000023355653,0.00020722914,0.00005528683,0.00010639854,0.00017642409],"domain_scores_gemma":[0.9993573,0.00010036002,0.000030205807,0.00010063179,0.00006409699,0.00034740038],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028847944,0.00008791428,0.00015011446,0.00004792352,0.000039076203,0.000048013208,0.00017636242,0.000033473047,0.000032275875],"category_scores_gemma":[0.000024600338,0.000068014975,0.0001090961,0.0003097419,0.000010151501,0.000099919285,0.0000064101114,0.00021098687,0.000004598428],"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":[0.000001202454,7.710983e-7,0.0000072186003,0.00001745965,0.00005802662,0.00001548489,0.0007578145,0.8835959,0.0012872429,0.012849485,0.018527742,0.08288169],"study_design_scores_gemma":[0.00012363362,0.000044352495,0.00004134181,0.00029437323,0.00009675396,0.00010572123,0.00010193244,0.87398064,0.021208135,0.018673852,0.08514809,0.00018118985],"about_ca_topic_score_codex":0.0006259173,"about_ca_topic_score_gemma":0.0006332667,"teacher_disagreement_score":0.7721922,"about_ca_system_score_codex":0.0001020269,"about_ca_system_score_gemma":0.00054907304,"threshold_uncertainty_score":0.277357},"labels":[],"label_agreement":null},{"id":"W4403426018","doi":"10.1016/j.energy.2024.133475","title":"Strategies for designing machine learning models in renewable energy with insufficient data","year":2024,"lang":"en","type":"article","venue":"Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"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":"Canada First Research Excellence Fund; University of Alberta","keywords":"Renewable energy; Computer science; Machine learning; Energy (signal processing); Artificial intelligence; Engineering; Electrical engineering","score_opus":0.03717648067477077,"score_gpt":0.22532723701700902,"score_spread":0.18815075634223824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403426018","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":[0.0028599743,0.007872634,0.96173656,0.000016036474,0.0003028435,0.000029996181,0.00002420865,0.0005654711,0.02659229],"genre_scores_gemma":[0.99155504,0.00024941552,0.0064636744,0.000022918332,0.000147392,0.000040846906,0.00029618226,0.000085656015,0.0011388593],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998947,0.000023364266,0.0002074453,0.0003301198,0.00012745439,0.0003645991],"domain_scores_gemma":[0.9995209,0.00012313027,0.000017028739,0.00026824907,0.000015538639,0.000055159493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018399824,0.00019735681,0.0001757868,0.00019407061,0.000068488815,0.00016932288,0.00025611155,0.00007113233,0.000014105285],"category_scores_gemma":[0.0000072448724,0.0001733156,0.000026883637,0.00034412547,0.000018170385,0.0005147547,0.000069715636,0.00013386451,6.7700495e-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":[0.000011493377,0.000009916991,0.000009410533,0.000060834227,0.000033328313,0.000027699178,0.00026532498,0.95434844,0.0012838724,0.03645159,0.0005284937,0.006969587],"study_design_scores_gemma":[0.00016654408,0.00006157775,6.49261e-7,0.00020856134,0.00001034853,0.000010647174,0.00016522619,0.891958,0.002488101,0.0020033007,0.10271278,0.00021423784],"about_ca_topic_score_codex":0.0027322352,"about_ca_topic_score_gemma":0.005788836,"teacher_disagreement_score":0.9886951,"about_ca_system_score_codex":0.000050667368,"about_ca_system_score_gemma":0.000071499926,"threshold_uncertainty_score":0.70676047},"labels":[],"label_agreement":null},{"id":"W4403525331","doi":"10.1016/j.enbuild.2024.114894","title":"Household energy consumption forecasting based on adaptive signal decomposition enhanced iTransformer network","year":2024,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Energy consumption; Decomposition; Consumption (sociology); Energy (signal processing); Environmental science; SIGNAL (programming language); Econometrics; Environmental economics; Computer science; Economics; Statistics; Engineering; Mathematics; Electrical engineering","score_opus":0.015986925186029958,"score_gpt":0.2091649781616634,"score_spread":0.19317805297563345,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403525331","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":[0.14819524,0.0025794175,0.8349247,0.000036849444,0.0009337647,0.000043513344,0.000014643746,0.00089241617,0.012379437],"genre_scores_gemma":[0.9966746,0.00027985455,0.002058656,0.00022645232,0.0005422833,0.000028355544,0.0000436072,0.00006343719,0.00008276425],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892676,0.000024114504,0.00022476139,0.00029550196,0.00014962947,0.0003792309],"domain_scores_gemma":[0.9995726,0.0002037511,0.000023917453,0.0000839557,0.000015449996,0.00010035488],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012608114,0.00025717527,0.00018118606,0.00014112571,0.00017264229,0.00010023883,0.00006809497,0.00014533242,0.000075832075],"category_scores_gemma":[0.0000026683083,0.0002481194,0.00008613978,0.00021292288,0.000041851083,0.00021297322,0.000012166869,0.00016003275,0.0000020401617],"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":[0.00006726999,0.000012426372,0.000032878317,0.00006146055,0.00006423498,0.00002666375,0.000084326406,0.85123485,0.013550762,0.030925883,0.0008906291,0.1030486],"study_design_scores_gemma":[0.00026921392,0.00014822056,0.000030917567,0.00086777133,0.000038433413,0.000021525233,0.000006299201,0.90601796,0.071419664,0.0011854795,0.019615935,0.00037860972],"about_ca_topic_score_codex":0.000036241647,"about_ca_topic_score_gemma":0.000043804397,"teacher_disagreement_score":0.84847933,"about_ca_system_score_codex":0.00005598258,"about_ca_system_score_gemma":0.000013962745,"threshold_uncertainty_score":0.9999971},"labels":[],"label_agreement":null},{"id":"W4403722361","doi":"10.1109/tpwrd.2024.3486010","title":"Leveraging Hypernetworks and Learnable Kernels for Consumer Energy Forecasting Across Diverse Consumer Types","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Energy Load and Power Forecasting","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":"Western University","funders":"Environment Canada","keywords":"Computer science; Energy (signal processing); Artificial intelligence; Mathematics; Statistics","score_opus":0.02560714486715991,"score_gpt":0.22922536073261218,"score_spread":0.20361821586545226,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403722361","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":[0.34073725,0.006334285,0.6454784,0.000027475211,0.004056042,0.0001625595,0.00015353528,0.0008378931,0.002212534],"genre_scores_gemma":[0.99632746,0.0008947268,0.00070488837,0.0000984674,0.00008602206,0.000047914804,0.0000080098935,0.00009529908,0.0017372329],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99871683,0.000019670975,0.00025746215,0.00035571257,0.00013496767,0.00051533437],"domain_scores_gemma":[0.99925923,0.00037876802,0.000021382102,0.00016354145,0.00005332749,0.00012375116],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00015069684,0.0002852483,0.0002330813,0.00013169169,0.00031183023,0.00018853809,0.00009714299,0.00016019205,0.00012907041],"category_scores_gemma":[0.000004225043,0.00029197714,0.00014516167,0.00019889364,0.000085492415,0.0003132073,0.0000036549488,0.0003109231,0.000026803618],"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":[0.00011725114,0.000056213114,0.00009592968,0.00035050738,0.0008872146,0.00011526469,0.003146248,0.71303535,0.0024389063,0.0002815068,0.004604828,0.27487078],"study_design_scores_gemma":[0.00084540376,0.00011771206,0.000015639502,0.00062849605,0.000201796,0.00016524339,0.0006405207,0.8446363,0.014330192,0.0001553852,0.13744149,0.0008217925],"about_ca_topic_score_codex":0.00008893862,"about_ca_topic_score_gemma":0.00005138926,"teacher_disagreement_score":0.6555902,"about_ca_system_score_codex":0.00006988195,"about_ca_system_score_gemma":0.000025184792,"threshold_uncertainty_score":0.9999532},"labels":[],"label_agreement":null},{"id":"W4403836307","doi":"10.18280/jesa.570517","title":"Time-Series Forecasting Models for Smart Meters Data: An Empirical Comparison and Analysis","year":2024,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Series (stratigraphy); Time series; Computer science; Econometrics; Data mining; Machine learning; Mathematics; Geology","score_opus":0.07060795337198847,"score_gpt":0.30002748690302566,"score_spread":0.2294195335310372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403836307","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":[0.46084997,0.008811643,0.5255758,0.000082455,0.00097086356,0.00020199206,0.00024583,0.0012632833,0.00199816],"genre_scores_gemma":[0.9196465,0.00015029583,0.07913083,0.000024051385,0.0004149244,0.000010314533,0.00015732009,0.0001183636,0.00034738798],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982515,0.00009855573,0.0006479376,0.00033485025,0.00024566875,0.0004214737],"domain_scores_gemma":[0.99900967,0.00027798026,0.00008869117,0.0003225482,0.00006561948,0.00023549459],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007727898,0.00027974555,0.00052915246,0.0004145895,0.00029511473,0.0008365865,0.00033569167,0.00008825027,0.00003171937],"category_scores_gemma":[0.00008361652,0.00024003212,0.0001562475,0.000572363,0.00008482958,0.0017958271,0.0001031173,0.00029247726,0.000007454935],"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":[0.000029608496,0.00003452942,0.0028425457,0.00086578756,0.0028589189,0.00015223329,0.003687911,0.6036484,0.0005439595,0.0003744389,0.007075796,0.37788585],"study_design_scores_gemma":[0.00013224677,0.00011969687,0.0023102541,0.00025308356,0.00068384653,0.00051046006,0.00011342067,0.9919281,0.00006337571,0.0011958865,0.0024109953,0.0002786586],"about_ca_topic_score_codex":0.000008439596,"about_ca_topic_score_gemma":0.00004240763,"teacher_disagreement_score":0.45879656,"about_ca_system_score_codex":0.00008509353,"about_ca_system_score_gemma":0.000035757253,"threshold_uncertainty_score":0.9788226},"labels":[],"label_agreement":null},{"id":"W4403937950","doi":"10.1109/ict-pep63827.2024.10733457","title":"Integrating Climate Change and Power System Resilience in the Canadian Building Sector Using Monte Carlo Model of Heating and Cooling Demand","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Monte Carlo method; Resilience (materials science); Environmental science; Psychological resilience; Climate change; Power (physics); Power demand; Computer science; Materials science; Geology; Physics","score_opus":0.03005586615738728,"score_gpt":0.2318050231374376,"score_spread":0.2017491569800503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403937950","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":[0.9916128,0.0030935346,0.0027456714,0.000011953215,0.00012524585,0.00011278594,0.000011566433,0.00007921596,0.0022072466],"genre_scores_gemma":[0.995803,0.000039892497,0.0040600193,0.000019626345,0.000043407035,0.000007665685,3.7917124e-7,0.000023824823,0.0000022117792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917215,0.000025242136,0.00023850141,0.00016378911,0.00009448274,0.00030581007],"domain_scores_gemma":[0.9997034,0.00010867872,0.000018917775,0.000085290616,0.00001651959,0.00006719528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051093154,0.00013538917,0.00015914255,0.00018217445,0.00014137234,0.00013712527,0.00007006997,0.0000617103,8.117466e-7],"category_scores_gemma":[0.000020940957,0.00010215884,0.000019201923,0.00021705861,0.000026682328,0.0002146619,0.000028076834,0.00018799279,8.682113e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000043282953,0.000001988484,0.008215771,0.0015398813,0.000023289596,0.000047969905,0.024930267,0.9276156,0.012298274,0.022903282,0.0000032728929,0.00241611],"study_design_scores_gemma":[0.000047929138,0.000007940441,0.00016382347,0.001736907,0.000009522411,0.000045853936,0.0025990407,0.99482626,0.00043277006,0.000014321903,0.000004367272,0.00011126065],"about_ca_topic_score_codex":0.04736483,"about_ca_topic_score_gemma":0.11505486,"teacher_disagreement_score":0.06769002,"about_ca_system_score_codex":0.00010071445,"about_ca_system_score_gemma":0.00002582111,"threshold_uncertainty_score":0.95897883},"labels":[],"label_agreement":null},{"id":"W4403993488","doi":"10.1016/j.compchemeng.2024.108898","title":"Physics-informed neural networks for state reconstruction of hydrogen energy transportation systems","year":2024,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"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","keywords":"Artificial neural network; Energy (signal processing); State (computer science); Hydrogen; Statistical physics; Hydrogen fuel; Computer science; Engineering; Physics; Artificial intelligence; Algorithm; Quantum mechanics","score_opus":0.006454293136211651,"score_gpt":0.17885185327538725,"score_spread":0.1723975601391756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403993488","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":[0.20297946,0.0009474911,0.7935671,0.0000023973569,0.0018881843,0.00006323654,0.000017079912,0.00050303905,0.00003199193],"genre_scores_gemma":[0.9965282,0.000028937273,0.0028607678,0.0000030228202,0.00040312723,0.000025284548,0.000096191725,0.00004859432,0.0000058552114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992629,0.000001986013,0.00029670767,0.00013696363,0.000073307245,0.00022807959],"domain_scores_gemma":[0.9996952,0.00011657367,0.00002197768,0.00008253614,0.000020892421,0.00006283407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000034440694,0.00016123443,0.00019406415,0.00005810086,0.000012501388,0.000033048407,0.000082243896,0.00006977866,8.8067753e-7],"category_scores_gemma":[0.0000033068527,0.00017471428,0.00010860023,0.00017274692,0.000012687705,0.00015875239,0.000005174965,0.000105726125,2.3513805e-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":[0.0000035114776,0.0000019660552,0.0000050265244,0.0004881761,0.00006736504,0.0000017785702,0.00010120883,0.9396828,0.018918272,0.0006782074,0.00006899625,0.03998271],"study_design_scores_gemma":[0.00012487137,0.0000114128825,0.000002084449,0.00030425302,0.000017717683,0.000010550179,0.0000033121087,0.9471738,0.05136188,0.000026956697,0.00079951994,0.00016367398],"about_ca_topic_score_codex":0.0000067067754,"about_ca_topic_score_gemma":4.81269e-7,"teacher_disagreement_score":0.79354876,"about_ca_system_score_codex":0.00005818977,"about_ca_system_score_gemma":0.000008220877,"threshold_uncertainty_score":0.71246415},"labels":[],"label_agreement":null},{"id":"W4404039318","doi":"10.2139/ssrn.4999656","title":"Greenhouse-Gas-Emission-Aware Portfolio Optimization with Deep Reinforcement Learning &lt;br&gt;","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Reinforcement learning; Portfolio; Greenhouse gas; Reinforcement; Portfolio optimization; Computer science; Artificial intelligence; Economics; Psychology; Geology; Financial economics; Social psychology","score_opus":0.005352320843288303,"score_gpt":0.20150351674691838,"score_spread":0.1961511959036301,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404039318","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":[0.036999617,0.019761156,0.9163862,0.00018332986,0.0022796409,0.00040599552,0.0000037760753,0.0016385365,0.022341777],"genre_scores_gemma":[0.9746331,0.018798348,0.0010303272,0.000018028511,0.0010882237,0.000026925913,0.00010348527,0.0002994359,0.0040021217],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9959786,0.00005262467,0.00064369343,0.00041997593,0.00056274195,0.0023423769],"domain_scores_gemma":[0.99906147,0.000036906804,0.00025108687,0.00030748712,0.00012950662,0.00021354339],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00081201334,0.00059932563,0.0004526046,0.00039827602,0.0002656084,0.00027277463,0.0004146674,0.0003867801,0.00017153495],"category_scores_gemma":[0.000033349315,0.00051713013,0.00022562257,0.00029333655,0.000029642873,0.00013855769,0.00027959692,0.008109052,0.000026257629],"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":[0.00002921656,0.000010027698,0.000061181505,0.00017245294,0.00054908614,0.000047357775,0.00027174354,0.98531204,0.00005575601,0.0016035807,0.00018588203,0.011701677],"study_design_scores_gemma":[0.00045433594,0.0003190278,0.0000029130272,0.001281242,0.00024242711,0.0010070575,0.0003176678,0.9830523,0.00018642943,0.009743576,0.0026195194,0.0007734773],"about_ca_topic_score_codex":0.000033580145,"about_ca_topic_score_gemma":0.00020023208,"teacher_disagreement_score":0.9376335,"about_ca_system_score_codex":0.0016837891,"about_ca_system_score_gemma":0.001427919,"threshold_uncertainty_score":0.999728},"labels":[],"label_agreement":null},{"id":"W4404047938","doi":"10.54254/2753-8818/51/2024ch0196","title":"Forecast of consumption of natural gas in U.S. based on time series analysis and ARIMA model","year":2024,"lang":"en","type":"article","venue":"Theoretical and Natural Science","topic":"Energy Load and Power Forecasting","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":"Autoregressive integrated moving average; Series (stratigraphy); Time series; Box–Jenkins; Gas consumption; Econometrics; Consumption (sociology); Natural gas; Statistics; Environmental science; Mathematics; Economics; Engineering; Geology; Philosophy; Environmental economics","score_opus":0.004145763291062661,"score_gpt":0.21133851060015313,"score_spread":0.20719274730909046,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404047938","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":[0.9976561,0.00066410075,0.00054195983,0.000050156537,0.00005575661,0.000026472611,0.000007917187,0.000027024033,0.0009705295],"genre_scores_gemma":[0.9989938,0.000044357483,0.0009273359,0.000008745836,0.0000056820886,7.2034067e-7,0.000002469914,0.0000032791972,0.000013580586],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945974,0.000009135221,0.00012224358,0.00013302485,0.00014211639,0.00013376276],"domain_scores_gemma":[0.99976796,0.00010746681,0.000009968573,0.00005801393,0.000017569284,0.000039003447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025025505,0.00007306748,0.0001376686,0.00022330713,0.00002411544,0.000021626023,0.000065174616,0.00002838887,0.00001887557],"category_scores_gemma":[0.00003701728,0.000051002782,0.000028444221,0.0005884141,0.00092424627,0.00013325954,0.000024225776,0.00011737526,5.602395e-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":[0.00013409375,0.000018836,0.0027536268,0.0003015783,0.000039570325,0.0000071465943,0.0005007424,0.07530738,0.13894194,0.75263995,0.000004811259,0.029350335],"study_design_scores_gemma":[0.00005851313,0.000028823762,0.0025534106,0.00008796875,0.000023405988,0.000002348901,0.000003176367,0.9630611,0.027876494,0.006240618,0.0000015855375,0.000062575586],"about_ca_topic_score_codex":0.0000044043195,"about_ca_topic_score_gemma":0.0000057113048,"teacher_disagreement_score":0.8877537,"about_ca_system_score_codex":0.000011943267,"about_ca_system_score_gemma":0.000013146244,"threshold_uncertainty_score":0.34054238},"labels":[],"label_agreement":null},{"id":"W4404068822","doi":"10.1088/1742-6596/2875/1/012011","title":"Applying triple collocation for verifying wind resource measurements and reanalysis data","year":2024,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Energy Load and Power Forecasting","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":"McGill University","funders":"","keywords":"Collocation (remote sensing); Environmental science; Computer science; Resource (disambiguation); Meteorology; Remote sensing; Geology; Geography","score_opus":0.0940691791277136,"score_gpt":0.27722527065376235,"score_spread":0.18315609152604873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404068822","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":[0.15828748,0.009667728,0.82206553,0.00042650834,0.0023808554,0.0006105487,0.00018019175,0.00027185373,0.0061092926],"genre_scores_gemma":[0.99637526,0.000151386,0.0029698734,0.000008659751,0.00035741302,0.0000049075443,0.0000296855,0.000020258836,0.00008257688],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932754,0.000014462553,0.0002551887,0.00010543669,0.0001800195,0.00011733235],"domain_scores_gemma":[0.9995673,0.000057991034,0.00007034226,0.00015182025,0.00010770004,0.00004485812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036547362,0.00010098455,0.00018448567,0.000064905915,0.00007682649,0.00020079539,0.00016660569,0.000032225613,0.0000042147994],"category_scores_gemma":[0.000049680642,0.0000924762,0.000042881078,0.00016836294,0.000024607465,0.00075620314,0.000034298384,0.00012669666,5.1988457e-7],"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":[0.00008608599,0.000022038888,0.00042202935,0.0009806177,0.00091698323,0.000009744617,0.0027244636,0.03158812,0.07444022,0.0051351204,0.002960497,0.88071406],"study_design_scores_gemma":[0.0013683562,0.00036225197,0.00028352227,0.003110872,0.0011859622,0.00011945758,0.0035462857,0.43791777,0.2587196,0.011759335,0.2806784,0.0009481875],"about_ca_topic_score_codex":0.0000028635047,"about_ca_topic_score_gemma":0.000008020908,"teacher_disagreement_score":0.87976587,"about_ca_system_score_codex":0.000027630003,"about_ca_system_score_gemma":0.000055244287,"threshold_uncertainty_score":0.377107},"labels":[],"label_agreement":null},{"id":"W4404099359","doi":"10.46959/jeess.1556704","title":"ANALYSIS OF SELECTED COUNTRIES ACCORDING TO THEIR ENERGY CONSUMPTION BY CLUSTER ANALYSIS K-MEANS METHOD","year":2024,"lang":"en","type":"article","venue":"Journal of Empirical Economics and Social Sciences","topic":"Energy Load and Power Forecasting","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":"Cluster (spacecraft); Consumption (sociology); Energy consumption; Statistics; Computer science; Mathematics; Engineering; Sociology; Social science; Electrical engineering","score_opus":0.029418426296908596,"score_gpt":0.31322632169544834,"score_spread":0.28380789539853973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404099359","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":[0.9466116,0.0008769634,0.051324107,0.00064624683,0.00017263452,0.000010623977,0.000030879826,0.000011661059,0.0003152802],"genre_scores_gemma":[0.99812686,0.00063479494,0.00097573135,0.00015409727,0.000087599285,4.7728935e-7,0.0000027567617,0.0000045437773,0.00001310741],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992591,0.000037812686,0.00037625217,0.00011001822,0.000078908844,0.00013788455],"domain_scores_gemma":[0.99946433,0.00030920297,0.00009129581,0.000026643267,0.000052282823,0.00005625415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006827186,0.000082903825,0.0003863598,0.00055718195,0.00011176803,0.0001608337,0.00011235943,0.00005729215,0.000025599425],"category_scores_gemma":[0.000016861877,0.000064308086,0.00019765271,0.0012709025,0.00007330396,0.00018715691,0.00001994042,0.000073044874,2.2966931e-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":[0.000024934337,0.000020634727,0.072296076,0.00003934613,0.0071880165,0.0000020232817,0.0064387913,0.884268,0.00070950674,0.0046033813,0.0017025307,0.022706792],"study_design_scores_gemma":[0.000070139344,0.00006179194,0.0033081383,0.000013358631,0.0011909456,0.0000027518663,0.0005366334,0.97686476,0.00040485154,0.00022808085,0.017175756,0.00014280088],"about_ca_topic_score_codex":0.000031070453,"about_ca_topic_score_gemma":0.0001817013,"teacher_disagreement_score":0.092596784,"about_ca_system_score_codex":0.0000530704,"about_ca_system_score_gemma":0.000043786844,"threshold_uncertainty_score":0.26224077},"labels":[],"label_agreement":null},{"id":"W4404102683","doi":"10.1109/sege62220.2024.10739563","title":"Enhancing Wind Power Forecasting Accuracy in Canada Using a Solar Data-Enhanced Hybrid Machine Learning Model: Integrating ANN, LSTM, and SVR","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Wind power; Machine learning; Artificial intelligence; Support vector machine; Data modeling; Power (physics); Artificial neural network; Engineering","score_opus":0.025049364182273708,"score_gpt":0.2327546681013189,"score_spread":0.20770530391904518,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404102683","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":[0.89674056,0.0027050038,0.09671084,0.000014739955,0.00047313375,0.00011629223,0.000051224262,0.00034779415,0.0028404146],"genre_scores_gemma":[0.98918146,0.00006177397,0.010353042,0.000039927705,0.000095239535,0.0000031743477,0.00009545671,0.000108620996,0.000061298226],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99796784,0.000040014074,0.0005754844,0.00053063815,0.00023482674,0.0006512167],"domain_scores_gemma":[0.9990765,0.00041349538,0.000054074764,0.00028764224,0.000031738702,0.00013658058],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00049286033,0.00036907275,0.00035475005,0.00020403315,0.0001684205,0.00024057426,0.00025628542,0.00006197898,0.000065672604],"category_scores_gemma":[0.00035701648,0.0003536865,0.000035237903,0.00036367448,0.000019316216,0.000962307,0.00025835278,0.00085787795,0.000001526763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000005492699,0.0000029588139,0.0007195831,0.00022862054,0.00004943477,0.00011159044,0.00112581,0.94701916,0.025351139,0.000042109776,0.00006545213,0.025278658],"study_design_scores_gemma":[0.00015209297,0.000011982677,0.000013121902,0.0008641175,0.000018663011,0.0000946248,0.00055262545,0.98614097,0.011294899,0.00007195988,0.0003767732,0.00040818006],"about_ca_topic_score_codex":0.2720501,"about_ca_topic_score_gemma":0.69485104,"teacher_disagreement_score":0.42280096,"about_ca_system_score_codex":0.00050088315,"about_ca_system_score_gemma":0.0004769904,"threshold_uncertainty_score":0.9998915},"labels":[],"label_agreement":null},{"id":"W4404102801","doi":"10.1109/sege62220.2024.10739606","title":"Addressing Explainability in Load Forecasting Using Time Series Machine Learning Models","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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; Series (stratigraphy); Time series; Machine learning; Artificial intelligence","score_opus":0.07363738974450154,"score_gpt":0.2533671889262247,"score_spread":0.17972979918172313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404102801","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":[0.87519807,0.0048249867,0.044173226,0.000033598728,0.00054560666,0.0001398307,0.0000074053373,0.0019927684,0.07308452],"genre_scores_gemma":[0.98946327,0.000024377297,0.009480251,0.0000074730688,0.000120480276,0.000007409242,0.00001046277,0.000071673385,0.0008145861],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874496,0.000041080075,0.00033280705,0.000270204,0.00017776563,0.0004331959],"domain_scores_gemma":[0.9996402,0.0001219719,0.00001663906,0.00012693836,0.000027557126,0.00006665077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046515485,0.00022793835,0.00022476196,0.00015987353,0.000111737885,0.00017516069,0.0000961826,0.00009728118,0.00017283995],"category_scores_gemma":[0.00006750975,0.0002205069,0.00006909391,0.00040608426,0.000031406744,0.00091795065,0.00006551485,0.0004363977,0.000015430714],"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":[0.0000058766204,0.0000050153053,0.00049587863,0.00026161282,0.000015890553,0.00006761335,0.0011723336,0.98138857,0.0032918698,0.00045837424,0.000020369813,0.012816595],"study_design_scores_gemma":[0.00009095461,0.000013726774,0.000009055935,0.0005405256,0.000008165762,0.00006766801,0.0001047644,0.9944341,0.0020744263,0.0011137313,0.0012861104,0.00025676694],"about_ca_topic_score_codex":0.00019965364,"about_ca_topic_score_gemma":0.00013980636,"teacher_disagreement_score":0.114265226,"about_ca_system_score_codex":0.00028393517,"about_ca_system_score_gemma":0.000048113765,"threshold_uncertainty_score":0.89920104},"labels":[],"label_agreement":null},{"id":"W4404102951","doi":"10.1109/sege62220.2024.10739610","title":"Solar Energy Forecasting Using Statistical, Machine Learning, and Deep Learning Approaches","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Energie NB Power (Canada); University of New Brunswick","funders":"","keywords":"Computer science; Statistical learning; Artificial intelligence; Machine learning; Solar energy; Energy (signal processing); Engineering; Statistics; Electrical engineering; Mathematics","score_opus":0.03403148350715721,"score_gpt":0.21985448243015435,"score_spread":0.18582299892299714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404102951","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":[0.03930033,0.014295275,0.9061674,0.000012170569,0.00040523877,0.000045507117,0.0000055208607,0.0015574038,0.03821114],"genre_scores_gemma":[0.9779783,0.00030149808,0.020468533,0.000009193669,0.00018842389,0.0000050417116,0.000040199095,0.00009634681,0.00091247365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989248,0.00004465373,0.00023310738,0.00027456856,0.00013176748,0.00039108156],"domain_scores_gemma":[0.99954623,0.00024269107,0.000017727976,0.000063175685,0.000011381424,0.000118766126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002054512,0.00023216209,0.0001939194,0.00013724301,0.00020541024,0.00023422347,0.000059428137,0.00009356973,0.00018968228],"category_scores_gemma":[0.00006599054,0.00021427422,0.000041394906,0.00019571185,0.000046492685,0.00018572503,0.00006439465,0.00044354415,0.0000062147114],"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":[0.0000048061906,0.000008532291,0.001988605,0.00040981636,0.00014077731,0.0001072849,0.00074105314,0.5505024,0.0013347347,0.017328441,0.000052065207,0.42738146],"study_design_scores_gemma":[0.00007690628,0.00003454791,0.000009841779,0.000095064264,0.000030303592,0.00011663638,0.00013414367,0.95303,0.00058732653,0.00024708037,0.04537949,0.0002586756],"about_ca_topic_score_codex":0.00012285022,"about_ca_topic_score_gemma":0.000071014416,"teacher_disagreement_score":0.93867797,"about_ca_system_score_codex":0.00003819823,"about_ca_system_score_gemma":0.0000104192195,"threshold_uncertainty_score":0.8737849},"labels":[],"label_agreement":null},{"id":"W4404281803","doi":"10.1007/s43621-024-00615-6","title":"Optimizing deep neural network architectures for renewable energy forecasting","year":2024,"lang":"en","type":"article","venue":"Discover Sustainability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":51,"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 Moncton","funders":"","keywords":"Renewable energy; Artificial neural network; Computer science; Artificial intelligence; Deep learning; Engineering; Electrical engineering","score_opus":0.010027851501131838,"score_gpt":0.22469871062498564,"score_spread":0.2146708591238538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404281803","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":[0.28528002,0.009352271,0.69070154,0.00018827645,0.00261541,0.000478728,0.000037099704,0.0016016965,0.009744926],"genre_scores_gemma":[0.9953992,0.0000059844415,0.0030320266,0.000049865816,0.0008429159,0.00013832105,0.00003765118,0.000086740576,0.00040730106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828506,0.00003739088,0.00032510434,0.00040467753,0.00013852546,0.0008092407],"domain_scores_gemma":[0.9990879,0.0004127748,0.000023109069,0.0002966816,0.00006990704,0.00010958427],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031473697,0.0002893926,0.00025232637,0.00008625824,0.00019914345,0.00023478745,0.00018844838,0.00010192833,0.000022508637],"category_scores_gemma":[0.00015623022,0.00026546253,0.00022806911,0.0003582868,0.00006070263,0.00015550268,0.00008085041,0.0001907435,4.7381727e-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":[0.000023800505,0.00000791705,0.00039877673,0.00065528066,0.000050194652,0.000020709604,0.0008199294,0.96079147,0.000018552395,0.002026283,0.00074559817,0.034441493],"study_design_scores_gemma":[0.00013071578,0.000045481003,0.00005730291,0.00006542796,0.00003044392,0.000013345487,0.00029637179,0.9510018,0.00043282632,0.028276369,0.019335369,0.0003145364],"about_ca_topic_score_codex":0.0003560362,"about_ca_topic_score_gemma":0.0006233954,"teacher_disagreement_score":0.7101192,"about_ca_system_score_codex":0.00026973983,"about_ca_system_score_gemma":0.00007083405,"threshold_uncertainty_score":0.99997973},"labels":[],"label_agreement":null},{"id":"W4404319805","doi":"10.5267/j.ccl.2024.11.007","title":"In vitro evaluation of antibacterial and antibiofilm activity of new bis-quaternary ammonium compounds based on natural products","year":2024,"lang":"en","type":"review","venue":"Current Chemistry Letters","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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":"National Research Foundation; National Research Foundation of Ukraine","keywords":"Chemistry; Biochemical engineering; Management science","score_opus":0.04373201024624057,"score_gpt":0.30676632087709027,"score_spread":0.2630343106308497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404319805","genre_codex":"review","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30170593,0.6955711,0.0000041428784,0.000032908687,0.0022752287,0.00021959387,0.00009622261,0.000042108215,0.000052736028],"genre_scores_gemma":[0.621304,0.37709984,0.00007008536,0.0000035057715,0.0009248619,0.000011850591,0.00049725285,0.000082226914,0.0000063760954],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986308,0.0000509361,0.00043172954,0.0003514202,0.0003282209,0.00020687595],"domain_scores_gemma":[0.9994369,0.00004601105,0.00018049986,0.00026333117,0.000029795443,0.000043461823],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00024850926,0.0003838951,0.0008265444,0.000131144,0.000010628829,0.000024934507,0.00015675949,0.00013362247,0.0000064469587],"category_scores_gemma":[0.00003470515,0.00034734377,0.0001535842,0.0002724407,0.000050473915,0.0000773437,0.000040315677,0.0005244275,0.0000010069874],"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":[0.0000337284,0.000038555754,0.0000044987723,0.062313784,0.000069341586,0.0000049679907,0.000026534912,0.00026728562,0.6575011,1.9441373e-7,0.00047303713,0.27926695],"study_design_scores_gemma":[0.00090125325,0.000007776064,0.000013700105,0.03798689,0.0009893362,0.000021014259,0.0000015563326,0.01897781,0.8937741,0.0000013810676,0.046483044,0.0008421297],"about_ca_topic_score_codex":0.000008226787,"about_ca_topic_score_gemma":2.4102212e-7,"teacher_disagreement_score":0.31959808,"about_ca_system_score_codex":0.00014225263,"about_ca_system_score_gemma":0.00015784893,"threshold_uncertainty_score":0.99989784},"labels":[],"label_agreement":null},{"id":"W4404414732","doi":"10.1029/2024jh000243","title":"Using Explainable AI and Transfer Learning to Understand and Predict the Maintenance of Atlantic Blocking With Limited Observational Data","year":2024,"lang":"en","type":"article","venue":"Journal of Geophysical Research Machine Learning and Computation","topic":"Energy Load and Power Forecasting","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":"Army Research Office; Climate Extremes; Schmidt Family Foundation; National Science Foundation","keywords":"Blocking (statistics); Observational study; Transfer of learning; Observational learning; Computer science; Artificial intelligence; Psychology; Mathematics education; Mathematics; Computer network; Statistics","score_opus":0.09927608756865201,"score_gpt":0.3485107465980402,"score_spread":0.24923465902938818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404414732","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":[0.89118457,0.00095892325,0.10735646,0.00036885758,0.000025956952,0.0000510524,0.0000019155516,0.000017243186,0.00003499512],"genre_scores_gemma":[0.99862534,0.00019695585,0.001018363,0.000008421386,0.0000996822,4.4442828e-7,0.000007808268,0.000016209771,0.00002679698],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990333,0.00013414735,0.00018905285,0.0001260649,0.00034621707,0.00017125568],"domain_scores_gemma":[0.999013,0.000709503,0.00002433402,0.000043368087,0.00013547877,0.000074324285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008950715,0.000083873914,0.00014877772,0.00014337084,0.000198532,0.00015465065,0.000074292744,0.000025566529,0.00000127656],"category_scores_gemma":[0.00015962654,0.00005527601,0.000014937604,0.00032735226,0.00008031491,0.00024239608,0.00005967672,0.00076873216,1.2609921e-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":[0.00015963186,0.000016756396,0.012184008,0.00045900344,0.00014363394,0.000041555253,0.0033596104,0.94233805,0.0037049658,0.0011500817,0.000044291097,0.036398396],"study_design_scores_gemma":[0.00023469301,0.00036658187,0.0057479492,0.0007664105,0.0000250745,0.000089326335,0.00059209776,0.9911919,0.000041086263,0.00043417668,0.00044869326,0.000061985345],"about_ca_topic_score_codex":0.00010374936,"about_ca_topic_score_gemma":0.000013695904,"teacher_disagreement_score":0.107440725,"about_ca_system_score_codex":0.000022396385,"about_ca_system_score_gemma":0.00004109723,"threshold_uncertainty_score":0.33398014},"labels":[],"label_agreement":null},{"id":"W4404418597","doi":"10.1016/b978-0-443-28951-4.00007-1","title":"Smart grid stability prediction using binary manta ray foraging-based machine learning","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Foraging; Computer science; Binary number; Stability (learning theory); Artificial intelligence; Machine learning; Mathematics; Biology; Ecology; Arithmetic","score_opus":0.018403670626900008,"score_gpt":0.21106609325476283,"score_spread":0.19266242262786282,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404418597","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.002447689,0.005199501,0.00030001125,0.0000111113095,0.0035339699,0.0003600762,0.00026333448,0.0014228323,0.98646146],"genre_scores_gemma":[0.10284131,0.00016269353,0.001229938,0.00007748025,0.0021134762,0.000050993563,0.0008256215,0.000830206,0.8918683],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99795854,0.000028831051,0.000624373,0.0005679439,0.00035205073,0.00046827534],"domain_scores_gemma":[0.9991339,0.00008258068,0.00011309611,0.00046057822,0.00005500332,0.0001548703],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004036174,0.0006730288,0.0005634265,0.00031946492,0.00019431865,0.00010474508,0.00020667029,0.00041636667,0.0002979039],"category_scores_gemma":[0.000012470897,0.0006885038,0.0003386123,0.000038295413,0.000087399385,0.00008451062,0.00011235483,0.0014746276,0.00007386071],"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":[0.00007050479,0.000021019681,0.00054137467,0.004652701,0.00075479015,0.00039824852,0.0008163848,0.22817504,0.002254806,0.0011623842,0.00016979735,0.76098293],"study_design_scores_gemma":[0.00020652561,0.000063387495,0.000007009267,0.0017203175,0.00023210014,0.000023327906,0.0000064982123,0.29621795,0.00035355322,0.00056103576,0.7000364,0.0005718949],"about_ca_topic_score_codex":0.0000041039743,"about_ca_topic_score_gemma":0.00003466714,"teacher_disagreement_score":0.760411,"about_ca_system_score_codex":0.00033747221,"about_ca_system_score_gemma":0.000073402836,"threshold_uncertainty_score":0.9995566},"labels":[],"label_agreement":null},{"id":"W4404421344","doi":"10.52783/anvi.v27.1356","title":"Computational Forecasting of Power Prices Using Artificial Neural Networks","year":2024,"lang":"en","type":"article","venue":"Advances in Nonlinear Variational Inequalities","topic":"Energy Load and Power Forecasting","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":"Artificial neural network; Computer science; Power (physics); Artificial intelligence; Econometrics; Economics","score_opus":0.03452078640335731,"score_gpt":0.28427549127574386,"score_spread":0.24975470487238655,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404421344","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":[0.34131697,0.011710524,0.6402559,0.000038055736,0.002919032,0.00012469108,0.00016642416,0.0002728316,0.0031955587],"genre_scores_gemma":[0.9560382,0.000041710606,0.043287627,0.000018944123,0.00046653202,0.0000057318207,0.000103902225,0.000028465221,0.000008919516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988648,0.00003295673,0.0005204759,0.00015141956,0.00021983958,0.00021048736],"domain_scores_gemma":[0.99906117,0.00073399657,0.000055150682,0.00006109468,0.00006119858,0.000027386972],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003075038,0.00014441067,0.00017942912,0.00018394597,0.00004430864,0.00005013173,0.00008800937,0.000062818435,0.00005864503],"category_scores_gemma":[0.00006757552,0.00014735135,0.000056357578,0.00035893955,0.00004441894,0.00061151787,0.000025637291,0.00017488319,0.000001216236],"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":[0.0000066610087,0.000010999005,0.00030629395,0.00011424145,0.000018447428,0.0000059364506,0.00042500754,0.88511,0.000030407904,0.110919006,0.00000274421,0.003050249],"study_design_scores_gemma":[0.00006623179,0.000017135211,0.00008967219,0.00017614228,0.0000067911997,0.000010936086,0.00013361621,0.98526126,0.0000650847,0.013356178,0.00066654524,0.000150427],"about_ca_topic_score_codex":0.00002026066,"about_ca_topic_score_gemma":0.000026626878,"teacher_disagreement_score":0.6147212,"about_ca_system_score_codex":0.000051037383,"about_ca_system_score_gemma":0.000035245623,"threshold_uncertainty_score":0.6008814},"labels":[],"label_agreement":null},{"id":"W4404510334","doi":"10.2316/j.2024.203-0534","title":"NEURAL NETWORK BASED DATA QUALITY MONITORING AND REAL-TIME ANALYSIS METHOD FOR ENERGY STORAGE POWER PLANTS, 1-15.","year":2024,"lang":"en","type":"article","venue":"International Journal of Power and Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Power quality; Power network; Energy storage; Artificial neural network; Computer science; Data quality; Real-time computing; Quality (philosophy); Power (physics); Reliability engineering; Electric power system; Environmental science; Electrical engineering; Engineering; Artificial intelligence; Operations management; Voltage","score_opus":0.02479320614310928,"score_gpt":0.30219422294300263,"score_spread":0.2774010167998934,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404510334","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":[0.1387841,0.035318498,0.79840225,0.00026314537,0.022354612,0.000064805834,0.0007358194,0.00027869543,0.0037980927],"genre_scores_gemma":[0.995211,0.0004443245,0.0024852403,0.000019199188,0.0013861229,0.00000410222,0.000088187015,0.000036792215,0.00032505268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842256,0.00010740422,0.00063975545,0.00023951379,0.0003645314,0.00022625162],"domain_scores_gemma":[0.9988261,0.00055699877,0.00014755719,0.00019594224,0.00013239894,0.0001410392],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009450652,0.00020206292,0.00040939322,0.00034510807,0.000055420016,0.00031616466,0.00035832374,0.000097108066,0.000019204464],"category_scores_gemma":[0.00002979093,0.00017419572,0.00014350266,0.00017409348,0.000020082884,0.00037429229,0.00007343655,0.00012102265,3.134367e-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":[0.00013533272,0.000022880937,0.0013983981,0.00008296299,0.0043949885,0.00018713802,0.00032850367,0.9722797,0.003705467,0.0038462284,0.0047967904,0.008821609],"study_design_scores_gemma":[0.0003682332,0.00005992219,0.0006631682,0.00031677674,0.00020330209,0.00017353181,0.00009039411,0.9282631,0.00020050572,0.00007220611,0.06934245,0.00024637292],"about_ca_topic_score_codex":0.00051510066,"about_ca_topic_score_gemma":0.000025076777,"teacher_disagreement_score":0.8564269,"about_ca_system_score_codex":0.00006091233,"about_ca_system_score_gemma":0.000030296018,"threshold_uncertainty_score":0.71034956},"labels":[],"label_agreement":null},{"id":"W4404562801","doi":"10.1109/mpe.2024.3408111","title":"Toward Artificial-Intelligence-Empowered Smarter Power Grid: Forecasting, Dispatch, and Control","year":2024,"lang":"en","type":"article","venue":"IEEE Power and Energy Magazine","topic":"Energy Load and Power Forecasting","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":"Simon Fraser University","funders":"","keywords":"Power grid; Computer science; Control (management); Grid; Economic dispatch; Artificial intelligence; Power (physics); Operations research; Industrial engineering; Electric power system; Engineering","score_opus":0.019173341396337622,"score_gpt":0.21637728945708187,"score_spread":0.19720394806074426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404562801","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":[0.40399972,0.034459587,0.34503028,0.0016655064,0.028920751,0.00035996758,0.00041520078,0.003686605,0.18146238],"genre_scores_gemma":[0.997245,0.00055315736,0.00029009173,0.00019405172,0.0005201083,0.000027102635,0.000022845401,0.00010328066,0.0010443741],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99820906,0.000029572126,0.00046684558,0.00048813256,0.00020628344,0.00060010684],"domain_scores_gemma":[0.9992642,0.000159385,0.000030525927,0.0002357347,0.000041989155,0.000268207],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00019328589,0.0004383491,0.0003892312,0.00021285233,0.000102858765,0.00028507254,0.0001292687,0.00018415917,0.00028303693],"category_scores_gemma":[0.000021000069,0.0003883256,0.000114183065,0.0002558792,0.00012296106,0.00028270576,0.000039792107,0.00026587013,0.000049461993],"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":[0.00056410907,0.0003634058,0.0019365873,0.0018289911,0.0026142537,0.0030094173,0.012014025,0.05245712,0.06851964,0.09724758,0.0972216,0.6622233],"study_design_scores_gemma":[0.0006720282,0.00057879667,0.0006134892,0.00076850003,0.00022904947,0.0007558578,0.00028104012,0.4220697,0.013210791,0.008810381,0.5498517,0.00215863],"about_ca_topic_score_codex":0.00004576798,"about_ca_topic_score_gemma":0.0000910946,"teacher_disagreement_score":0.66006464,"about_ca_system_score_codex":0.00003158333,"about_ca_system_score_gemma":0.000019181549,"threshold_uncertainty_score":0.9998569},"labels":[],"label_agreement":null},{"id":"W4404612815","doi":"10.1007/s10489-024-05856-6","title":"Unsupervised anomaly detection and imputation in noisy time series data for enhancing load forecasting","year":2024,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"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; Concordia University","keywords":"Computer science; Anomaly detection; Imputation (statistics); Time series; Data mining; Series (stratigraphy); Anomaly (physics); Artificial intelligence; Machine learning; Missing data","score_opus":0.022859108288929706,"score_gpt":0.23456198353049107,"score_spread":0.21170287524156137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404612815","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":[0.25775534,0.0013603724,0.7353471,0.000015569523,0.00040073163,0.00032956214,0.000030599247,0.00048138615,0.004279339],"genre_scores_gemma":[0.99414796,0.00006881524,0.005478783,0.000008376419,0.00012405237,0.000047891404,0.000045433502,0.000037351525,0.00004136644],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991299,0.000006770144,0.00025821992,0.0002968273,0.00008224989,0.00022601834],"domain_scores_gemma":[0.9995371,0.00021927411,0.000015853677,0.00017218625,0.000019039304,0.00003655279],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037645415,0.00014217055,0.0001258204,0.00009784857,0.00006670614,0.000101695674,0.0001323728,0.0000724865,0.000009544897],"category_scores_gemma":[0.000060791677,0.00015071087,0.000016077764,0.00026483028,0.00002230747,0.0003494969,0.000058810263,0.00014875864,0.000018101951],"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":[0.000034874378,0.0000065662352,0.000028169678,0.00064099056,0.000034978733,0.000007962958,0.0021529759,0.07179295,0.17403986,0.0012769834,0.00003136245,0.7499523],"study_design_scores_gemma":[0.000041487787,0.000024683659,0.00002241137,0.00014425207,0.0000109111,0.000016403621,0.00021118156,0.8743782,0.122302465,0.0013015795,0.0013730206,0.00017338374],"about_ca_topic_score_codex":0.000031642143,"about_ca_topic_score_gemma":0.0006022188,"teacher_disagreement_score":0.80258524,"about_ca_system_score_codex":0.00006435119,"about_ca_system_score_gemma":0.000021809528,"threshold_uncertainty_score":0.61458105},"labels":[],"label_agreement":null},{"id":"W4404708495","doi":"10.1109/access.2024.3505258","title":"Novel Time-Varying Risk-Averse and Risk-Seeker Frameworks for Uncertain Wind Energy Generation in Electric Power Systems","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"Ontario Ministry of Research and Innovation","keywords":"Wind power; Electric power system; Computer science; Electricity generation; Energy (signal processing); Risk analysis (engineering); Power (physics); Electrical engineering; Business; Engineering; Mathematics","score_opus":0.016555980563991414,"score_gpt":0.2485755920887707,"score_spread":0.2320196115247793,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404708495","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":[0.719843,0.0046491134,0.27217627,0.000010528602,0.0019636133,0.00017992154,0.000048031718,0.00026093764,0.0008686371],"genre_scores_gemma":[0.99823004,0.00050654385,0.0004902269,0.000025832802,0.00044490764,0.000046209145,0.00002835532,0.00006657862,0.00016132128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998943,0.000032134325,0.00027643694,0.0002956823,0.00012132735,0.0003314198],"domain_scores_gemma":[0.99947315,0.00023193618,0.000049568418,0.00014987428,0.000030770534,0.0000647077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026496142,0.00020312678,0.00019991244,0.00026192082,0.00009418592,0.00038829818,0.00014425351,0.0002858682,0.000016508146],"category_scores_gemma":[0.00004893359,0.0001999613,0.000050881426,0.0004417347,0.0000111502295,0.00045195865,0.000021078375,0.00040526027,0.0000056731496],"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":[0.0000102338645,0.000012448958,0.0008274273,0.0000986812,0.00008582479,0.000010715673,0.0002482086,0.9794312,0.011710843,0.00031226093,0.001587334,0.0056647975],"study_design_scores_gemma":[0.00026645628,0.000024123672,0.00015348395,0.00015016782,0.000037673162,0.000011570251,0.000008581144,0.98999536,0.0038803269,0.000105614185,0.005110612,0.00025604287],"about_ca_topic_score_codex":0.0007677748,"about_ca_topic_score_gemma":0.00010948802,"teacher_disagreement_score":0.27838707,"about_ca_system_score_codex":0.000093603594,"about_ca_system_score_gemma":0.000023687779,"threshold_uncertainty_score":0.81541854},"labels":[],"label_agreement":null},{"id":"W4404711507","doi":"10.3390/jrfm17120538","title":"Optimizing Energy Storage Profits: A New Metric for Evaluating Price Forecasting Models","year":2024,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"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":"European Commission","keywords":"Metric (unit); Econometrics; Computer science; Energy (signal processing); Economics; Mathematical optimization; Operations research; Mathematics; Operations management; Statistics","score_opus":0.02713094410754203,"score_gpt":0.23965885810126644,"score_spread":0.21252791399372442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404711507","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":[0.017584026,0.017889366,0.9611908,0.000013315126,0.0010626497,0.00010700525,0.0000058173314,0.00006819081,0.0020788123],"genre_scores_gemma":[0.87947416,0.0029886083,0.116348445,0.00002455572,0.0008468636,0.000010704447,0.0000021999308,0.00005215749,0.00025231438],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989261,0.000017011855,0.00044097056,0.00014139399,0.00021191959,0.00026259283],"domain_scores_gemma":[0.99951,0.00014940475,0.0001182729,0.000070198876,0.000054783268,0.00009734474],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000683323,0.00016318692,0.00023530144,0.00044898476,0.00011881518,0.00013874174,0.00011519115,0.000056853238,0.000003890907],"category_scores_gemma":[0.000088186935,0.000144788,0.000125664,0.0004500523,0.000008533372,0.0003586349,0.000049015045,0.00018942544,3.4864934e-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":[0.00002036581,0.0000054959532,0.000005174076,0.00021805771,0.000047553036,0.000034645647,0.00064445025,0.42201158,0.000018608982,0.0069723153,0.00061480026,0.569407],"study_design_scores_gemma":[0.0005261517,0.00018450418,0.00003003764,0.0006649187,0.00019621103,0.000042668995,0.00012584559,0.942749,0.0000946071,0.009065475,0.046120428,0.00020012636],"about_ca_topic_score_codex":0.000012789856,"about_ca_topic_score_gemma":0.0000047978433,"teacher_disagreement_score":0.86189014,"about_ca_system_score_codex":0.0000779164,"about_ca_system_score_gemma":0.000032583393,"threshold_uncertainty_score":0.59042835},"labels":[],"label_agreement":null},{"id":"W4405385150","doi":"10.21203/rs.3.rs-5632095/v1","title":"AI and Kernel Influences: The Impact of Kernel Optimization on Energy Consumption Forecasting","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Kernel (algebra); Energy consumption; Computer science; Econometrics; Artificial intelligence; Machine learning; Mathematical optimization; Economics; Mathematics; Engineering; Combinatorics","score_opus":0.06875782726566106,"score_gpt":0.35868468314171104,"score_spread":0.28992685587605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405385150","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":[0.96938705,0.010500772,0.0066263326,0.000153833,0.0006367794,0.00059289637,0.00024165763,0.00041377585,0.011446895],"genre_scores_gemma":[0.9975244,0.0017073618,0.00019084384,0.0000087574645,0.00023594272,0.00007545745,0.0000867612,0.00007195358,0.0000985364],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981342,0.00016622645,0.0003519699,0.00035411134,0.00054494553,0.00044853165],"domain_scores_gemma":[0.99873316,0.00048082785,0.00006175066,0.00038964595,0.00022422377,0.00011041774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000824312,0.00028026733,0.0002755577,0.0004083784,0.00013762226,0.00022925819,0.00027023625,0.00029049953,0.00007814273],"category_scores_gemma":[0.00019750788,0.00019552199,0.00015510203,0.00032765415,0.00017898266,0.00007178073,0.0005287445,0.0013628834,0.000007709747],"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":[0.000021178292,0.000012741965,0.001190428,0.0012085488,0.00012779378,0.000010422237,0.0005636422,0.9853279,0.00012046244,0.0009731142,0.000608565,0.009835225],"study_design_scores_gemma":[0.00011817343,0.00012310775,0.0011117173,0.0029849876,0.000016232843,0.0000101640635,0.00006286999,0.99249965,0.00046965544,0.002313565,0.000095815005,0.00019408106],"about_ca_topic_score_codex":0.000998048,"about_ca_topic_score_gemma":0.00004731871,"teacher_disagreement_score":0.028137328,"about_ca_system_score_codex":0.0002350962,"about_ca_system_score_gemma":0.00014202719,"threshold_uncertainty_score":0.79731554},"labels":[],"label_agreement":null},{"id":"W4405385169","doi":"10.21203/rs.3.rs-5632243/v1","title":"Seasonal Dynamics in Energy Forecasting: A Deep Learning Approach for Student Residences","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Autoregressive model; Computer science; Energy consumption; Robustness (evolution); Artificial neural network; Feed forward; Artificial intelligence; Machine learning; Econometrics; Engineering; Mathematics","score_opus":0.058348447028721745,"score_gpt":0.3399595372361274,"score_spread":0.2816110902074056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405385169","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":[0.57526153,0.09349701,0.108938016,0.00047025504,0.0040868036,0.0041440395,0.0005010992,0.0030928084,0.21000843],"genre_scores_gemma":[0.9911969,0.00083630695,0.004053874,0.0000036746926,0.00072107854,0.0011366257,0.000595774,0.00018470045,0.0012711111],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.996226,0.00025173105,0.0005072258,0.0007930253,0.0010648767,0.0011571549],"domain_scores_gemma":[0.9984085,0.0007396666,0.00004941658,0.00036533197,0.00023620934,0.00020086512],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002085992,0.00042243465,0.00048551217,0.00086939137,0.00019280898,0.00045146153,0.0006868744,0.00050667446,0.000021473359],"category_scores_gemma":[0.00035863745,0.00042570027,0.00024300456,0.00070265844,0.00009811446,0.00007047662,0.0013570557,0.003100442,0.000005985561],"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":[0.0000309016,0.00004899219,0.0026915795,0.004621014,0.000118161835,0.00008349838,0.0011888952,0.9445605,0.0000117092095,0.010906504,0.0003294548,0.035408754],"study_design_scores_gemma":[0.00019667017,0.000104678475,0.00016971704,0.0019253345,0.000015222188,0.000010301592,0.0019816286,0.98735315,0.000052326228,0.0057171015,0.00206316,0.00041074245],"about_ca_topic_score_codex":0.00034734333,"about_ca_topic_score_gemma":0.0017997563,"teacher_disagreement_score":0.4159353,"about_ca_system_score_codex":0.0013186751,"about_ca_system_score_gemma":0.00022993653,"threshold_uncertainty_score":0.99981946},"labels":[],"label_agreement":null},{"id":"W4405484540","doi":"10.3934/mbe.2025002","title":"Probabilistic prediction intervals of short-term wind speed using selected features and time shift dependent machine learning models","year":2024,"lang":"en","type":"article","venue":"Mathematical Biosciences & Engineering","topic":"Energy Load and Power Forecasting","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":"Wind Energy Institute of Canada","funders":"","keywords":"Wind speed; Probabilistic logic; Prediction interval; Daytime; Computer science; Parametric statistics; Wind power; Probabilistic forecasting; Meteorology; Machine learning; Statistics; Artificial intelligence; Mathematics; Engineering","score_opus":0.017545855552738073,"score_gpt":0.22064198329759982,"score_spread":0.20309612774486174,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405484540","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":[0.94772,0.0012035449,0.04960512,0.0000074648415,0.0002793008,0.00013898035,0.000016771488,0.0005537796,0.00047505877],"genre_scores_gemma":[0.99665236,0.000020841184,0.003189008,0.0000010108266,0.000055242996,0.0000017019752,0.000006070503,0.00003380323,0.000039976716],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988594,0.000014601826,0.0003345052,0.00024070386,0.0002545321,0.00029626902],"domain_scores_gemma":[0.9996147,0.00015317745,0.000017909006,0.00009711184,0.000021469206,0.00009559129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003091716,0.00020779882,0.00025480354,0.0001929144,0.000052271134,0.00013674748,0.0001326218,0.0000841454,0.000030545918],"category_scores_gemma":[0.00010045663,0.00017502908,0.000047747242,0.00042827646,0.000060784067,0.0002907793,0.000060607315,0.0002518154,0.0000028127047],"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":[0.0000014121728,0.000011948823,0.000107351836,0.0007286592,0.00003415958,0.0000063677844,0.0008391179,0.84261096,0.15306477,0.0022183137,0.0000027288495,0.00037421548],"study_design_scores_gemma":[0.00004317228,0.000043934466,0.00020125792,0.0007461598,0.000038662558,0.000075619304,0.000015329868,0.9900303,0.0078593,0.00077323185,0.0000064014043,0.00016662579],"about_ca_topic_score_codex":0.0000042009106,"about_ca_topic_score_gemma":9.691395e-7,"teacher_disagreement_score":0.14741935,"about_ca_system_score_codex":0.000048631242,"about_ca_system_score_gemma":0.000014293406,"threshold_uncertainty_score":0.71374786},"labels":[],"label_agreement":null},{"id":"W4405626796","doi":"10.1175/aies-d-24-0127.1","title":"Self-Attentive Transformer for Fast and Accurate Postprocessing of Temperature and Wind Speed Forecasts","year":2025,"lang":"en","type":"preprint","venue":"Artificial Intelligence for the Earth Systems","topic":"Energy Load and Power Forecasting","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":"United Nations University Institute for Water, Environment, and Health","funders":"","keywords":"Transformer; Wind speed; Computer science; Speedup; Environmental science; Meteorology; Electrical engineering; Engineering; Physics; Parallel computing; Voltage","score_opus":0.034042481254649384,"score_gpt":0.26974205268936546,"score_spread":0.2356995714347161,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405626796","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":[0.66120666,0.021456093,0.29585704,0.00026126573,0.010753973,0.0066988906,0.0022077435,0.00040748832,0.0011508273],"genre_scores_gemma":[0.9980792,0.00036441066,0.0008063703,0.000010249963,0.00036169885,0.000054171374,0.00004574518,0.00004002892,0.00023814301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998497,0.000026185024,0.0006555976,0.00035050657,0.00013103268,0.00033969013],"domain_scores_gemma":[0.9988683,0.0004890293,0.00015588863,0.00021415995,0.00020929289,0.00006336672],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00043831885,0.00034588654,0.000490255,0.00012308534,0.00023287591,0.00024027615,0.00021183686,0.0002939042,0.0000017528848],"category_scores_gemma":[0.000047194815,0.0002666788,0.00014301811,0.00014119296,0.00009156484,0.000117147974,0.00004878986,0.0003073461,5.8810645e-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":[0.00036652133,0.0000620662,0.00013396828,0.019053891,0.001328766,0.000002491675,0.020375641,0.7609205,0.0125433775,0.0091388635,0.000107718704,0.17596617],"study_design_scores_gemma":[0.00009747333,0.000116422765,0.000020397187,0.0020431685,0.000247099,0.000011965863,0.002846083,0.91103435,0.079626806,0.0018756556,0.0016487592,0.00043182712],"about_ca_topic_score_codex":0.000060014347,"about_ca_topic_score_gemma":0.00010003575,"teacher_disagreement_score":0.3368725,"about_ca_system_score_codex":0.000016008418,"about_ca_system_score_gemma":0.000056077984,"threshold_uncertainty_score":0.99997854},"labels":[],"label_agreement":null},{"id":"W4405695404","doi":"10.1016/j.jclepro.2024.144555","title":"Dual-channel encoded bidirectional LSTM for multi-building short-term load forecasting","year":2024,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Government of British Columbia; Ministry of Health; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Term (time); Computer science; Dual (grammatical number); Channel (broadcasting); Real-time computing; Artificial intelligence; Computer network","score_opus":0.052043996470161946,"score_gpt":0.27860576604987053,"score_spread":0.22656176957970858,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405695404","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":[0.8417645,0.003682771,0.13422568,0.00030296523,0.018760156,0.000221603,0.000009762511,0.00037609658,0.0006564756],"genre_scores_gemma":[0.9810013,0.00013130567,0.012463269,0.000007879182,0.005605552,0.00000984962,0.000004596166,0.000070650305,0.0007055728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99872476,0.000018960878,0.00052306463,0.00019483523,0.0002787662,0.00025960896],"domain_scores_gemma":[0.99944675,0.0000703136,0.000080817874,0.00009320732,0.00022864537,0.000080262864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007630774,0.00017826476,0.00021320586,0.00027111816,0.0001156477,0.000099363155,0.00007357155,0.00008768019,0.000012355274],"category_scores_gemma":[0.0001897594,0.00016090303,0.00019500755,0.00020487962,0.00002253513,0.000559643,0.000014979359,0.00033705906,0.0000023949792],"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":[0.00007637495,0.000054082266,0.00017165048,0.0005631968,0.0003042719,0.0000646824,0.0012847368,0.74162257,0.12392912,0.000121530495,0.007839753,0.12396802],"study_design_scores_gemma":[0.0004866553,0.00022172618,0.00020661531,0.0013158256,0.00017162618,0.0037209636,0.00020280026,0.7430161,0.19630376,0.00060820143,0.053252056,0.000493648],"about_ca_topic_score_codex":0.0000025307183,"about_ca_topic_score_gemma":0.000011372522,"teacher_disagreement_score":0.13923684,"about_ca_system_score_codex":0.00021001256,"about_ca_system_score_gemma":0.000055481527,"threshold_uncertainty_score":0.65614355},"labels":[],"label_agreement":null},{"id":"W4405745042","doi":"10.1016/j.egyr.2024.12.038","title":"Kolmogorov–Arnold recurrent network for short term load forecasting across diverse consumers","year":2024,"lang":"en","type":"article","venue":"Energy Reports","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Canada Research Chairs","keywords":"Term (time); Computer science; Economics; Econometrics; Artificial intelligence; Physics","score_opus":0.02544008887540088,"score_gpt":0.254263199666105,"score_spread":0.2288231107907041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405745042","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":[0.812685,0.027800195,0.040706977,0.00006271761,0.044168834,0.00055866333,0.0001426028,0.0048438315,0.06903117],"genre_scores_gemma":[0.9951877,0.00018522516,0.0014428317,0.000024111238,0.0011971885,0.000113627124,0.0001321194,0.000119930366,0.0015972453],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99782157,0.00001389175,0.0005882436,0.0004953842,0.00026652202,0.00081437983],"domain_scores_gemma":[0.99920124,0.00015639479,0.00005748727,0.00034047995,0.0000658284,0.00017854685],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00044276126,0.00034351108,0.00031385792,0.000055622444,0.00022440153,0.00017149586,0.000117416195,0.00016704289,0.000044750963],"category_scores_gemma":[0.00006090127,0.00034029988,0.00024203524,0.0002806048,0.000054120203,0.00021574931,0.00008487687,0.00021339199,0.000003542399],"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":[0.000046221146,0.00003640579,0.010500681,0.00059243117,0.0006383755,0.0030988636,0.0013010663,0.21361057,0.0009955637,0.0016613242,0.040823136,0.72669536],"study_design_scores_gemma":[0.00019619151,0.00010859197,0.00025638577,0.0010642769,0.00011702075,0.0013217898,0.000103906284,0.20670526,0.0037254267,0.00073088927,0.78470534,0.00096495124],"about_ca_topic_score_codex":0.000061424274,"about_ca_topic_score_gemma":0.0004763895,"teacher_disagreement_score":0.7438822,"about_ca_system_score_codex":0.00018243077,"about_ca_system_score_gemma":0.00006581926,"threshold_uncertainty_score":0.99990493},"labels":[],"label_agreement":null},{"id":"W4405777746","doi":"10.23646/9nev-py65","title":"Leveraging Artificial Intelligence to Improve the Integration of Photovoltaic Energy into the Grid","year":2024,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Energy Load and Power Forecasting","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":"Impact","funders":"Association Nationale de la Recherche et de la Technologie","keywords":"Photovoltaic system; Grid; Computer science; Energy (signal processing); Artificial intelligence; Systems engineering; Electrical engineering; Engineering; Geography; Physics","score_opus":0.017810559904216518,"score_gpt":0.2268444070583404,"score_spread":0.2090338471541239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405777746","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":[0.16783813,0.0034366287,0.7921702,0.0059274244,0.0029631183,0.00046690897,0.000048938124,0.0005011805,0.026647428],"genre_scores_gemma":[0.99097854,0.00031611597,0.0073562097,0.00007382342,0.000117138035,0.000115148476,0.000097339886,0.000056210076,0.0008894481],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99758565,0.00087981316,0.00056552805,0.00041211245,0.00030082537,0.00025609147],"domain_scores_gemma":[0.99686813,0.0009490474,0.00015600427,0.0013429656,0.000605762,0.00007811521],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027083051,0.00029016213,0.0002428834,0.00015816472,0.0002249335,0.00033236787,0.0011174863,0.00015990055,0.000037618032],"category_scores_gemma":[0.00051793276,0.0002079495,0.00018149804,0.0004945453,0.00014430447,0.00005121815,0.0010865233,0.0007617637,0.000019623514],"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":[0.00000831418,0.000065826665,0.000014509377,0.0002513016,0.00013480935,0.0000023186828,0.059424464,0.029378809,0.09247728,0.1770837,0.00073998305,0.6404187],"study_design_scores_gemma":[0.000016370728,3.69708e-7,0.00001897852,0.0013609204,0.000036425987,0.000002435657,0.0005375296,0.32025722,0.6376222,0.033239584,0.0066779805,0.00022999532],"about_ca_topic_score_codex":0.0036033983,"about_ca_topic_score_gemma":0.0049711694,"teacher_disagreement_score":0.82314044,"about_ca_system_score_codex":0.000111594185,"about_ca_system_score_gemma":0.00010318302,"threshold_uncertainty_score":0.8479935},"labels":[],"label_agreement":null},{"id":"W4405847138","doi":"10.1016/j.enbuild.2024.115217","title":"An efficient hybrid deep neural network model for multi-horizon forecasting of power loads in academic buildings","year":2024,"lang":"en","type":"article","venue":"Energy and Buildings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Horizon; Power (physics); Computer science; Engineering; Environmental science; Artificial intelligence; Mathematics","score_opus":0.02282055307544063,"score_gpt":0.2517576322499256,"score_spread":0.22893707917448497,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405847138","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":[0.70810014,0.0040007895,0.28691724,0.000013097742,0.00053408707,0.000067797606,0.000011217819,0.00019897067,0.00015666793],"genre_scores_gemma":[0.9818731,0.00011341213,0.017560706,0.00003686967,0.00023934747,0.000031044634,0.000018644781,0.00008045699,0.000046451925],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984664,0.00001553659,0.00044057416,0.0003810661,0.00013413152,0.00056233275],"domain_scores_gemma":[0.99953514,0.00013018631,0.000049064045,0.00013245133,0.000030448653,0.00012268138],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00036782864,0.0002790705,0.0003006327,0.0002058932,0.00008684473,0.00005442021,0.00018834666,0.00016614169,0.0000047396625],"category_scores_gemma":[0.000029300223,0.00027391256,0.00009598969,0.00030304774,0.000051871528,0.00023328277,0.00005388174,0.0002921001,2.065722e-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":[0.00002429467,0.0000137221095,0.00015509823,0.0001159291,0.000021119535,0.0000092349255,0.00047570074,0.9485611,0.0070608347,0.010496792,0.00008369871,0.032982502],"study_design_scores_gemma":[0.0003253011,0.000102335136,0.000018568282,0.00029301606,0.000020388687,0.00002743469,0.000025149093,0.98901826,0.007855169,0.00068530435,0.0013269486,0.00030209983],"about_ca_topic_score_codex":0.00003576748,"about_ca_topic_score_gemma":0.00002782599,"teacher_disagreement_score":0.27377293,"about_ca_system_score_codex":0.00004771011,"about_ca_system_score_gemma":0.000014357558,"threshold_uncertainty_score":0.99997133},"labels":[],"label_agreement":null},{"id":"W4405918468","doi":"10.1007/s43621-024-00783-5","title":"Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features","year":2024,"lang":"en","type":"article","venue":"Discover Sustainability","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":47,"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 Moncton","funders":"","keywords":"Artificial neural network; Renewable energy; Computer science; Artificial intelligence; Machine learning; Data mining; Engineering","score_opus":0.010294087609749147,"score_gpt":0.24480278163511038,"score_spread":0.23450869402536123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405918468","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":[0.7871442,0.0023401899,0.20846838,0.00009284794,0.00008695617,0.0003013463,0.00006930595,0.00023224691,0.0012645539],"genre_scores_gemma":[0.99767506,0.000001558367,0.001981265,0.0000249892,0.000080495556,0.00006376319,0.0000963637,0.000019692727,0.0000567926],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866456,0.00006750183,0.00032144596,0.00038722926,0.00013854203,0.000420728],"domain_scores_gemma":[0.9981721,0.0013542732,0.00005737197,0.00023065228,0.00010630719,0.00007930238],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036652194,0.0002580298,0.00053840614,0.00017515598,0.00010180254,0.000095282805,0.000115348084,0.00008446218,0.000014867628],"category_scores_gemma":[0.00014130663,0.00018556596,0.00019217038,0.00073207595,0.000135567,0.00008495624,0.000042277636,0.00014218439,4.9467836e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023605858,0.000017071543,0.0016581864,0.00030117578,0.0005920462,0.000008075115,0.0006996727,0.99277335,0.000073275405,0.00054767163,0.000078772195,0.0030146528],"study_design_scores_gemma":[0.00023227598,0.00021893773,0.0029394885,0.000032230495,0.00053631817,0.0000030917931,0.00020276925,0.9896535,0.0026988944,0.0027623917,0.0004894625,0.00023059383],"about_ca_topic_score_codex":0.00038374227,"about_ca_topic_score_gemma":0.001996638,"teacher_disagreement_score":0.2105309,"about_ca_system_score_codex":0.000094068244,"about_ca_system_score_gemma":0.00006244972,"threshold_uncertainty_score":0.756716},"labels":[],"label_agreement":null},{"id":"W4405945667","doi":"10.18280/mmep.111213","title":"Application of Classical Multiplicative Decomposition Time Series Predictive Model for the Forecast of Domestic Electricity Demand and Supply: A Ghanaian Context","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Electricity; Electricity demand; Decomposition; Multiplicative function; Supply and demand; Series (stratigraphy); Time series; Econometrics; Environmental economics; Demand forecasting; Economics; Mains electricity; Computer science; Natural resource economics; Environmental science; Microeconomics; Electricity generation; Mathematics; Operations management; Engineering; Machine learning; Chemistry; Geography; Power (physics); Thermodynamics","score_opus":0.011000800434555098,"score_gpt":0.21419690344143996,"score_spread":0.20319610300688487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405945667","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":[0.029975887,0.0016928177,0.9676828,0.000052919175,0.000018548842,0.00036306572,0.000036041904,0.000137471,0.00004045387],"genre_scores_gemma":[0.97525704,0.00014264171,0.024336338,0.000001729819,0.000022801765,0.0001768667,0.000009124042,0.000032369586,0.000021096017],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929947,0.0000056555277,0.0002885555,0.00015701451,0.00008354144,0.00016576947],"domain_scores_gemma":[0.9992295,0.0005482913,0.000029149756,0.000096856435,0.000035941393,0.000060294442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019628744,0.00015025814,0.00024106777,0.00006789053,0.000046971774,0.00002700168,0.00005926497,0.00007774088,7.924554e-7],"category_scores_gemma":[0.000025516441,0.00011324743,0.00004799194,0.00011169519,0.00006451409,0.00009215198,0.000017718197,0.000121420206,4.1749198e-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":[0.000014317526,0.000013325476,0.0000026152022,0.001708958,0.000074813666,1.3905043e-7,0.0015025582,0.9712706,0.0039109397,0.018903198,0.0000061459496,0.0025923545],"study_design_scores_gemma":[0.00013697123,0.00006607111,0.0000031836455,0.00040638188,0.00007754296,0.000016464362,0.000018507233,0.982119,0.0019756472,0.01504023,0.000030278177,0.00010970809],"about_ca_topic_score_codex":0.0000032970927,"about_ca_topic_score_gemma":9.584594e-7,"teacher_disagreement_score":0.94528115,"about_ca_system_score_codex":0.000019508874,"about_ca_system_score_gemma":0.00000843454,"threshold_uncertainty_score":0.4618096},"labels":[],"label_agreement":null},{"id":"W4406115269","doi":"10.1002/cjce.25590","title":"Deep learning models for forecasting sour gas generation in a petroleum refinery","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Refinery; Oil refinery; Sour gas; Petroleum engineering; Engineering; Environmental science; Waste management; Natural gas","score_opus":0.01856417580277116,"score_gpt":0.18624401286762354,"score_spread":0.16767983706485237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406115269","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":[0.84927005,0.0022570551,0.14626686,0.00026499727,0.0006710864,0.000083651095,0.0000019890429,0.000046076435,0.0011382112],"genre_scores_gemma":[0.9973043,0.000006306652,0.002259303,0.000027672495,0.00032997434,0.00000796914,0.000003950158,0.000029359218,0.000031159176],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913454,0.000008888264,0.00035774283,0.00007334911,0.00007796797,0.00034752866],"domain_scores_gemma":[0.9995627,0.00012175311,0.000046211037,0.00007431599,0.000055934062,0.00013912811],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035383034,0.00013240674,0.00019278991,0.00027456132,0.00006457252,0.000058837744,0.00017729451,0.000087145185,0.0000036467966],"category_scores_gemma":[0.000234545,0.00011818882,0.00009005583,0.00020899632,0.000014185382,0.00012861218,0.000008129176,0.00047487218,2.441439e-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":[0.0000043839773,0.0000010599455,0.000036070163,0.00004251146,0.000024735758,0.000008464289,0.00020227405,0.9820798,0.013888703,0.0009449598,0.00008100008,0.0026860565],"study_design_scores_gemma":[0.00026031936,0.000009975551,0.0000051615557,0.00017589127,0.000015227968,0.000040249506,0.000016071766,0.9876015,0.010311245,0.00034723748,0.0011085459,0.0001085503],"about_ca_topic_score_codex":0.00015959171,"about_ca_topic_score_gemma":0.0008580554,"teacher_disagreement_score":0.14803423,"about_ca_system_score_codex":0.00025650405,"about_ca_system_score_gemma":0.000104334984,"threshold_uncertainty_score":0.48196003},"labels":[],"label_agreement":null},{"id":"W4406248180","doi":"10.3390/en18020278","title":"LSTM vs. Prophet: Achieving Superior Accuracy in Dynamic Electricity Demand Forecasting","year":2025,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":24,"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":"Qassim University","keywords":"Mean absolute percentage error; Benchmark (surveying); Mean squared error; Computer science; Adaptability; Electricity; Grid; Artificial intelligence; Artificial neural network; Statistics; Engineering; Mathematics; Economics","score_opus":0.007517397878145455,"score_gpt":0.21815589275927852,"score_spread":0.21063849488113306,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406248180","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":[0.9829543,0.0023547872,0.0027784477,0.00008270337,0.0004351526,0.00009952191,0.000002730052,0.00040205137,0.0108903],"genre_scores_gemma":[0.99802566,0.00020709442,0.0012247559,0.000060021626,0.00005741819,0.000038846978,0.000009635918,0.00003410726,0.00034245045],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884117,0.000034596964,0.0003283969,0.00022830622,0.00010789593,0.00045960938],"domain_scores_gemma":[0.99946344,0.0002496147,0.00003110841,0.00018770024,0.000020402143,0.00004775562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019694206,0.00022402163,0.00026181398,0.00029775398,0.0001260328,0.00008201115,0.00020729072,0.000103764476,0.000022329834],"category_scores_gemma":[0.00029389936,0.00022188805,0.00006572639,0.0006470406,0.000030116578,0.00024876892,0.0000722891,0.00027360092,0.0000040366526],"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":[0.00003103416,0.000024288562,0.009381863,0.00031396444,0.000052539806,0.00004278447,0.0006867526,0.90142065,0.014128687,0.0022021513,0.00014781668,0.07156747],"study_design_scores_gemma":[0.0009329314,0.00006215892,0.011987037,0.00076063443,0.000036598656,0.000031640182,0.00031175403,0.9320811,0.03691446,0.0010186597,0.015077614,0.0007854054],"about_ca_topic_score_codex":0.0000986526,"about_ca_topic_score_gemma":0.00048383526,"teacher_disagreement_score":0.070782065,"about_ca_system_score_codex":0.0001447024,"about_ca_system_score_gemma":0.00004822331,"threshold_uncertainty_score":0.9048332},"labels":[],"label_agreement":null},{"id":"W4406248455","doi":"10.1142/s2972379525500012","title":"Machine learning natural gas price predictions","year":2025,"lang":"en","type":"article","venue":"Energy Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":66,"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":"","keywords":"Artificial intelligence; Machine learning; Artificial neural network; Computer science; Economy; Economics","score_opus":0.004107819576972419,"score_gpt":0.198189049091034,"score_spread":0.19408122951406157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406248455","genre_codex":"other","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":[0.10954344,0.0045529264,0.08326954,0.0002306151,0.006053101,0.000044738936,0.0000037730174,0.001707032,0.7945948],"genre_scores_gemma":[0.99315757,0.00008434773,0.00076982536,0.000063649015,0.00006242416,0.000005200292,0.000003444376,0.0000070890014,0.005846433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928415,0.000008932468,0.000106159045,0.00016636534,0.00015876064,0.0002756104],"domain_scores_gemma":[0.9997304,0.00004151049,0.000012938061,0.00012683253,0.000032261138,0.00005603483],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015635935,0.00008770297,0.00006750618,0.00019129775,0.00033032498,0.000060786897,0.00025209552,0.000025615502,0.000025267866],"category_scores_gemma":[0.00007983272,0.000082154525,0.000025583675,0.0010905748,0.000118480675,0.00024175784,0.00006131655,0.000174478,0.0000053003273],"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":[0.0000019571526,0.000009065997,0.0012764577,0.0000095693285,0.000013317744,0.000004194601,0.00017070153,0.8412044,0.019756306,0.116062656,0.0005078636,0.020983515],"study_design_scores_gemma":[0.00007828867,0.000009286988,0.00081749994,0.00003758213,0.000003679349,0.0000063786533,0.000017085482,0.8076015,0.011587225,0.00027575076,0.17945707,0.00010864441],"about_ca_topic_score_codex":0.00006814648,"about_ca_topic_score_gemma":0.00004072326,"teacher_disagreement_score":0.8836141,"about_ca_system_score_codex":0.00006196977,"about_ca_system_score_gemma":0.00004671567,"threshold_uncertainty_score":0.33501643},"labels":[],"label_agreement":null},{"id":"W4406339889","doi":"","title":"Spatio-temporal clustering and reconciliation for regional electricity demand forecasting","year":2025,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Energy Load and Power Forecasting","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":"Cluster analysis; Electricity demand; Demand forecasting; Electricity; Economics; Computer science; Econometrics; Artificial intelligence; Operations management; Electricity generation; Engineering; Power (physics)","score_opus":0.023952751413191878,"score_gpt":0.22111551257754425,"score_spread":0.19716276116435238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406339889","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":[0.14453214,0.00196287,0.8322581,0.0012526199,0.00042709525,0.0005360874,0.00010162003,0.00047389773,0.018455584],"genre_scores_gemma":[0.9020638,0.0006044699,0.09304354,0.00006961685,0.00008040989,0.00015969304,0.001105433,0.00005327355,0.0028197514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979416,0.00059050333,0.00048250915,0.00048617588,0.00017691354,0.00032230557],"domain_scores_gemma":[0.99708897,0.0013211804,0.00023085193,0.0005551175,0.000681444,0.00012243354],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0026052778,0.0003000923,0.00032419642,0.00020407268,0.00030961717,0.00022732955,0.0003536876,0.00028636458,0.000014532279],"category_scores_gemma":[0.0009276602,0.00035675053,0.00012920362,0.00021436115,0.000058824153,0.00012312733,0.00035311317,0.000424274,9.1998675e-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":[0.00014766608,0.00033914507,0.02074165,0.008492628,0.0007948044,0.000010092399,0.027733056,0.17509189,0.002002619,0.05231735,0.009338551,0.70299053],"study_design_scores_gemma":[0.00043202532,4.344668e-7,0.0003763474,0.0018308356,0.000042664695,0.000009093945,0.000027359081,0.9735498,0.0069040367,0.002965798,0.013484073,0.0003775542],"about_ca_topic_score_codex":0.00038123227,"about_ca_topic_score_gemma":0.004119849,"teacher_disagreement_score":0.7984579,"about_ca_system_score_codex":0.000158714,"about_ca_system_score_gemma":0.00014352742,"threshold_uncertainty_score":0.9998884},"labels":[],"label_agreement":null},{"id":"W4406864694","doi":"10.1016/j.enconman.2025.119555","title":"Machine learning and statistical approaches for wind speed estimation at partially sampled and unsampled locations; review and open questions","year":2025,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Energy Load and Power Forecasting","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":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Wind speed; Computer science; Statistical analysis; Machine learning; Meteorology; Environmental science; Artificial intelligence; Statistics; Physics; Mathematics","score_opus":0.02403595555986322,"score_gpt":0.2581462129738454,"score_spread":0.23411025741398217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406864694","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":[0.00950777,0.04800222,0.92323226,0.0029786052,0.00038572223,0.0016461526,0.00006488422,0.00029699176,0.013885375],"genre_scores_gemma":[0.8755725,0.07831651,0.03596619,0.00087488507,0.000027619199,0.00008109816,0.00069143483,0.000039573206,0.008430184],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946344,0.000031520718,0.000151545,0.000195803,0.00004419916,0.0001135181],"domain_scores_gemma":[0.9997199,0.00011181218,0.000026050853,0.00006592669,0.000012718488,0.000063572246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018115524,0.00011340063,0.0001694202,0.00006634531,0.00021563085,0.00007402942,0.00004240461,0.00003014106,0.000024749968],"category_scores_gemma":[0.000030299314,0.00011025964,0.000009186535,0.000068879475,0.00004339733,0.0000898541,0.00018796223,0.00004090034,3.6064063e-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":[0.00008813915,0.00004648959,0.0014650621,0.0072359596,0.0004217825,0.0000052455666,0.00022836066,0.02706111,0.00008420374,0.6111792,0.0059591574,0.34622532],"study_design_scores_gemma":[0.0011416242,0.000045239412,0.0012376376,0.00048227704,0.0003015762,0.00000424288,0.00006275052,0.71197164,0.000053285858,0.0012320844,0.28326902,0.00019863897],"about_ca_topic_score_codex":0.00007919209,"about_ca_topic_score_gemma":0.00007593582,"teacher_disagreement_score":0.8872661,"about_ca_system_score_codex":0.000021414307,"about_ca_system_score_gemma":0.0000048035795,"threshold_uncertainty_score":0.4496258},"labels":[],"label_agreement":null},{"id":"W4406873467","doi":"10.1016/j.gerr.2025.100115","title":"Using machine learning methods for long-term technical and economic evaluation of wind power plants","year":2025,"lang":"en","type":"article","venue":"Green Energy and Resources","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Term (time); Wind power; Power (physics); Computer science; Environmental science; Environmental economics; Engineering; Economics; Electrical engineering; Physics","score_opus":0.02928163507789641,"score_gpt":0.3123080897538571,"score_spread":0.2830264546759607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406873467","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":[0.97554797,0.006641062,0.015681809,0.0000117378595,0.000105282445,0.00004683937,0.0000055536966,0.000041991425,0.0019177627],"genre_scores_gemma":[0.9960076,0.0001293107,0.0036578253,0.000010232963,0.00003886564,0.0000038525623,0.000009671928,0.000012759591,0.00012987011],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994797,0.00006321903,0.00016530338,0.00012873381,0.00004615479,0.00011690119],"domain_scores_gemma":[0.99971,0.00014237332,0.00003826185,0.0000657846,0.000014762049,0.000028806877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004776925,0.00009782408,0.00016049668,0.00011135463,0.00008740283,0.000015148299,0.00004861241,0.0000932216,0.00000937035],"category_scores_gemma":[0.000019262856,0.000088891335,0.000028955588,0.000038479327,0.00004554337,0.000045129855,0.000037553906,0.00006254436,3.8068308e-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":[0.0001096336,0.000019536637,0.0629693,0.0002901872,0.00032982472,0.0000016867996,0.0007142469,0.34209698,0.035329886,0.0020958153,0.000018740726,0.5560242],"study_design_scores_gemma":[0.0009783139,0.000069346475,0.021072164,0.00027896804,0.00015228215,0.000025630106,0.000043259522,0.95136696,0.016431602,0.0007266171,0.008606567,0.00024829275],"about_ca_topic_score_codex":0.00018851343,"about_ca_topic_score_gemma":0.00022357442,"teacher_disagreement_score":0.60927,"about_ca_system_score_codex":0.00001981983,"about_ca_system_score_gemma":0.0000098191595,"threshold_uncertainty_score":0.36248833},"labels":[],"label_agreement":null},{"id":"W4406949747","doi":"10.1109/tit.2025.3536463","title":"Optimal Short-Term Forecast for Locally Stationary Functional Time Series","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Information Theory","topic":"Energy Load and Power Forecasting","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":"University of Toronto","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Term (time); Series (stratigraphy); Time series; Computer science; Econometrics; Mathematics; Machine learning","score_opus":0.007304194607999415,"score_gpt":0.20214673390949295,"score_spread":0.19484253930149353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406949747","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":[0.0062093553,0.000019718858,0.9772125,0.000041170963,0.0011172419,0.00022040952,0.0001994097,0.0003909039,0.014589279],"genre_scores_gemma":[0.98878866,0.000027363358,0.0061623626,0.00027416844,0.00007720947,0.00033716703,0.0002876683,0.000028703182,0.0040166867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925107,0.000016101985,0.0003400224,0.00008129998,0.00012269273,0.00018882162],"domain_scores_gemma":[0.99952585,0.00017680817,0.000025688765,0.00012783152,0.00009907806,0.000044741708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018025248,0.00015929571,0.000118339296,0.00027837275,0.0002439142,0.00006295171,0.00008455599,0.00009586532,0.00029267193],"category_scores_gemma":[0.0000055928954,0.00016626762,0.00010212446,0.00018635351,0.000045888086,0.0012262588,8.476467e-7,0.0001434669,0.00009370885],"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":[0.0002421257,0.000017898288,0.0000019188149,0.00009138764,0.00009606139,2.0675097e-7,0.00034102268,0.90862197,0.00025624808,0.0091758035,0.0014744676,0.07968088],"study_design_scores_gemma":[0.0022319867,0.0004083437,0.0003300267,0.00040987163,0.00018581148,0.00004884557,0.001032674,0.8497701,0.079091385,0.007292657,0.0581491,0.0010491844],"about_ca_topic_score_codex":4.883035e-7,"about_ca_topic_score_gemma":0.0000019057941,"teacher_disagreement_score":0.9825793,"about_ca_system_score_codex":0.00008079605,"about_ca_system_score_gemma":0.00005129661,"threshold_uncertainty_score":0.6780197},"labels":[],"label_agreement":null},{"id":"W4407086267","doi":"10.3390/wind5010004","title":"Maximizing Wind Turbine Power Generation Through Adaptive Fuzzy Logic Control for Optimal Efficiency and Performance","year":2025,"lang":"en","type":"article","venue":"Wind","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Wind power; Turbine; Automotive engineering; Fuzzy logic; Renewable energy; Computer science; Pitch control; Variable speed wind turbine; Electric power system; Control theory (sociology); Wind speed; Power (physics); Engineering; Control (management); Electrical engineering; Permanent magnet synchronous generator; Meteorology; Aerospace engineering","score_opus":0.016337748957781482,"score_gpt":0.22048416170188026,"score_spread":0.20414641274409878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407086267","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":[0.8681858,0.0016548348,0.11331495,0.000078759775,0.0006295483,0.0002420143,0.0000155257,0.000110714056,0.015767818],"genre_scores_gemma":[0.99443126,0.00003193525,0.0049973372,0.00013064475,0.00014741358,0.000008985558,0.000010603074,0.00001597788,0.00022586538],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993526,0.000008542568,0.00016408757,0.00017262781,0.00006513388,0.00023704467],"domain_scores_gemma":[0.9997677,0.000048130318,0.000023736533,0.000097531636,0.000035869292,0.00002703411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000103347455,0.00014347612,0.0001540455,0.00005022472,0.00013780179,0.000039805655,0.00006521083,0.00007791593,0.000011581655],"category_scores_gemma":[0.00001824422,0.00013307572,0.000035552435,0.000108499815,0.00003142024,0.00017896603,0.000016247344,0.000101354686,0.0000028364436],"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":[0.000078399855,0.000023345336,0.0006040828,0.000068712085,0.00007958068,0.0000028752136,0.0009297241,0.9720141,0.009714187,0.010770007,0.00053767156,0.0051772776],"study_design_scores_gemma":[0.0018211324,0.00028138733,0.0015976928,0.000112000154,0.00004948218,0.000009377318,0.00011142265,0.9820474,0.0077097598,0.00027056297,0.005671419,0.00031836296],"about_ca_topic_score_codex":0.0000029964647,"about_ca_topic_score_gemma":0.0000018283098,"teacher_disagreement_score":0.1262454,"about_ca_system_score_codex":0.00003089273,"about_ca_system_score_gemma":0.000014577565,"threshold_uncertainty_score":0.54266703},"labels":[],"label_agreement":null},{"id":"W4407121222","doi":"10.1088/2631-8695/adb23f","title":"Forecasting the electricity usage in single-family housings of a case study via kho-kho optimization algorithm","year":2025,"lang":"en","type":"article","venue":"Engineering Research Express","topic":"Energy Load and Power Forecasting","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":"Electricity; Algorithm; Computer science; Optimization algorithm; Mathematical optimization; Engineering; Mathematics; Electrical engineering","score_opus":0.03876009391278107,"score_gpt":0.27916054594400824,"score_spread":0.24040045203122717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407121222","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":[0.67363673,0.00067341863,0.3239428,0.0000076595215,0.0002319576,0.00044429454,0.000004469071,0.00017610496,0.0008825272],"genre_scores_gemma":[0.98994255,0.000021115166,0.009737762,0.000003965742,0.00008300457,0.00009792842,0.0000033654499,0.00005589031,0.00005438858],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980304,0.00012993313,0.0004542712,0.0002800166,0.00043237663,0.0006729873],"domain_scores_gemma":[0.99853295,0.0008403617,0.000034527904,0.00037557096,0.00014641836,0.000070142894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014593337,0.00022543392,0.00028242328,0.0007331048,0.00015384593,0.000093901544,0.0003588228,0.000103626604,0.000005897027],"category_scores_gemma":[0.00035866807,0.00020527116,0.000056829627,0.0020128638,0.000046967863,0.00019848208,0.0001664702,0.00076529634,6.033232e-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":[0.0000069366483,0.000102586215,0.00032610097,0.000121369616,0.000047945887,0.00038478646,0.0014822199,0.97096914,0.011154408,0.000014964688,0.000051875835,0.015337657],"study_design_scores_gemma":[0.00042559713,0.00008809178,0.00009976997,0.00025218318,0.0000108231825,0.00006690115,0.00062624016,0.989792,0.008285218,0.000015802812,0.00016625486,0.00017111558],"about_ca_topic_score_codex":0.0007008858,"about_ca_topic_score_gemma":0.00005085485,"teacher_disagreement_score":0.31630582,"about_ca_system_score_codex":0.00020007676,"about_ca_system_score_gemma":0.00003666117,"threshold_uncertainty_score":0.8370715},"labels":[],"label_agreement":null},{"id":"W4407241407","doi":"10.36227/techrxiv.173894961.19222689/v1","title":"Kolmogorov-Arnold recurrent network for short term load forecasting across diverse consumers","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Western University","funders":"Canada Research Chairs","keywords":"Term (time); Computer science; Econometrics; Economics; Physics","score_opus":0.044975452639313754,"score_gpt":0.28184247948989366,"score_spread":0.23686702685057992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407241407","genre_codex":"other","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":[0.3410844,0.0125231305,0.1904308,0.00012930161,0.06716698,0.005032278,0.0043464173,0.0069633196,0.37232336],"genre_scores_gemma":[0.9525387,0.001024382,0.034612887,0.00013553664,0.0024175057,0.00078646385,0.0011826293,0.00024869747,0.0070531513],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99707305,0.000025873078,0.00072633533,0.00071918126,0.0002892143,0.0011663318],"domain_scores_gemma":[0.9985838,0.00034689496,0.00010008448,0.00060499983,0.0001717925,0.00019244841],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00049821474,0.00071540027,0.00072789827,0.000082565006,0.00029612033,0.00019435126,0.00057906454,0.00059601554,0.00006736387],"category_scores_gemma":[0.00010155173,0.0007446392,0.00046553175,0.00019439755,0.00007378969,0.00010007133,0.00089539663,0.0009252218,0.000007688253],"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":[0.00008138307,0.00004372022,0.0117673995,0.0029079944,0.0008913562,0.000022278766,0.0012288229,0.5905213,0.000042990967,0.00058870367,0.035437442,0.35646662],"study_design_scores_gemma":[0.0011445596,0.00009692296,0.0005532371,0.00513833,0.00041948716,0.000020254049,0.00039484858,0.86566985,0.0012650219,0.0010114788,0.12165758,0.0026284356],"about_ca_topic_score_codex":0.00009849951,"about_ca_topic_score_gemma":0.001371841,"teacher_disagreement_score":0.61145437,"about_ca_system_score_codex":0.00038249017,"about_ca_system_score_gemma":0.0001408799,"threshold_uncertainty_score":0.99950045},"labels":[],"label_agreement":null},{"id":"W4407301494","doi":"10.31224/4361","title":"Leveraging Hypernetworks and Learnable Kernels for Consumer Energy Forecasting Across Diverse Consumer Types","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Western University","funders":"Environment Canada","keywords":"Energy (signal processing); Computer science; Mathematics; Statistics","score_opus":0.0374229406987648,"score_gpt":0.2519658662963699,"score_spread":0.21454292559760513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407301494","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":[0.39395827,0.044899106,0.39674205,0.00011942198,0.01225989,0.0012839816,0.000773434,0.0032262397,0.1467376],"genre_scores_gemma":[0.97433114,0.0016019215,0.008098378,0.00017677566,0.00042144302,0.00013807442,0.00016799427,0.000108635206,0.014955621],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99807006,0.000027602557,0.00044104474,0.0005727993,0.00013910478,0.00074941234],"domain_scores_gemma":[0.9988697,0.00044390123,0.00009599485,0.00033613472,0.00011590132,0.00013834181],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027497858,0.0005176066,0.0005863028,0.00010888138,0.0002712062,0.00023521928,0.0002577197,0.00048385753,0.00007731015],"category_scores_gemma":[0.00007461939,0.0005301564,0.00017016778,0.00011321337,0.00009597097,0.00011085578,0.0006883471,0.0005532539,0.0000036181843],"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":[0.000050974228,0.000024612908,0.0060813283,0.001824666,0.0010754355,0.000028205934,0.0016113213,0.74265474,0.00008047487,0.00255356,0.012299922,0.23171476],"study_design_scores_gemma":[0.0007518618,0.000015881431,0.000039449955,0.00094691577,0.00016590499,0.00002190367,0.00035543388,0.80867237,0.0011479685,0.0012044797,0.18576777,0.000910038],"about_ca_topic_score_codex":0.00049522636,"about_ca_topic_score_gemma":0.0002248474,"teacher_disagreement_score":0.58037287,"about_ca_system_score_codex":0.00007811108,"about_ca_system_score_gemma":0.00005906721,"threshold_uncertainty_score":0.999715},"labels":[],"label_agreement":null},{"id":"W4407303910","doi":"10.1109/ecce55643.2024.10861361","title":"From Black Box to Clarity: AI-Powered Smart Grid Optimization with Kolmogorov-Arnold Networks","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Alberta","funders":"","keywords":"CLARITY; Black box; Computer science; Smart grid; Grid; Artificial intelligence; Engineering; Mathematics; Electrical engineering","score_opus":0.006324035788049331,"score_gpt":0.1957671707848793,"score_spread":0.18944313499682996,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407303910","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":[0.0149097545,0.0006386794,0.9304911,0.0005021005,0.002183241,0.00015463558,0.000025064726,0.0017753816,0.04932006],"genre_scores_gemma":[0.96776736,0.000091899776,0.028114779,0.00069301925,0.0013903514,0.00002113638,0.00023647046,0.00013569026,0.0015492954],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990582,0.000012585148,0.00019722222,0.0002755148,0.00014228231,0.00031421674],"domain_scores_gemma":[0.9995406,0.00005604612,0.00000951224,0.0002268921,0.000028267688,0.00013865686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007397165,0.00021183904,0.00017049174,0.00009144037,0.000042314536,0.00021945382,0.000120970064,0.00013630744,0.00050737854],"category_scores_gemma":[0.000007121037,0.00018004059,0.000046600042,0.00038730784,0.000017580234,0.00022314378,0.00003783376,0.00028090997,0.00009296526],"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":[0.000009410114,0.00000607539,0.00016269999,0.000021502081,0.00007547976,0.000024805577,0.00034180644,0.97787297,0.000073440926,0.00041109792,0.018782167,0.0022185473],"study_design_scores_gemma":[0.00013298387,0.00004596775,0.00006089218,0.0001702457,0.000029906561,0.0000045501865,0.000053889155,0.9576337,0.00053696777,0.00005385858,0.040990632,0.00028642162],"about_ca_topic_score_codex":0.00020623834,"about_ca_topic_score_gemma":0.00020232565,"teacher_disagreement_score":0.9528576,"about_ca_system_score_codex":0.00005028705,"about_ca_system_score_gemma":0.000016121778,"threshold_uncertainty_score":0.73418427},"labels":[],"label_agreement":null},{"id":"W4407367090","doi":"10.2139/ssrn.5074301","title":"Stochastic Modelling and Simulation of Wind Power, Electricity Load and Natural Gas Prices for Texas Energy Market","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Wind power; Natural gas; Electricity; Electricity market; Natural gas prices; Econometrics; Environmental science; Economics; Engineering; Electrical engineering; Waste management","score_opus":0.006965886471838272,"score_gpt":0.22004301281382954,"score_spread":0.21307712634199127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407367090","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":[0.2109369,0.04474279,0.743013,0.000022121592,0.00045050995,0.00015827308,0.000018631807,0.000056438188,0.00060132414],"genre_scores_gemma":[0.99275476,0.0059850835,0.00061356294,0.000009793498,0.00017361846,0.000004033944,0.000011630976,0.000036351223,0.00041115572],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978542,0.00003402136,0.0004548258,0.00028348106,0.00021967449,0.001153802],"domain_scores_gemma":[0.9990859,0.00032800445,0.00022111661,0.00013424557,0.00016198054,0.000068734],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007883315,0.0003335893,0.00043025252,0.00025728633,0.00011573753,0.0000738162,0.00017706549,0.00025429216,0.000003324916],"category_scores_gemma":[0.00007067555,0.00033214845,0.000119175245,0.0001294872,0.00003276513,0.00012315794,0.00009354409,0.0017058724,4.7137537e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000092935006,0.00001066038,0.000029771925,0.00016145653,0.0002593731,4.6166312e-7,0.00010072383,0.9837298,0.000049692557,0.004304485,0.000011120053,0.01124957],"study_design_scores_gemma":[0.00045542367,0.00010418493,0.000018813575,0.00026603474,0.00011378542,0.00004124712,0.000034246226,0.93294543,0.00012505676,0.06540012,0.00021948726,0.0002761841],"about_ca_topic_score_codex":0.000061879975,"about_ca_topic_score_gemma":0.00015973608,"teacher_disagreement_score":0.78181785,"about_ca_system_score_codex":0.0005236657,"about_ca_system_score_gemma":0.00077968265,"threshold_uncertainty_score":0.99991304},"labels":[],"label_agreement":null},{"id":"W4407409814","doi":"10.2139/ssrn.5134519","title":"Simple Data Driven Metrics to Characterize Electrical Grid Outage and Recovery Performance","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Simple (philosophy); Computer science; Grid; Distributed computing; Reliability engineering; Engineering; Mathematics","score_opus":0.01962587318572582,"score_gpt":0.23923923597273228,"score_spread":0.21961336278700647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407409814","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":[0.9425583,0.010976372,0.0392398,0.00018457593,0.003421232,0.00036621423,0.0005052746,0.000358861,0.002389397],"genre_scores_gemma":[0.9310653,0.06373466,0.0010633087,0.00016239229,0.0019373071,0.000018255967,0.000604623,0.000099436365,0.0013147041],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99639595,0.00005467642,0.0005263861,0.00049497857,0.00029944952,0.0022285564],"domain_scores_gemma":[0.9988297,0.000117085416,0.000131657,0.0006642681,0.000063710635,0.00019355776],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0011213637,0.00042070163,0.00051492435,0.000577164,0.00016880725,0.00021711832,0.0010941441,0.00030642364,0.000014213466],"category_scores_gemma":[0.00017337858,0.00042596916,0.000101987825,0.0004834571,0.000016648604,0.0002860098,0.0009594725,0.0051367385,0.000010256485],"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":[0.00021933363,0.00010877804,0.009129567,0.00078418705,0.0023822037,0.0000460341,0.0004568802,0.1403341,0.0010421484,0.0031624616,0.0055424143,0.8367919],"study_design_scores_gemma":[0.0019408918,0.0013004149,0.0074376566,0.0014537015,0.0008087083,0.0011139455,0.00018839481,0.79484576,0.0008761251,0.02288757,0.16326357,0.0038832505],"about_ca_topic_score_codex":0.00004135437,"about_ca_topic_score_gemma":0.00019656119,"teacher_disagreement_score":0.83290863,"about_ca_system_score_codex":0.0009483547,"about_ca_system_score_gemma":0.0011188951,"threshold_uncertainty_score":0.9998192},"labels":[],"label_agreement":null},{"id":"W4407626438","doi":"10.1016/j.renene.2025.122692","title":"A reinforcement learning-based ensemble forecasting framework for renewable energy forecasting","year":2025,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":16,"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":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Reinforcement learning; Renewable energy; Probabilistic forecasting; Ensemble learning; Computer science; Artificial intelligence; Demand forecasting; Technology forecasting; Machine learning; Engineering; Operations research","score_opus":0.019251033894858122,"score_gpt":0.22529882533696197,"score_spread":0.20604779144210383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407626438","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":[0.0008121811,0.0015017032,0.9105825,0.000050921524,0.0013783848,0.00013528489,0.0000065994886,0.000916807,0.08461563],"genre_scores_gemma":[0.94269377,0.00013940652,0.030722734,0.0004897708,0.0006479548,0.00044878907,0.00022474602,0.00020229278,0.024430554],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964918,0.00006782687,0.00088658696,0.0006705303,0.00035069435,0.0015325969],"domain_scores_gemma":[0.99772626,0.0010356187,0.00021045623,0.0005803761,0.00018770869,0.0002595735],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00047163872,0.0006553304,0.0006735229,0.00052475755,0.0006537978,0.00019921646,0.00046530063,0.00046355854,0.00010154655],"category_scores_gemma":[0.0005389534,0.0007161231,0.00035111693,0.0010712895,0.000059271268,0.00024331194,0.00012767443,0.0002938247,0.000002283553],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006902026,0.00002742949,0.00010216651,0.00026907536,0.00015139264,0.000013146898,0.00005810262,0.97219414,0.0020838957,0.00718112,0.006161221,0.011689315],"study_design_scores_gemma":[0.0006676947,0.0001220773,9.153549e-7,0.00077021145,0.0000572766,0.0000072482976,0.00005701475,0.69443816,0.09670667,0.009010918,0.19765289,0.0005089196],"about_ca_topic_score_codex":0.006814186,"about_ca_topic_score_gemma":0.0031229665,"teacher_disagreement_score":0.9418816,"about_ca_system_score_codex":0.00035409007,"about_ca_system_score_gemma":0.00023125578,"threshold_uncertainty_score":0.99979955},"labels":[],"label_agreement":null},{"id":"W4407679140","doi":"10.1007/s40866-025-00249-1","title":"Forecasting International Electricity Market Prices by Using Optimized Machine Learning Systems","year":2025,"lang":"en","type":"article","venue":"Smart Grids and Sustainable Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Electricity; Electricity market; Electricity price forecasting; Economics; Computer science; Business; Engineering; Electrical engineering","score_opus":0.005621348855098591,"score_gpt":0.1941427107118288,"score_spread":0.18852136185673019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407679140","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":[0.2865521,0.033108693,0.37337586,0.00013732786,0.0037867818,0.0003211247,0.000029722289,0.0010722408,0.30161613],"genre_scores_gemma":[0.9752842,0.0006423557,0.0007780226,0.000038900616,0.00017746749,0.000024102255,0.000052493473,0.000039407987,0.02296301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99870044,0.000046724505,0.000299544,0.00024301079,0.00014263546,0.0005676453],"domain_scores_gemma":[0.9995326,0.00012862487,0.000058503756,0.00010086804,0.000102717866,0.00007667743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040006635,0.0002260076,0.00026124154,0.00024963025,0.00034827524,0.00022129224,0.00014916479,0.000108634405,0.000031240874],"category_scores_gemma":[0.00009482362,0.00022435194,0.000052076623,0.00038504708,0.000027538932,0.00024613796,0.0001206748,0.00021683612,1.3955113e-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":[0.00008836702,0.00003318444,0.005390609,0.00051100255,0.00033373604,0.00007915353,0.00014766125,0.9594525,0.0010632902,0.014995513,0.008947032,0.008957946],"study_design_scores_gemma":[0.00037799877,0.000018316436,0.000011820853,0.00006783106,0.000019227862,0.000016505785,0.00025392498,0.79699373,0.00044085053,0.000084039784,0.20152983,0.00018594056],"about_ca_topic_score_codex":0.0016100268,"about_ca_topic_score_gemma":0.000015868534,"teacher_disagreement_score":0.68873215,"about_ca_system_score_codex":0.00017118738,"about_ca_system_score_gemma":0.000040553023,"threshold_uncertainty_score":0.91488063},"labels":[],"label_agreement":null},{"id":"W4407691436","doi":"10.1109/mosicom63082.2024.10881012","title":"Short-term Wind Power Ramp Forecasting Using Sequential Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 New Brunswick","funders":"","keywords":"Term (time); Wind power; Wind power forecasting; Computer science; Power (physics); Meteorology; Environmental science; Electric power system; Engineering; Electrical engineering","score_opus":0.047854021743756074,"score_gpt":0.24881955327053243,"score_spread":0.20096553152677635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407691436","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":[0.65445894,0.0013386483,0.14871554,0.0000068855097,0.0026307716,0.00009517329,0.000008898048,0.0014138964,0.19133124],"genre_scores_gemma":[0.98844546,0.000006726648,0.010615291,0.000010665338,0.00042617405,0.0000028462573,0.000015932937,0.00008460317,0.00039232784],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892616,0.000012418023,0.00024326248,0.00025428092,0.0001598151,0.00040407444],"domain_scores_gemma":[0.9996673,0.000057821504,0.000007494424,0.00015811947,0.000016973172,0.00009229719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015687157,0.00021733364,0.00015865505,0.0001343447,0.00008926333,0.00023078997,0.0001188534,0.00011041501,0.00025709637],"category_scores_gemma":[0.0000123166255,0.00019889874,0.00010796965,0.00026031258,0.00003184367,0.0003092192,0.000051014285,0.0002468046,0.000020645028],"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":[0.00001010581,0.000042047985,0.0018915674,0.0010106305,0.00040590178,0.00034717977,0.003258688,0.86119515,0.06914407,0.009157546,0.001738489,0.05179864],"study_design_scores_gemma":[0.000056614404,0.000011716471,0.000034681223,0.00016976104,0.000031343723,0.00022635542,0.00007696592,0.9909855,0.005088735,0.00008314551,0.0028966472,0.00033854684],"about_ca_topic_score_codex":0.000009640128,"about_ca_topic_score_gemma":0.0000025768884,"teacher_disagreement_score":0.3339865,"about_ca_system_score_codex":0.00007104191,"about_ca_system_score_gemma":0.000018447397,"threshold_uncertainty_score":0.81108546},"labels":[],"label_agreement":null},{"id":"W4407872235","doi":"10.2139/ssrn.5125439","title":"LSTM and Transformer-based framework for bias correction of ERA5 hourly wind speeds","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Transformer; Wind speed; Computer science; Environmental science; Artificial intelligence; Meteorology; Electrical engineering; Engineering; Physics; Voltage","score_opus":0.015396679116360931,"score_gpt":0.2457994070116075,"score_spread":0.23040272789524657,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407872235","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":[0.21746697,0.009432738,0.7662172,0.00014286407,0.0041978317,0.0003118835,0.00006001705,0.00012982616,0.0020406959],"genre_scores_gemma":[0.9914232,0.0044147996,0.0030530095,0.000025393289,0.00053957995,0.000010772545,0.00003580609,0.00005679042,0.00044065632],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99787873,0.000034809415,0.00047150292,0.00020803163,0.00017679083,0.0012301629],"domain_scores_gemma":[0.9992757,0.00024101432,0.00015924525,0.00016266246,0.00008796596,0.000073417665],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00083043915,0.00031275421,0.00043120157,0.0003044969,0.0001122722,0.000059427337,0.00020507514,0.00043762985,0.0000110449255],"category_scores_gemma":[0.00008098403,0.00031252397,0.00026274077,0.00015715977,0.000034483488,0.00006722999,0.000016661714,0.0035220056,4.3919223e-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":[0.0002896222,0.0000791499,0.0010544477,0.0011719516,0.001373339,0.0000025436875,0.0008023833,0.6046604,0.00073960226,0.035969052,0.00029061953,0.35356688],"study_design_scores_gemma":[0.003372759,0.0016083515,0.00040833274,0.0067115403,0.0010826731,0.0002891977,0.0010346796,0.3014949,0.027560024,0.6433604,0.01113614,0.0019410553],"about_ca_topic_score_codex":0.00005045701,"about_ca_topic_score_gemma":0.00027792933,"teacher_disagreement_score":0.77395624,"about_ca_system_score_codex":0.00044304342,"about_ca_system_score_gemma":0.0012049217,"threshold_uncertainty_score":0.9999327},"labels":[],"label_agreement":null},{"id":"W4408042924","doi":"10.1007/s12667-024-00718-z","title":"Data-driven electricity price calibration based on Bayesian inference","year":2025,"lang":"en","type":"article","venue":"Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Water Power Technologies Office","keywords":"Calibration; Bayesian probability; Bayesian inference; Inference; Econometrics; Electricity; Computer science; Economics; Artificial intelligence; Statistics; Mathematics; Engineering","score_opus":0.015085665686923782,"score_gpt":0.23037634660939496,"score_spread":0.21529068092247117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408042924","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":[0.0025454173,0.00034325395,0.9131403,0.000039342573,0.0014303249,0.000069972404,0.000039088176,0.00055488787,0.08183744],"genre_scores_gemma":[0.9984993,0.00001403161,0.00022919639,0.00009866898,0.00017206362,0.00002272484,0.00019738387,0.000020296338,0.00074637274],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909246,0.0000613086,0.00024061359,0.00023118625,0.00014922193,0.00022521615],"domain_scores_gemma":[0.99921095,0.00016166049,0.00003664545,0.0005147917,0.000022996965,0.00005297719],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001237071,0.00015231334,0.00016860334,0.000159261,0.00007387463,0.000081815604,0.00031323347,0.00009885915,0.000012844116],"category_scores_gemma":[0.000050060815,0.00014746875,0.000024464158,0.00043031777,0.0000094991965,0.00015945455,0.000032333308,0.00011499853,0.0000041439516],"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":[0.0000041285857,0.000012470431,0.0004012379,0.00006306074,0.000022988983,0.000003694736,0.000012819037,0.97980654,0.00041106084,0.014703029,0.003189246,0.0013697421],"study_design_scores_gemma":[0.00013282959,0.000017497521,0.00007926853,0.0001596569,0.000007316101,6.8558705e-7,0.000004794795,0.9524927,0.0011691239,0.000026850208,0.045773312,0.00013595093],"about_ca_topic_score_codex":0.00028141428,"about_ca_topic_score_gemma":0.00012409386,"teacher_disagreement_score":0.99595386,"about_ca_system_score_codex":0.00007379493,"about_ca_system_score_gemma":0.000055485292,"threshold_uncertainty_score":0.6013601},"labels":[],"label_agreement":null},{"id":"W4408104941","doi":"10.1016/j.eneco.2025.108332","title":"Distributional forecasting of electricity DART spreads with a covariate-dependent mixture model","year":2025,"lang":"en","type":"article","venue":"Energy Economics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Covariate; Econometrics; Dart; Electricity; Economics; Computer science; Engineering","score_opus":0.010234075869031644,"score_gpt":0.18159499005389318,"score_spread":0.17136091418486155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408104941","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":[0.58194053,0.00022487939,0.3784979,0.000031562286,0.00028334573,0.000042145104,0.00011733813,0.00013535406,0.038726956],"genre_scores_gemma":[0.9967019,0.000051459545,0.002597941,0.000038038157,0.00005879419,0.0000125183415,0.000092034636,0.000023238437,0.00042405867],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992377,0.000008513635,0.00027713296,0.00017895311,0.00004761029,0.0002500999],"domain_scores_gemma":[0.99961966,0.00006177216,0.000060564915,0.00017136664,0.00003870433,0.000047929345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009263481,0.0001685172,0.00022744336,0.000093056165,0.00006103007,0.000022705426,0.00014627555,0.00010316904,0.00001310154],"category_scores_gemma":[0.00001334369,0.0001682591,0.00005674134,0.00014018058,0.000027753113,0.00010323689,0.000036176098,0.00012594524,4.4989892e-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":[0.000026719896,0.000016802747,0.0014133168,0.000024252162,0.00010002344,0.0000014843954,0.000023455119,0.9345396,0.0002944149,0.06158143,0.00021285572,0.0017656386],"study_design_scores_gemma":[0.00032495867,0.000020705025,0.000043976095,0.000043064272,0.000023219609,0.0000081033395,0.000006981207,0.97023183,0.02429384,0.0027734987,0.0020637165,0.00016609319],"about_ca_topic_score_codex":0.000060883427,"about_ca_topic_score_gemma":0.00026609743,"teacher_disagreement_score":0.4147614,"about_ca_system_score_codex":0.0001323868,"about_ca_system_score_gemma":0.00008215243,"threshold_uncertainty_score":0.6861407},"labels":[],"label_agreement":null},{"id":"W4408183216","doi":"10.1109/tpec63981.2025.10906930","title":"Hybrid Deep Learning Model for Accurate Short-Term Electricity Price Forecasting","year":2025,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Term (time); Computer science; Electricity price forecasting; Electricity; Artificial intelligence; Deep learning; Electricity price; Machine learning; Econometrics; Economics; Engineering; Electrical engineering","score_opus":0.0238404355888171,"score_gpt":0.24199333757854244,"score_spread":0.21815290198972534,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408183216","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":[0.14232251,0.00021252975,0.79936594,0.0000109626235,0.00020082129,0.00014521969,0.0000023527527,0.0005986459,0.057141025],"genre_scores_gemma":[0.9870045,0.00002971254,0.010059709,0.000052349693,0.00007982609,0.000053284195,0.000021546952,0.000040308078,0.0026587385],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99890536,0.000010465898,0.00028133014,0.00022356713,0.00008520374,0.00049405184],"domain_scores_gemma":[0.9995253,0.00019912,0.000024309962,0.00012917904,0.00006024668,0.00006184738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018971623,0.00020198608,0.00020088263,0.0001380475,0.0001946124,0.00007172434,0.00016467614,0.00006494103,0.000014483077],"category_scores_gemma":[0.0001192056,0.00020205381,0.00009714098,0.00021857634,0.000010420412,0.00018266984,0.000043106633,0.0002492606,0.0000027221838],"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":[0.0000094588595,0.000007569693,0.0006007137,0.00011544525,0.000046253634,0.0000022494362,0.000077346755,0.93327314,0.0022914442,0.00165821,0.00027111563,0.061647065],"study_design_scores_gemma":[0.00017582352,0.000017704044,0.000050975574,0.000051028983,0.000021552962,0.000005618252,0.000008333981,0.9859948,0.011869933,0.00071317225,0.00087327074,0.00021776535],"about_ca_topic_score_codex":0.000004634294,"about_ca_topic_score_gemma":0.00002284752,"teacher_disagreement_score":0.84468204,"about_ca_system_score_codex":0.00007482287,"about_ca_system_score_gemma":0.00002307507,"threshold_uncertainty_score":0.82395154},"labels":[],"label_agreement":null},{"id":"W4408183501","doi":"10.1109/icoris63540.2024.10903813","title":"Enhancing Solar Energy Production Forecasting with Ensemble-based Learning Techniques","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Production (economics); Computer science; Ensemble learning; Solar energy; Energy (signal processing); Artificial intelligence; Engineering; Electrical engineering; Physics","score_opus":0.010206064536216627,"score_gpt":0.19360606176039152,"score_spread":0.18339999722417488,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408183501","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":[0.060440794,0.0007716261,0.89337564,0.00006301544,0.00062193983,0.000083265244,5.494743e-7,0.00598139,0.0386618],"genre_scores_gemma":[0.9668324,0.000023031624,0.031186236,0.000022815686,0.00043245617,0.000036703535,0.000012868321,0.000090828245,0.0013626353],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990642,0.000017851144,0.00019267178,0.0002605312,0.00014861443,0.0003161536],"domain_scores_gemma":[0.99970955,0.0000635801,0.000017354147,0.00011934304,0.000036116002,0.000054036915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020607305,0.00019917355,0.00013602381,0.00018706353,0.00011873105,0.00011983313,0.00006335185,0.00007601052,0.000038462258],"category_scores_gemma":[0.000027356646,0.00016454897,0.000046757243,0.00036544912,0.000018188468,0.00024450148,0.000013406303,0.0002632991,0.0000066545667],"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":[0.00001831826,0.000013226571,0.00023469832,0.00064489176,0.000097227115,0.00010046263,0.00046181245,0.31281522,0.3265273,0.002065414,0.0005029123,0.35651854],"study_design_scores_gemma":[0.00003534423,0.00006546487,0.000001878729,0.00060814444,0.000014647794,0.00005373322,0.000037430833,0.19929974,0.76413846,0.000051141666,0.03547385,0.00022018567],"about_ca_topic_score_codex":0.000040383984,"about_ca_topic_score_gemma":0.00016691114,"teacher_disagreement_score":0.9063916,"about_ca_system_score_codex":0.00006547275,"about_ca_system_score_gemma":0.000032224503,"threshold_uncertainty_score":0.6710112},"labels":[],"label_agreement":null},{"id":"W4408281308","doi":"10.1109/iecon55916.2024.10905676","title":"From C.elegans to Liquid Neural Networks: A Robust Wind Power Multi-Time Scale Prediction Framework","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of Alberta","funders":"","keywords":"Artificial neural network; Scale (ratio); Computer science; Wind power; Power (physics); Artificial intelligence; Engineering; Electrical engineering; Physics","score_opus":0.008999003718968156,"score_gpt":0.20617336885690155,"score_spread":0.19717436513793338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408281308","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":[0.50708103,0.0010510901,0.4791032,0.0001441302,0.004161292,0.00014458125,0.0000620309,0.0024090358,0.005843594],"genre_scores_gemma":[0.98463136,0.000012582833,0.0124743935,0.00016892343,0.0013290576,0.000011329659,0.000046951875,0.0000875117,0.0012378739],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989622,0.000015789283,0.00023206467,0.0002930813,0.0001327554,0.00036410106],"domain_scores_gemma":[0.9994699,0.000111578025,0.00000806909,0.00021978382,0.000018071461,0.00017260356],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007724085,0.00021425237,0.00016476327,0.00009203074,0.000057651716,0.00015522691,0.00013572142,0.00019746277,0.0009519476],"category_scores_gemma":[0.00001813325,0.00019915064,0.00008686426,0.00036768286,0.000014695849,0.0002308357,0.000046557783,0.00037120606,0.00025581173],"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":[0.00001326238,0.000011400644,0.00010831094,0.000010791394,0.0000525838,0.00001619459,0.000923022,0.9901373,0.0016009388,0.00004277915,0.005714163,0.0013692962],"study_design_scores_gemma":[0.00007966645,0.000078404024,0.0002344178,0.00023977994,0.000018435998,0.00000609594,0.000057448262,0.9890332,0.0007541245,0.00001935288,0.00925334,0.0002256932],"about_ca_topic_score_codex":0.00007381059,"about_ca_topic_score_gemma":0.000035960944,"teacher_disagreement_score":0.47755033,"about_ca_system_score_codex":0.00005100285,"about_ca_system_score_gemma":0.0000071390677,"threshold_uncertainty_score":0.9999613},"labels":[],"label_agreement":null},{"id":"W4408442076","doi":"10.1109/gtdla61236.2024.10913837","title":"Voltage Stability in Low-Voltage Distribution Grids Using Machine Learning Based Forecasting and MILP Based Optimization","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Voltage; Computer science; Stability (learning theory); Control theory (sociology); Mathematical optimization; Artificial intelligence; Engineering; Machine learning; Mathematics; Electrical engineering; Control (management)","score_opus":0.018468941359648543,"score_gpt":0.21249822933655188,"score_spread":0.19402928797690333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408442076","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":[0.3520087,0.0003559869,0.64650166,0.000008961459,0.00019378681,0.00008812575,0.00003776759,0.00040740997,0.0003976156],"genre_scores_gemma":[0.9936007,0.000009617316,0.0058415867,0.000014342107,0.00006601592,0.000007776399,0.00039171355,0.000047539903,0.000020668722],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988788,0.000039826184,0.00032878199,0.00028866035,0.00013967683,0.00032426795],"domain_scores_gemma":[0.99953717,0.00021995015,0.000024246336,0.00011260497,0.000027736081,0.000078279314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048523897,0.00021628702,0.00018374981,0.00014275732,0.00010501188,0.00013315778,0.00005581782,0.000107880805,0.00016497278],"category_scores_gemma":[0.00013648965,0.00021546095,0.00005134293,0.00048124153,0.00003080379,0.00028315355,0.000026719068,0.0003369139,0.0000012560184],"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":[0.000008461543,0.000011607754,0.0098888,0.00044120563,0.0000066849434,0.0000191609,0.000070243994,0.9837749,0.003564261,0.000053734864,0.000004298223,0.0021566516],"study_design_scores_gemma":[0.00029532218,0.000023309889,0.00022273559,0.0004147012,0.000013748773,0.0000042449924,0.0000311161,0.9911769,0.0073060077,0.000012679996,0.000263828,0.00023539246],"about_ca_topic_score_codex":0.00021798459,"about_ca_topic_score_gemma":0.00016360711,"teacher_disagreement_score":0.641592,"about_ca_system_score_codex":0.00018960485,"about_ca_system_score_gemma":0.000034844452,"threshold_uncertainty_score":0.87862426},"labels":[],"label_agreement":null},{"id":"W4408488151","doi":"10.5194/egusphere-egu25-9712","title":"Modelling convective cell occurrence in proximity to cold fronts using machine learning","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":"Western University","funders":"","keywords":"Convection; Computer science; Artificial intelligence; Meteorology; Physics","score_opus":0.029332374474186015,"score_gpt":0.2400382435263255,"score_spread":0.21070586905213948,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408488151","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":[0.37367737,0.00088103296,0.59518325,0.000008116065,0.0014125315,0.0005297014,0.00012638731,0.0005204787,0.027661113],"genre_scores_gemma":[0.97214705,0.00006461278,0.02680202,0.000031372536,0.00006784633,0.000043488708,0.0000480574,0.000031565218,0.00076399883],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99843764,0.00005612432,0.00041087152,0.00049757364,0.00016554065,0.00043223097],"domain_scores_gemma":[0.9994242,0.00009241695,0.0000612621,0.0002549815,0.000059473572,0.00010763508],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00023558503,0.00039358213,0.00046539927,0.0003280908,0.000066806475,0.00006920499,0.0002831271,0.00028902854,0.00003682709],"category_scores_gemma":[0.00002856392,0.00044166678,0.00008798038,0.00026912542,0.00001280675,0.00009594069,0.00044867554,0.0014376772,0.000008745105],"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":[0.0000077481,0.000020301739,0.0027068497,0.00040872264,0.000020799673,0.0000070867513,0.000719037,0.99539334,0.00036000786,0.00007981922,0.000025695885,0.00025056812],"study_design_scores_gemma":[0.00018042845,0.000010172592,0.000008760742,0.00080139725,0.000017703413,5.9093685e-7,0.00003624343,0.98740023,0.010309134,0.000085178195,0.0007148823,0.0004352824],"about_ca_topic_score_codex":0.00176374,"about_ca_topic_score_gemma":0.00036055455,"teacher_disagreement_score":0.5984697,"about_ca_system_score_codex":0.00031041112,"about_ca_system_score_gemma":0.00010933393,"threshold_uncertainty_score":0.9998035},"labels":[],"label_agreement":null},{"id":"W4408625128","doi":"10.1002/cjce.25677","title":"Prediction control of <scp> CO <sub>2</sub> </scp> capture in coal‐fired power plants based on <scp>ERIME</scp> ‐optimized <scp>CNN</scp> ‐ <scp>LSTM</scp> ‐multi‐head‐attention","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Natural Science Foundation of Gansu Province; National Natural Science Foundation of China","keywords":"Coal; Power (physics); Chemistry; Computer science; Environmental science; Waste management; Engineering; Physics","score_opus":0.007124458935544781,"score_gpt":0.185918277507065,"score_spread":0.1787938185715202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408625128","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":[0.9769324,0.0036142285,0.013650975,0.00008788892,0.0017830795,0.00055797916,0.0003861036,0.00021761142,0.0027697377],"genre_scores_gemma":[0.9982471,0.00011485798,0.00046445397,0.00033669436,0.0003185082,0.000039762257,0.00011229497,0.00016778686,0.00019855553],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957246,0.00012504685,0.0015414477,0.00045567157,0.00072724716,0.0014259899],"domain_scores_gemma":[0.9952915,0.0024928306,0.0004558105,0.00054290745,0.00026961853,0.00094736955],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010587997,0.00081134844,0.0012100155,0.0010955699,0.00015920177,0.00013594478,0.0008097397,0.00067021133,0.000006332421],"category_scores_gemma":[0.0031589495,0.00077469804,0.00050210825,0.0008641006,0.00015362902,0.00034142088,0.000036669906,0.0019989032,0.0000102906],"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":[0.000009345949,0.000068258734,0.0010699965,0.0004017221,0.00033092027,0.00013552574,0.0006707466,0.662441,0.32498983,0.00005569146,0.0096918065,0.00013516042],"study_design_scores_gemma":[0.006486357,0.00015631379,0.0033442539,0.004668822,0.00029052462,0.00016135097,0.0003465953,0.5169834,0.4607019,0.00003851439,0.006685029,0.00013696199],"about_ca_topic_score_codex":0.0003504635,"about_ca_topic_score_gemma":0.00028570203,"teacher_disagreement_score":0.14545763,"about_ca_system_score_codex":0.00080227124,"about_ca_system_score_gemma":0.00054149644,"threshold_uncertainty_score":0.9994704},"labels":[],"label_agreement":null},{"id":"W4408675662","doi":"10.5194/wes-2025-29","title":"Minimum Open Data Subset for Wind Power Prediction","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wind power; Open data; Power (physics); Predictive power; Computer science; Meteorology; Environmental science; Geography; Physics; Electrical engineering; Engineering; Philosophy; World Wide Web; Epistemology","score_opus":0.05854305630025496,"score_gpt":0.29284596215401426,"score_spread":0.2343029058537593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408675662","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012039091,0.0010378774,0.09944184,0.0002800373,0.015786057,0.0022354173,0.018750485,0.0014770975,0.8489521],"genre_scores_gemma":[0.8001692,0.00048690132,0.07335431,0.0005355448,0.0021592907,0.00043293313,0.047941346,0.00030141347,0.07461902],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99886864,0.000011256814,0.00031280416,0.0004704428,0.00009581288,0.00024104498],"domain_scores_gemma":[0.99859315,0.000088821434,0.000038871873,0.0011800064,0.00004076321,0.000058356967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027820596,0.00024298018,0.0002800228,0.00008679345,0.000053791922,0.00020493985,0.0016985798,0.00029920318,0.0001972374],"category_scores_gemma":[0.000048876605,0.00024251772,0.00005861823,0.00007142154,0.000011826038,0.00020249393,0.0028991792,0.0002988715,0.000007989],"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":[0.00005657323,0.00006139999,0.00082895264,0.0013897717,0.0007537931,0.0000048908705,0.00044646935,0.24152222,0.000213818,0.003245828,0.7394463,0.012030024],"study_design_scores_gemma":[0.00052700774,0.000028215021,0.0002584453,0.00056456187,0.00009890638,0.0000023953849,0.000048354857,0.42615536,0.0007480273,0.0011169317,0.5699932,0.00045858222],"about_ca_topic_score_codex":0.000090578906,"about_ca_topic_score_gemma":0.0001260766,"teacher_disagreement_score":0.78813016,"about_ca_system_score_codex":0.000044254106,"about_ca_system_score_gemma":0.00007996407,"threshold_uncertainty_score":0.98895854},"labels":[],"label_agreement":null},{"id":"W4408712585","doi":"10.1109/itsc58415.2024.10919924","title":"Demand Forecasting and Rebalancing in Shared Bike Systems Using Deep Learning and Evolutionary Computation*","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"McMaster University","funders":"","keywords":"Evolutionary computation; Computer science; Artificial intelligence; Computation; Deep learning; Demand forecasting; Machine learning; Operations research; Engineering; Algorithm","score_opus":0.017077794980095616,"score_gpt":0.22401700664367596,"score_spread":0.20693921166358034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408712585","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":[0.9111894,0.021466866,0.06298152,0.0000073400124,0.0003996503,0.000084615844,0.0000019975903,0.00040654588,0.0034620774],"genre_scores_gemma":[0.9946402,0.00009135077,0.005064512,0.0000032657892,0.000098820696,0.0000040279474,0.000009570198,0.000030853804,0.00005742387],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928683,0.00002481017,0.00022008434,0.00018073597,0.00007859356,0.00020894465],"domain_scores_gemma":[0.99970967,0.0001799881,0.000014697398,0.000028881752,0.0000143007155,0.000052459025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019438256,0.00012422823,0.00013994894,0.00018818818,0.000095444695,0.00016007973,0.000022239139,0.000061396095,0.0000064409896],"category_scores_gemma":[0.000028799404,0.00012434772,0.000014623204,0.00022352797,0.000017248587,0.00025393226,0.000034613327,0.0001810968,0.0000011685821],"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":[0.0000016264174,0.000001492192,0.019434877,0.00051413424,0.000020193787,0.000043312753,0.00075708044,0.9697627,0.00097465323,0.0002826437,0.000019964275,0.008187351],"study_design_scores_gemma":[0.00011720612,0.0000099441395,0.00132833,0.0007539922,0.000008735959,0.00021654542,0.0003835924,0.99659085,0.000019524601,0.000084662635,0.00033826582,0.00014833198],"about_ca_topic_score_codex":0.00009101034,"about_ca_topic_score_gemma":0.000038193073,"teacher_disagreement_score":0.08345079,"about_ca_system_score_codex":0.000061964296,"about_ca_system_score_gemma":0.000008617327,"threshold_uncertainty_score":0.5070753},"labels":[],"label_agreement":null},{"id":"W4408777410","doi":"10.3390/s25072026","title":"Clustering and Interpretability of Residential Electricity Demand Profiles","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":17,"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":"Interpretability; Cluster analysis; Electricity; Electricity demand; Demand response; Computer science; Data mining; Engineering; Artificial intelligence; Electricity generation; Electrical engineering; Power (physics)","score_opus":0.005032771326364237,"score_gpt":0.21252093812446013,"score_spread":0.2074881667980959,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408777410","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":[0.98728925,0.00035175108,0.0039901356,0.000015665775,0.00017114649,0.000049397928,0.0000025782654,0.000083515115,0.008046565],"genre_scores_gemma":[0.9993511,0.00003363488,0.000466089,0.000006599154,0.000019008834,0.0000016654373,9.1376984e-7,0.000006073284,0.00011495271],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995907,0.000019785139,0.00014697215,0.000089970796,0.00004238541,0.00011017584],"domain_scores_gemma":[0.9997987,0.00006088843,0.000014361291,0.00008817636,0.000013980006,0.000023866149],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010219894,0.00006737318,0.00011330014,0.00006797693,0.000026545356,0.000010136713,0.000042045864,0.000041181327,0.000008825409],"category_scores_gemma":[0.00006445612,0.00006645342,0.000024196399,0.000116383104,0.000030756353,0.000031133062,0.000030390747,0.00007488168,4.6207268e-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":[0.00025668886,0.000072083036,0.14015405,0.0046724956,0.00049502833,0.000024418014,0.005301435,0.52776855,0.2420568,0.0035011864,0.0012043713,0.07449287],"study_design_scores_gemma":[0.0003754061,0.000036437297,0.021531409,0.00025792245,0.00004015906,0.000008636001,0.00013272789,0.6226916,0.35321414,0.00081150787,0.00067192834,0.00022815286],"about_ca_topic_score_codex":0.000039547383,"about_ca_topic_score_gemma":0.00007298001,"teacher_disagreement_score":0.118622646,"about_ca_system_score_codex":0.000015736292,"about_ca_system_score_gemma":0.0000074070713,"threshold_uncertainty_score":0.27098918},"labels":[],"label_agreement":null},{"id":"W4408860324","doi":"10.1109/concapan63470.2024.10933874","title":"Leveraging Temporal Patterns in Electricity Price Forecasting with Cluster-Based Feature Engineering and Temporal Fusion Transformers","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Electricity; Transformer; Cluster (spacecraft); Artificial intelligence; Fusion; Feature engineering; Data mining; Pattern recognition (psychology); Engineering; Electrical engineering; Voltage; Deep learning","score_opus":0.009616488594970224,"score_gpt":0.18881082842297306,"score_spread":0.17919433982800284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408860324","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":[0.850067,0.0005002956,0.14688481,0.00010687596,0.00018167276,0.00015860015,0.000004898039,0.00060822774,0.0014875826],"genre_scores_gemma":[0.99528927,0.000018604791,0.004380898,0.000039927767,0.000083500425,0.000016268274,0.000028553943,0.000073115014,0.000069879505],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885464,0.000012103066,0.00021704938,0.0002860921,0.0001674934,0.0004626409],"domain_scores_gemma":[0.9996898,0.00010421582,0.000014502585,0.00008058371,0.000013038877,0.0000978619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002017092,0.00028650922,0.00021204879,0.0003338792,0.0000535214,0.00012422995,0.000071218594,0.00011079579,0.000016700376],"category_scores_gemma":[0.000007874866,0.00023870598,0.000044742425,0.0005459158,0.000010546966,0.00028053412,0.000013695433,0.0004681678,8.9555897e-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":[0.00009045902,0.000039095037,0.124525,0.0032707267,0.00011932326,0.00048631112,0.0030049465,0.8024193,0.007425071,0.00012354093,0.0002307575,0.058265485],"study_design_scores_gemma":[0.00046542584,0.000069626905,0.0012837597,0.0008568366,0.000012435202,0.00006697367,0.00008631056,0.99173313,0.0035839847,0.0000044013927,0.0014810275,0.0003560819],"about_ca_topic_score_codex":0.0001699533,"about_ca_topic_score_gemma":0.000319505,"teacher_disagreement_score":0.18931386,"about_ca_system_score_codex":0.000102316946,"about_ca_system_score_gemma":0.000031918342,"threshold_uncertainty_score":0.9734148},"labels":[],"label_agreement":null},{"id":"W4408879994","doi":"10.1016/j.eng.2025.03.021","title":"Artificial Intelligence for Power Systems with Renewable Energy","year":2025,"lang":"en","type":"article","venue":"Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Project 211; Higher Education Discipline Innovation Project; State Key Laboratory of Industrial Control Technology; National Natural Science Foundation of China","keywords":"Renewable energy; Power (physics); Energy (signal processing); Computer science; Electrical engineering; Engineering; Physics","score_opus":0.008549911821807218,"score_gpt":0.19211503482924217,"score_spread":0.18356512300743494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408879994","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":[0.002798284,0.00097228907,0.98542047,0.000011027677,0.0012596649,0.000072336996,0.0000048575575,0.00049767375,0.00896339],"genre_scores_gemma":[0.99516755,0.000012607776,0.0038756065,0.000010708417,0.000086095184,0.000078797304,0.0000071096842,0.000040222396,0.0007212966],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931043,0.0000027615179,0.00019779343,0.0001383016,0.00006779179,0.00028293478],"domain_scores_gemma":[0.9996761,0.00008632846,0.000012009005,0.00015373902,0.000026983176,0.000044802713],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007817748,0.00015413605,0.00015261106,0.00013354665,0.000043860724,0.000056392208,0.00011045388,0.000063454565,0.000007379297],"category_scores_gemma":[0.000019669304,0.00014785372,0.00003647883,0.00026244164,0.000007853402,0.00007733134,0.00001328672,0.00007084494,0.0000018669579],"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":[0.0000056477966,0.000003874507,0.000010462067,0.00009366847,0.000042971806,0.0000023459934,0.000040267096,0.9715462,0.0020351023,0.024575906,0.00042953077,0.001214037],"study_design_scores_gemma":[0.000038977083,0.000029228491,0.0000063147336,0.00024406688,0.000012416229,0.0000045734396,0.00004400194,0.9066441,0.036450524,0.00019822957,0.056114487,0.00021309644],"about_ca_topic_score_codex":0.000057653626,"about_ca_topic_score_gemma":0.000027344115,"teacher_disagreement_score":0.9923693,"about_ca_system_score_codex":0.000047187863,"about_ca_system_score_gemma":0.000013745598,"threshold_uncertainty_score":0.60292995},"labels":[],"label_agreement":null},{"id":"W4408997779","doi":"10.3390/atmos16040394","title":"Deep Learning for Atmospheric Modeling: A Proof of Concept Using a Fourier Neural Operator on WRF Data to Accelerate Transient Wind Forecasting at Multiple Altitudes","year":2025,"lang":"en","type":"article","venue":"Atmosphere","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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 Guelph; Lakes Environmental (Canada)","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Weather Research and Forecasting Model; Transient (computer programming); Fourier transform; Meteorology; Artificial neural network; Proof of concept; Operator (biology); Environmental science; Computer science; Artificial intelligence; Mathematics; Physics; Mathematical analysis","score_opus":0.06532428047538891,"score_gpt":0.26998087604440185,"score_spread":0.20465659556901294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408997779","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":[0.8144857,0.0010025973,0.18285136,0.000031122923,0.0003985265,0.0005759103,0.000022459379,0.0001366324,0.00049565954],"genre_scores_gemma":[0.94837976,0.0000057589045,0.05098811,0.00010529076,0.00012094944,0.000035495617,0.00006078396,0.000089253845,0.00021458938],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99817544,0.000038828333,0.0005236866,0.00051231566,0.00019648278,0.00055325334],"domain_scores_gemma":[0.9989993,0.00019020785,0.00007075574,0.0004893312,0.00011995993,0.00013045488],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001936889,0.00034961346,0.00043608292,0.0000067893907,0.00028392076,0.00006920868,0.00044751947,0.00013071497,0.000058692513],"category_scores_gemma":[0.00019709,0.0003457909,0.00010718318,0.0004065281,0.000031488576,0.00025953408,0.00019568462,0.0002411492,0.0000017332704],"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":[0.000110546265,0.000031716525,0.0011447333,0.0002001174,0.00011889146,0.0000050171966,0.00087770075,0.96869856,0.0022975514,0.00002373847,0.0001465331,0.0263449],"study_design_scores_gemma":[0.00096370257,0.0001464433,0.000032693497,0.00042369994,0.00006550083,0.0000044796343,0.00025729786,0.9897781,0.004879876,0.0000072208495,0.0031123443,0.00032862625],"about_ca_topic_score_codex":0.00006323224,"about_ca_topic_score_gemma":0.00031605037,"teacher_disagreement_score":0.13389404,"about_ca_system_score_codex":0.00013322021,"about_ca_system_score_gemma":0.0000522881,"threshold_uncertainty_score":0.9998994},"labels":[],"label_agreement":null},{"id":"W4409085309","doi":"10.18280/mmep.120302","title":"Comparative Analysis of Activation Functions in Recurrent Neural Network: An Application to Indonesian Inflation Forecasting","year":2025,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Indonesian; Inflation (cosmology); Artificial neural network; Computer science; Econometrics; Economics; Artificial intelligence; Keynesian economics; Philosophy; Physics; Linguistics","score_opus":0.0332272955334094,"score_gpt":0.24313671607919632,"score_spread":0.20990942054578693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409085309","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":[0.41963387,0.000025396073,0.5798314,0.000008186943,0.000027745808,0.000107146676,0.0000013469203,0.00006934406,0.00029556206],"genre_scores_gemma":[0.9875541,0.000003056562,0.012297333,0.0000034511836,0.000021084685,0.0000681885,0.000034882592,0.000010107272,0.00000781603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992052,0.000010987144,0.00036886687,0.00015310742,0.00007776183,0.00018410877],"domain_scores_gemma":[0.99965835,0.00011085545,0.000032386783,0.000114607246,0.000030997147,0.00005280169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001997797,0.00012914602,0.00028792335,0.00041163346,0.00003858037,0.000025158473,0.000051966817,0.00006312744,0.0000015379096],"category_scores_gemma":[0.000011407196,0.00013401364,0.000034865516,0.0011384989,0.00000790899,0.00012443442,0.000016167196,0.00013300031,5.19565e-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":[0.0000039336546,0.000018438446,0.0004866327,0.00018659992,0.000069560534,5.6213537e-8,0.0008725043,0.99186885,0.00044162563,0.0039237635,0.0000019000921,0.0021261077],"study_design_scores_gemma":[0.00008371924,0.000015858517,0.00071129925,0.00027853006,0.000071510985,2.9565342e-7,0.000039312134,0.99779266,0.00015845065,0.0007063058,0.000028951525,0.00011309228],"about_ca_topic_score_codex":0.0000116043475,"about_ca_topic_score_gemma":0.000015898293,"teacher_disagreement_score":0.5679202,"about_ca_system_score_codex":0.000035356483,"about_ca_system_score_gemma":0.0000040535633,"threshold_uncertainty_score":0.54649174},"labels":[],"label_agreement":null},{"id":"W4409102667","doi":"10.1109/saupec65723.2025.10944326","title":"Electrical Grid Stability Prediction with In-Bag and Out-of-Bag Estimates on Imbalanced Dataset","year":2025,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Grid; Stability (learning theory); Computer science; Machine learning; Data mining; Artificial intelligence; Mathematics","score_opus":0.009467740135269182,"score_gpt":0.22333418172718952,"score_spread":0.21386644159192034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409102667","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":[0.9885257,0.00015809419,0.0037858742,0.00003146267,0.00025658277,0.00010360264,0.00030070296,0.00016269395,0.00667532],"genre_scores_gemma":[0.99840295,0.000019587307,0.0012894461,0.000016950575,0.000017386426,0.0000071847867,0.00022851901,0.000006079084,0.000011890531],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953866,0.000008171983,0.00013783862,0.00012759859,0.000061005314,0.00012673563],"domain_scores_gemma":[0.9997241,0.00010589164,0.000010622773,0.00012534458,0.000009396321,0.000024632785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008263895,0.00008468872,0.000121507146,0.00006339528,0.000018895478,0.000010730062,0.000042153362,0.000039142145,0.000013563445],"category_scores_gemma":[0.000032898635,0.000066105364,0.000007902089,0.0001511592,0.000021497177,0.000059455135,0.00001208485,0.000103645965,8.270354e-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":[0.00055105495,0.0003418751,0.69673157,0.0009003271,0.00020962038,0.00001848025,0.00067794323,0.2212014,0.021077938,0.0067016487,0.011064991,0.04052317],"study_design_scores_gemma":[0.0017167483,0.0004863896,0.1150194,0.00036740385,0.000041371746,0.0000035769303,0.000053695276,0.72759986,0.15005806,0.00042127047,0.0038844233,0.00034781254],"about_ca_topic_score_codex":0.000035313486,"about_ca_topic_score_gemma":0.00013693374,"teacher_disagreement_score":0.5817121,"about_ca_system_score_codex":0.000027108947,"about_ca_system_score_gemma":0.000010903521,"threshold_uncertainty_score":0.26956987},"labels":[],"label_agreement":null},{"id":"W4409195059","doi":"10.5220/0013116800003953","title":"Topological Attention and Deep Learning Integration for Electricity Consumption Forecasting","year":2025,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Electricity; Consumption (sociology); Computer science; Deep learning; Artificial intelligence; Engineering; Electrical engineering; Sociology; Social science","score_opus":0.02212567575310763,"score_gpt":0.24390799268735566,"score_spread":0.22178231693424805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409195059","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":[0.49482363,0.00023163317,0.49900803,0.00001661002,0.00012580794,0.0000690912,2.7212656e-7,0.00018906302,0.0055358415],"genre_scores_gemma":[0.99469525,0.0000748934,0.004689382,0.000021571588,0.000034985263,0.00002009825,0.000016820273,0.000005997209,0.00044098016],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999599,0.000012351932,0.00012509344,0.0000997843,0.000030067502,0.00013369828],"domain_scores_gemma":[0.99978054,0.0001322845,0.000014912741,0.000029550534,0.000024600107,0.0000181408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012799505,0.00007317987,0.000079890604,0.000069828595,0.00011350816,0.00003386692,0.0000234872,0.00006694816,0.000014386939],"category_scores_gemma":[0.000101741345,0.00006541102,0.00002684069,0.000083732666,0.000013546452,0.00007900625,0.000009636428,0.00010408508,0.0000010969881],"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":[0.000033345492,0.000015798954,0.036417272,0.00022186794,0.00005242057,9.762068e-7,0.00015805692,0.018047526,0.0313365,0.0678659,0.00013737241,0.84571296],"study_design_scores_gemma":[0.00022824148,0.00004091577,0.004236,0.000046764115,0.000015170415,0.000004179681,0.000060340953,0.9881394,0.0049652667,0.0013682074,0.00080375304,0.00009179364],"about_ca_topic_score_codex":0.0000055444584,"about_ca_topic_score_gemma":0.00003784651,"teacher_disagreement_score":0.9700918,"about_ca_system_score_codex":0.000028070157,"about_ca_system_score_gemma":0.0000021733952,"threshold_uncertainty_score":0.2667384},"labels":[],"label_agreement":null},{"id":"W4409250632","doi":"10.1007/978-981-97-9916-9_36","title":"Short-Term Electricity Price Forecasting of Ontario Market by LASSO-LSTM Model of Deep Learning","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Lasso (programming language); Term (time); Deep learning; Electricity price forecasting; Artificial intelligence; Electricity market; Econometrics; Computer science; Economics; Electricity; Engineering; World Wide Web; Physics","score_opus":0.007670693235546647,"score_gpt":0.1808645424238522,"score_spread":0.17319384918830555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409250632","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":[0.008698195,0.007242648,0.8285452,0.00000637625,0.000351169,0.00044976038,0.000032618565,0.00046837964,0.15420566],"genre_scores_gemma":[0.9858146,0.00032806402,0.0071758367,0.000012446508,0.00011485718,0.000021438,0.00007913363,0.00022949363,0.006224158],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99717534,0.00001615843,0.0010457452,0.0005037153,0.00042712776,0.00083193043],"domain_scores_gemma":[0.9984242,0.0008624832,0.00017669146,0.0003158388,0.00010008489,0.000120714314],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00026713344,0.0007770998,0.0011944724,0.00076956546,0.000043616332,0.000022604028,0.00040877168,0.0009220798,0.00006201678],"category_scores_gemma":[0.0004251674,0.00087166496,0.00030670795,0.00046107057,0.000029329176,0.00009232475,0.00009055707,0.002722731,5.384123e-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":[0.000028546247,0.000018270382,0.00026923465,0.0005495704,0.00017976157,0.000011461312,0.00019946524,0.94263095,0.005949419,0.00061907887,0.00003557648,0.049508676],"study_design_scores_gemma":[0.00022962631,0.00009259096,0.000028620818,0.0007475914,0.00009392956,0.000011731608,1.856429e-7,0.9836021,0.012657004,0.00051167124,0.0013378215,0.00068713643],"about_ca_topic_score_codex":0.00016266105,"about_ca_topic_score_gemma":0.0006680228,"teacher_disagreement_score":0.9771164,"about_ca_system_score_codex":0.001073013,"about_ca_system_score_gemma":0.00015148064,"threshold_uncertainty_score":0.999578},"labels":[],"label_agreement":null},{"id":"W4409254865","doi":"10.1016/j.egyr.2025.03.060","title":"Dynamic stacking ensemble hybrid model for enhanced short-term photovoltaic power forecasting with self-organizing maps and advanced deep learning","year":2025,"lang":"en","type":"article","venue":"Energy Reports","topic":"Energy Load and Power Forecasting","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":"Ministry of Education and Child Care","funders":"","keywords":"Photovoltaic system; Term (time); Stacking; Computer science; Artificial intelligence; Power (physics); Machine learning; Engineering; Electrical engineering; Physics","score_opus":0.005829021141916796,"score_gpt":0.20202590330616244,"score_spread":0.19619688216424563,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409254865","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":[0.6398444,0.0007531703,0.35339376,0.0000025169686,0.00030008797,0.00012947495,0.0000019457348,0.000599304,0.0049752938],"genre_scores_gemma":[0.97466755,0.00009636416,0.023993904,0.000029673625,0.000030806164,0.00010146121,0.00006546614,0.00013444056,0.00088034465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99805915,0.000016497788,0.00054199435,0.00056723855,0.00018153068,0.0006335677],"domain_scores_gemma":[0.99921,0.000120445446,0.00013801729,0.00030106114,0.00011544582,0.000115042254],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021298074,0.00040008756,0.00041359078,0.00020126216,0.00033226286,0.000102562284,0.00009279325,0.00010536069,0.00000469374],"category_scores_gemma":[0.00006769551,0.00040505506,0.00007931811,0.00026207342,0.000029102303,0.00029895836,0.00007730844,0.00025166266,1.719281e-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":[0.00003116936,0.000017397599,0.0012878533,0.00018130975,0.00015472592,0.00016081383,0.00066292356,0.8607666,0.11086738,0.00014334182,0.000015894391,0.025710594],"study_design_scores_gemma":[0.0003550016,0.000075490454,0.000068504254,0.00041677215,0.00006346806,0.0002934028,0.000110757486,0.90147877,0.095169514,0.00047436386,0.0010055194,0.00048844394],"about_ca_topic_score_codex":0.000009215572,"about_ca_topic_score_gemma":0.00017460214,"teacher_disagreement_score":0.3348231,"about_ca_system_score_codex":0.00014662898,"about_ca_system_score_gemma":0.00005731936,"threshold_uncertainty_score":0.99984014},"labels":[],"label_agreement":null},{"id":"W4409262385","doi":"10.1109/bdcat63179.2024.00016","title":"Deep Learning Models in Simulating and Analyzing Smart Grid Stability and Resilience","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Toronto Metropolitan University","funders":"","keywords":"Resilience (materials science); Computer science; Stability (learning theory); Grid; Deep learning; Smart grid; Artificial intelligence; Distributed computing; Machine learning; Geology; Engineering; Materials science; Electrical engineering","score_opus":0.017587671799189707,"score_gpt":0.2243625184708294,"score_spread":0.20677484667163967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409262385","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":[0.95876354,0.003189862,0.028025644,0.000010468781,0.00009592339,0.000032441745,3.8760845e-7,0.00026097742,0.009620751],"genre_scores_gemma":[0.9982162,0.00012791056,0.0015687277,0.000004314062,0.000032631782,0.000002032739,0.0000013009569,0.000014807217,0.000032089774],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993938,0.000019825868,0.00015880798,0.00018716119,0.00005790483,0.00018249346],"domain_scores_gemma":[0.9996676,0.00021347779,0.0000058890764,0.00005879541,0.0000068606078,0.000047369413],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027843722,0.00009387569,0.00010414163,0.00008281994,0.000048993697,0.00008323636,0.000029071722,0.00003949541,0.000018182078],"category_scores_gemma":[0.00004192517,0.00008850796,0.000013384238,0.0001883939,0.00002530712,0.00029170112,0.000038526352,0.00021523274,8.0619145e-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":[0.0000011760961,0.0000012476675,0.025058588,0.00012690085,0.000005256037,0.0000072029584,0.0012084851,0.94760996,0.0009257288,0.0007675919,0.0000012005892,0.02428669],"study_design_scores_gemma":[0.00005060187,0.000008391428,0.0014359014,0.000108008724,0.000003362764,0.0000038971166,0.00022531951,0.9970864,0.00041140858,0.00046573288,0.0000945566,0.000106396925],"about_ca_topic_score_codex":0.00008144067,"about_ca_topic_score_gemma":0.00026782622,"teacher_disagreement_score":0.04947649,"about_ca_system_score_codex":0.000023017741,"about_ca_system_score_gemma":0.0000041303138,"threshold_uncertainty_score":0.36092496},"labels":[],"label_agreement":null},{"id":"W4409351521","doi":"10.1016/j.jobe.2025.112445","title":"Robust model predictive control of battery energy storage with neural network forecasting for peak shaving in university campus","year":2025,"lang":"en","type":"article","venue":"Journal of Building Engineering","topic":"Energy Load and Power Forecasting","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":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; École de technologie supérieure","keywords":"Battery (electricity); Artificial neural network; Model predictive control; Automotive engineering; Control (management); Peaking power plant; Computer science; Energy storage; Battery storage; Engineering; Reliability engineering; Machine learning; Artificial intelligence; Electrical engineering; Renewable energy","score_opus":0.009036293349025708,"score_gpt":0.171294861402918,"score_spread":0.1622585680538923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409351521","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":[0.2862835,0.00038131475,0.7127909,0.000008929804,0.00032275287,0.000047508653,0.0000060716566,0.000037502705,0.00012155342],"genre_scores_gemma":[0.97628725,0.000016824946,0.023467027,0.0000093777335,0.00016232868,0.0000016606497,0.000001091081,0.000033912645,0.000020508782],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99902594,0.000013435011,0.00039373853,0.000112776375,0.0001262747,0.00032784275],"domain_scores_gemma":[0.99930185,0.00028937482,0.00015704607,0.000090138274,0.000100259196,0.00006132153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030040307,0.00018230439,0.0003907387,0.00038874807,0.000043778466,0.00001786811,0.00018486883,0.000080684906,8.029506e-7],"category_scores_gemma":[0.00005780886,0.00018365748,0.000108971224,0.0003542809,0.000014521841,0.000276741,0.000026659645,0.0002846686,7.1517596e-9],"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":[0.000101847516,0.00000865416,0.0011883111,0.00014442285,0.00012052367,0.00003257622,0.000107400425,0.9939645,0.003026805,0.0004498688,0.000059122955,0.00079596875],"study_design_scores_gemma":[0.0011024268,0.000075355354,0.00025202028,0.0011455868,0.000067042885,0.000025561028,0.000054679174,0.9964038,0.0005875192,0.000038275462,0.000099757686,0.00014798545],"about_ca_topic_score_codex":0.000008508245,"about_ca_topic_score_gemma":0.000011606625,"teacher_disagreement_score":0.69000375,"about_ca_system_score_codex":0.00019215356,"about_ca_system_score_gemma":0.00004587046,"threshold_uncertainty_score":0.74893343},"labels":[],"label_agreement":null},{"id":"W4409426296","doi":"10.1109/oajpe.2025.3559336","title":"A Novel Multi-Task Learning-Based Approach to Multi-Energy System Load Forecasting","year":2025,"lang":"en","type":"article","venue":"IEEE Open Access Journal of Power and Energy","topic":"Energy Load and Power Forecasting","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":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Task (project management); Computer science; Energy (signal processing); Artificial intelligence; Machine learning; Engineering; Statistics; Mathematics; Systems engineering","score_opus":0.04639214120867698,"score_gpt":0.2943582876460459,"score_spread":0.2479661464373689,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409426296","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":[0.012109905,0.0011917162,0.9509869,0.000033202108,0.0017574626,0.00006312899,0.000007951808,0.00008948166,0.03376024],"genre_scores_gemma":[0.9846133,0.00004119099,0.01347654,0.0002465817,0.000143059,0.000023129998,0.0000043121877,0.00006004073,0.0013918723],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981015,0.00006708062,0.00076407666,0.0003058135,0.0002926813,0.00046889868],"domain_scores_gemma":[0.9988467,0.00011959412,0.00023828853,0.00021699393,0.0002858846,0.0002925397],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055348896,0.0003584163,0.0006088846,0.00044652555,0.00022948036,0.00092390546,0.0012412597,0.00015504647,0.000007667162],"category_scores_gemma":[0.000086346154,0.0003173735,0.00014875113,0.00062399864,0.000036709902,0.000883482,0.0002904087,0.0003476132,8.93041e-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":[0.00009672946,0.00014676267,0.0003543767,0.00015601802,0.00020531482,0.000037375572,0.00025464364,0.98248404,0.0037023446,0.0010966876,0.0011808436,0.0102848755],"study_design_scores_gemma":[0.0034691326,0.00013099665,0.00028982628,0.0012980624,0.000089228764,0.00021535881,0.00039631402,0.9360655,0.007351664,0.0000110160245,0.05009108,0.00059184193],"about_ca_topic_score_codex":0.0007849454,"about_ca_topic_score_gemma":0.00021968792,"teacher_disagreement_score":0.97250336,"about_ca_system_score_codex":0.00020106453,"about_ca_system_score_gemma":0.00016838899,"threshold_uncertainty_score":0.9999278},"labels":[],"label_agreement":null},{"id":"W4409482842","doi":"10.1016/j.engappai.2025.110781","title":"Transformer-based deep probabilistic network for load forecasting","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":24,"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":"Computer science; Transformer; Probabilistic logic; Artificial intelligence; Machine learning; Data mining; Electrical engineering; Voltage","score_opus":0.02134293455820458,"score_gpt":0.24525083668724468,"score_spread":0.2239079021290401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409482842","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":[0.0027473278,0.0004778099,0.9943326,0.000030821913,0.00025771165,0.00054628233,0.000019084904,0.0003219955,0.0012663539],"genre_scores_gemma":[0.9117157,0.000009797134,0.08729535,0.0000096773265,0.00014656811,0.0007457993,0.000023606559,0.00003293617,0.000020595813],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998896,0.0000053528615,0.0004894036,0.00019008263,0.000101290825,0.00031786473],"domain_scores_gemma":[0.9990908,0.00044782742,0.00003798015,0.00024493207,0.0001274899,0.000050990628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028361718,0.00017216596,0.00019787104,0.000116551375,0.00011132862,0.000024678344,0.00023422424,0.00008747453,0.000008769895],"category_scores_gemma":[0.00012893628,0.00019783524,0.00009969997,0.0006828487,0.00004050155,0.000052514984,0.000007708257,0.00015166828,0.000003858674],"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":[0.000006758626,0.000018633718,0.000010432765,0.00030365927,0.000021221314,9.295483e-8,0.00005669422,0.8604899,0.00211322,0.04594524,0.000032197564,0.09100196],"study_design_scores_gemma":[0.00002469142,0.0000191379,0.0000051941006,0.000137515,0.000026502985,5.0059225e-7,0.000028380719,0.9369756,0.049807776,0.0062736385,0.0065397215,0.0001613339],"about_ca_topic_score_codex":0.000009329072,"about_ca_topic_score_gemma":0.000047121677,"teacher_disagreement_score":0.9089683,"about_ca_system_score_codex":0.00007864837,"about_ca_system_score_gemma":0.000052006577,"threshold_uncertainty_score":0.8067487},"labels":[],"label_agreement":null},{"id":"W4409537305","doi":"10.1002/ese3.2091","title":"Forecasting Green Energy Production in Latin American Countries and Canada via Temporal Fusion Transformer","year":2025,"lang":"en","type":"article","venue":"Energy Science & Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"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":"Teknologian Tutkimuskeskus VTT; Taif University","keywords":"Latin Americans; Fusion; Transformer; Computer science; Artificial intelligence; Engineering; Electrical engineering; Political science; Voltage","score_opus":0.004583361242453466,"score_gpt":0.172884013547883,"score_spread":0.16830065230542954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409537305","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":[0.97326267,0.00045681334,0.023338292,0.00017774382,0.0009862585,0.00004322609,0.0000029302832,0.00018870122,0.0015433831],"genre_scores_gemma":[0.9985737,0.000063142266,0.0009449829,0.00007488849,0.000088033674,0.000017647495,0.0000049023233,0.000021999986,0.0002106956],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864095,0.0000073905935,0.00027771702,0.00031200418,0.00025856655,0.00050335785],"domain_scores_gemma":[0.9996485,0.00004385182,0.00003030216,0.00014287868,0.000037564318,0.000096887794],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021594382,0.0002141328,0.00020784252,0.00040896953,0.00015174037,0.000051084655,0.00017511976,0.000038425067,0.000003558026],"category_scores_gemma":[0.000034900404,0.00022357206,0.000020281133,0.0011284964,0.00012551858,0.00034943828,0.00003125034,0.00013110465,8.8550685e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000074220657,0.0000065929703,0.008594078,0.000031015006,0.000013546209,0.000016682197,0.0003153019,0.90165484,0.027723968,0.01928512,0.00006667408,0.042284757],"study_design_scores_gemma":[0.00023580185,0.00003127157,0.005902384,0.00028037265,0.000009862797,0.000019560568,0.00013477555,0.8014788,0.09304875,0.000078824094,0.09822442,0.000555178],"about_ca_topic_score_codex":0.45256478,"about_ca_topic_score_gemma":0.58784276,"teacher_disagreement_score":0.135278,"about_ca_system_score_codex":0.0002688917,"about_ca_system_score_gemma":0.00017648196,"threshold_uncertainty_score":0.9117004},"labels":[],"label_agreement":null},{"id":"W4409583371","doi":"10.61091/jcmcc127a-006","title":"Research on Data-Driven Demand Forecasting and Service Optimisation Model for Electricity Users’ Behaviours","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Electricity demand; Electricity; Demand forecasting; Service (business); Service model; Computer science; Environmental economics; Operations research; Business; Economics; Electricity generation; Marketing; Engineering; Power (physics); Electrical engineering","score_opus":0.07095789180371882,"score_gpt":0.31576306585009917,"score_spread":0.24480517404638036,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409583371","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":[0.9365565,0.00030051486,0.057267655,0.0001228835,0.0045868778,0.00033152,0.000010745686,0.00005725023,0.0007660153],"genre_scores_gemma":[0.9877063,0.000045588247,0.011643088,0.000026480428,0.00052430294,0.0000032342352,0.0000078417725,0.000037399484,0.00000577626],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813616,0.00006829314,0.0007528473,0.00022899896,0.0004260688,0.00038761128],"domain_scores_gemma":[0.99729645,0.0014718499,0.0002673901,0.00026025777,0.00057629746,0.00012775858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024927836,0.00023323993,0.00050522154,0.00035877142,0.0004144411,0.00024209294,0.00042561654,0.00017872962,5.3157873e-7],"category_scores_gemma":[0.00039320748,0.00022522443,0.000059676495,0.0004418282,0.000043110525,0.00023960849,0.00023270446,0.0006055032,1.5333258e-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":[0.00025642788,0.00040611668,0.00024173416,0.0012501413,0.00031920173,0.000011553915,0.0025365516,0.25710076,0.0007877871,0.7283281,0.001491561,0.0072700824],"study_design_scores_gemma":[0.0020393955,0.000242001,0.000018464985,0.0005765272,0.000088012814,0.000016670214,0.00017382484,0.86902606,0.00044187641,0.1271243,0.00008336826,0.00016949729],"about_ca_topic_score_codex":0.000007564634,"about_ca_topic_score_gemma":0.0000025414863,"teacher_disagreement_score":0.6119253,"about_ca_system_score_codex":0.00010203356,"about_ca_system_score_gemma":0.000107510605,"threshold_uncertainty_score":0.91843855},"labels":[],"label_agreement":null},{"id":"W4409605110","doi":"10.61091/jcmcc127b-282","title":"Dynamic modeling and quantitative computational assessment of grid power supply capacity at multi-temporal and spatial scales","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Temporal scales; Grid; Computer science; Power grid; Environmental science; Power (physics); Geography; Physics","score_opus":0.014629369457949392,"score_gpt":0.267342607875339,"score_spread":0.2527132384173896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409605110","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":[0.84918004,0.00058062276,0.14500682,0.000023701788,0.004877927,0.0001186151,0.00000942922,0.00002516857,0.00017768845],"genre_scores_gemma":[0.9538036,0.00005253949,0.046017293,0.000004775036,0.00009248005,9.612447e-7,0.0000037247908,0.00002290587,0.0000017335159],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826777,0.00006496698,0.00095013675,0.00016270459,0.00033790254,0.00021651601],"domain_scores_gemma":[0.99846995,0.0006240516,0.00038861725,0.00009329777,0.0003105945,0.00011349064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007589163,0.00025623734,0.00067843223,0.00021520675,0.00020426598,0.00009281475,0.00013046371,0.00012873783,0.0000019586976],"category_scores_gemma":[0.00013377276,0.0002445616,0.00008821648,0.00015141717,0.000120779645,0.00014597176,0.00017580732,0.00034941087,9.01538e-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":[0.00017262835,0.000655476,0.013222299,0.0019721773,0.00079396385,0.00002496553,0.0046323133,0.13013856,0.0031371424,0.8433145,0.00006201413,0.0018739062],"study_design_scores_gemma":[0.002843872,0.0002637072,0.001678785,0.0006439027,0.000065380555,0.00003761703,0.00027070375,0.90410477,0.00012726558,0.08974243,0.000019073417,0.0002024787],"about_ca_topic_score_codex":0.00002977885,"about_ca_topic_score_gemma":0.000008742008,"teacher_disagreement_score":0.77396625,"about_ca_system_score_codex":0.00009540083,"about_ca_system_score_gemma":0.000061010036,"threshold_uncertainty_score":0.9972933},"labels":[],"label_agreement":null},{"id":"W4409705998","doi":"10.7717/peerj-cs.2819","title":"REDf: a deep learning model for short-term load forecasting to facilitate renewable integration and attaining the SDGs 7, 9, and 13","year":2025,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Energy Load and Power Forecasting","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":"McMaster University","funders":"","keywords":"Renewable energy; Computer science; Environmental economics; Demand response; Smart grid; Electricity; Engineering; Electrical engineering; Economics","score_opus":0.04327958082855131,"score_gpt":0.24663788143582405,"score_spread":0.20335830060727272,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409705998","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":[0.28129423,0.00015728538,0.7172428,0.00010648833,0.00018774685,0.00011587987,0.0000011588506,0.00009351897,0.00080091576],"genre_scores_gemma":[0.9390612,0.0000104964765,0.060487967,0.00008182796,0.000044153847,0.000026307609,0.0000015681971,0.000008566386,0.00027788183],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990045,0.000011537863,0.00017909851,0.00031508843,0.00016140916,0.00032837832],"domain_scores_gemma":[0.9994821,0.00017321849,0.000018039116,0.0001380027,0.00011145849,0.00007720925],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079910364,0.00013456009,0.00012871595,0.00011966676,0.0005154593,0.00030211578,0.00021405434,0.000030060866,4.512891e-7],"category_scores_gemma":[0.0001243371,0.00010769847,0.000022783379,0.00037009793,0.000101775724,0.0002975587,0.00018949516,0.00012365684,3.2437973e-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":[0.0000034169095,0.0000012789702,0.0004586316,0.000027959159,0.000004033422,4.189751e-7,0.0046571703,0.7788487,0.001988732,0.00018909047,0.00006294724,0.21375763],"study_design_scores_gemma":[0.000095783216,0.00003754466,0.00037120515,0.00013858752,0.000006354374,0.000007486321,0.000112990856,0.9976321,0.00071331864,0.0004280361,0.0003337867,0.00012283234],"about_ca_topic_score_codex":0.000040489045,"about_ca_topic_score_gemma":0.00017642468,"teacher_disagreement_score":0.657767,"about_ca_system_score_codex":0.00005969002,"about_ca_system_score_gemma":0.00003904843,"threshold_uncertainty_score":0.43918163},"labels":[],"label_agreement":null},{"id":"W4409787719","doi":"10.61091/jcmcc127a-524","title":"Quantification of ultra-short-term forecasting performance of wind power based on multivariate LSTM and logistic coupled models","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Term (time); Multivariate statistics; Logistic regression; Econometrics; Computer science; Wind power; Artificial intelligence; Machine learning; Environmental science; Statistics; Mathematics; Engineering; Physics","score_opus":0.02782414027685679,"score_gpt":0.24891529955909086,"score_spread":0.22109115928223408,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409787719","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":[0.96891874,0.0001822173,0.024566157,0.000007902403,0.0047113616,0.00015602153,0.0000031501534,0.000025505453,0.0014289503],"genre_scores_gemma":[0.997594,0.00002915838,0.002211564,0.000003498301,0.00013060054,7.873196e-7,0.0000017388311,0.000027042563,0.0000016108407],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980214,0.000044866127,0.00120131,0.00014877754,0.00035856152,0.00022508077],"domain_scores_gemma":[0.99785477,0.0009256802,0.0005832517,0.00018916372,0.00036686572,0.0000802541],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010147007,0.00024964826,0.0007081695,0.00027754938,0.0001171936,0.000054185624,0.00020449892,0.00014670826,0.0000016295046],"category_scores_gemma":[0.0002759209,0.00023010059,0.00010413211,0.00027189578,0.00009020866,0.00014750584,0.000043901742,0.0003290864,8.04394e-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":[0.00049088657,0.00078118884,0.0024495814,0.0037490022,0.00035727237,0.000011755547,0.0019487555,0.4293493,0.030992497,0.5265177,0.000023068815,0.0033290146],"study_design_scores_gemma":[0.0022105144,0.00056128245,0.0003552201,0.0021832774,0.00009797323,0.000011369961,0.000086324275,0.9563266,0.009203895,0.028761838,0.00000628852,0.00019540555],"about_ca_topic_score_codex":0.0000043483437,"about_ca_topic_score_gemma":1.5435255e-7,"teacher_disagreement_score":0.5269773,"about_ca_system_score_codex":0.000045049917,"about_ca_system_score_gemma":0.000066429384,"threshold_uncertainty_score":0.93832296},"labels":[],"label_agreement":null},{"id":"W4409789014","doi":"10.1007/s40435-025-01701-x","title":"One-day-ahead electricity load forecasting of non-residential buildings using a modified Transformer-BiLSTM adversarial domain adaptation forecaster","year":2025,"lang":"en","type":"article","venue":"International Journal of Dynamics and Control","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":false,"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":"Transformer; Adversarial system; Electricity; Domain adaptation; Computer science; Adaptation (eye); Econometrics; Engineering; Artificial intelligence; Economics; Electrical engineering; Voltage","score_opus":0.010599839467668156,"score_gpt":0.22896825530213102,"score_spread":0.21836841583446287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409789014","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":[0.46978632,0.00015157434,0.52865833,0.00009914691,0.0006610293,0.000060167353,0.000017307968,0.0000065259833,0.0005595969],"genre_scores_gemma":[0.99573547,0.000049692775,0.0038708926,0.000038010752,0.00026698448,0.0000019049215,0.0000042993092,0.000015698222,0.000017022781],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986478,0.000025346832,0.00065839774,0.00010572246,0.00037616247,0.00018653602],"domain_scores_gemma":[0.99909115,0.0001378459,0.00025498652,0.000051714338,0.00041175613,0.000052570274],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040541566,0.0001456798,0.00030357167,0.00032058184,0.000049728256,0.00006791078,0.00019825056,0.00009456369,0.0000052355863],"category_scores_gemma":[0.000051802268,0.00015026188,0.00015750292,0.00013474784,0.00003359113,0.00030622265,0.000013405206,0.00022227272,8.304984e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017461547,0.0001232559,0.0023451303,0.00014357035,0.0019454033,0.000042339114,0.0015402286,0.69413733,0.10945998,0.007671333,0.000020594207,0.18082468],"study_design_scores_gemma":[0.003287841,0.000095213145,0.00040718872,0.00034766,0.00010720572,0.000044180193,0.0000931514,0.99084705,0.0017522197,0.002863099,0.00003548627,0.000119724544],"about_ca_topic_score_codex":0.00013839097,"about_ca_topic_score_gemma":0.0001733091,"teacher_disagreement_score":0.5259492,"about_ca_system_score_codex":0.00020802993,"about_ca_system_score_gemma":0.00011849315,"threshold_uncertainty_score":0.6127502},"labels":[],"label_agreement":null},{"id":"W4409902815","doi":"10.18280/jesa.580315","title":"Advanced Deep Learning Models for Accurate Solar Energy Output Prediction","year":2025,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Deep learning; Solar energy; Energy (signal processing); Artificial intelligence; Computer science; Environmental science; Engineering; Mathematics; Statistics; Electrical engineering","score_opus":0.01710879061353105,"score_gpt":0.2307380979085647,"score_spread":0.21362930729503365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409902815","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":[0.033113837,0.0042728498,0.94531214,0.000027756067,0.0017887661,0.00011527717,0.0000118987,0.00093502126,0.014422438],"genre_scores_gemma":[0.9843289,0.0009987929,0.011222692,0.000048348,0.0003056847,0.000035398236,0.000016201266,0.000085258645,0.0029587103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984359,0.00009294012,0.00060885196,0.00019443581,0.00019723811,0.0004706216],"domain_scores_gemma":[0.99925154,0.00015824352,0.0001524628,0.00015285713,0.00015930338,0.00012556113],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003254463,0.000266162,0.00032385232,0.0002769578,0.00048282123,0.0002385398,0.00022832587,0.00011722355,0.000014838811],"category_scores_gemma":[0.00013392486,0.00025447074,0.00017552321,0.00029388088,0.000031741256,0.0006975123,0.000043197608,0.00035466044,0.0000052630667],"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":[0.000011574939,0.0000073615643,0.00006353551,0.00012688604,0.000097131946,0.00000861284,0.0002016903,0.61008376,0.00074010046,0.0015624692,0.00047634015,0.38662052],"study_design_scores_gemma":[0.00059088325,0.00008934224,0.0018709393,0.00057320244,0.000056558427,0.00012451995,0.00009626687,0.9703482,0.0009563037,0.0069137653,0.018163554,0.00021646601],"about_ca_topic_score_codex":0.00000552442,"about_ca_topic_score_gemma":0.000011857052,"teacher_disagreement_score":0.9512151,"about_ca_system_score_codex":0.00020463757,"about_ca_system_score_gemma":0.00004307467,"threshold_uncertainty_score":0.99999076},"labels":[],"label_agreement":null},{"id":"W4409979403","doi":"10.1155/acis/5211419","title":"Predicting Residential Energy Consumption in South Africa Using Ensemble Models","year":2025,"lang":"en","type":"article","venue":"Applied Computational Intelligence and Soft Computing","topic":"Energy Load and Power Forecasting","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":"École de Technologie Supérieure","funders":"University of South Africa","keywords":"Computer science; Energy consumption; Consumption (sociology); Energy (signal processing); Artificial intelligence; Statistics; Ecology","score_opus":0.03387955395922034,"score_gpt":0.24883503795827308,"score_spread":0.21495548399905273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409979403","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":[0.26016083,0.00053654367,0.73528147,0.0000033719155,0.00016354994,0.000050185838,0.0000013867096,0.00014053415,0.0036621403],"genre_scores_gemma":[0.9872771,0.000015294734,0.012539496,0.00004832338,0.00007346006,0.000004384415,0.000012000103,0.000017352106,0.000012599086],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99884427,0.000020513948,0.00042569116,0.0002681227,0.00014984352,0.00029155402],"domain_scores_gemma":[0.99946433,0.00032068903,0.000055203127,0.000077555975,0.000036100282,0.000046125213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019184392,0.00017394328,0.00019247789,0.00025724547,0.00018178258,0.000087912624,0.000110523055,0.00008881743,0.0000044143753],"category_scores_gemma":[0.000011353512,0.00020647502,0.00003277276,0.0003163387,0.000048747712,0.00011011738,0.00008765961,0.00018138114,0.0000020099853],"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":[0.000007921992,0.000008073881,0.0010859905,0.000048377515,0.000022435239,0.0000028984855,0.002061915,0.92398727,0.00020056697,0.05254546,0.00000618575,0.020022918],"study_design_scores_gemma":[0.00010866361,0.0000049390696,0.00020032052,0.00017865756,0.000010168963,0.0000044343114,0.0003272439,0.95479256,0.00077224703,0.043416362,0.000017416518,0.0001669748],"about_ca_topic_score_codex":0.000035175413,"about_ca_topic_score_gemma":0.000014265703,"teacher_disagreement_score":0.7271162,"about_ca_system_score_codex":0.000058186866,"about_ca_system_score_gemma":0.000032248525,"threshold_uncertainty_score":0.84198064},"labels":[],"label_agreement":null},{"id":"W4410045744","doi":"10.1016/b978-0-443-34041-3.00009-2","title":"LSTM-based day-ahead photovoltaic power prediction","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Photovoltaic system; Computer science; Power (physics); Artificial intelligence; Electrical engineering; Engineering; Physics","score_opus":0.008489167483844488,"score_gpt":0.19692766371977638,"score_spread":0.1884384962359319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410045744","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000050933017,0.0017554611,0.00019882312,0.0000069059406,0.0025686182,0.000317217,0.00018565383,0.0008584919,0.9940579],"genre_scores_gemma":[0.0057642143,0.00006106657,0.00037165752,0.00021307175,0.000404991,0.000054007825,0.00017567218,0.00018139805,0.99277395],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9983188,0.000015913705,0.0005267468,0.00042357334,0.00030819967,0.00040675412],"domain_scores_gemma":[0.99901915,0.00009308389,0.00009108119,0.0005895475,0.00007058384,0.00013654683],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00018121442,0.0005999046,0.00052172947,0.000319265,0.0001070084,0.000056195222,0.00025957802,0.0005760778,0.0004670378],"category_scores_gemma":[0.000015560261,0.0006356197,0.0003066283,0.00002821269,0.00006713438,0.00004740101,0.000053944772,0.0007438535,0.00012370202],"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":[0.000012102289,0.0000072304633,0.000017762113,0.00041551734,0.0002580911,0.00004968818,0.00013540707,0.0015574595,0.0006036404,0.0024573395,0.0037522076,0.99073356],"study_design_scores_gemma":[0.00029028812,0.00004301619,0.000010294398,0.0014945363,0.000114932685,0.000005164062,0.0000018067711,0.0036983548,0.00092742214,0.0009402863,0.99196714,0.00050677545],"about_ca_topic_score_codex":9.4019776e-7,"about_ca_topic_score_gemma":0.000028763021,"teacher_disagreement_score":0.9902268,"about_ca_system_score_codex":0.00017780099,"about_ca_system_score_gemma":0.00010822739,"threshold_uncertainty_score":0.99960953},"labels":[],"label_agreement":null},{"id":"W4410045901","doi":"10.1016/b978-0-443-34041-3.00003-1","title":"Generative adversarial network–based annual photovoltaic power simulation","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Photovoltaic system; Generative grammar; Adversarial system; Computer science; Generative adversarial network; Power (physics); Artificial intelligence; Electrical engineering; Engineering; Deep learning; Physics","score_opus":0.008976154398505751,"score_gpt":0.21377569613170033,"score_spread":0.2047995417331946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410045901","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00003232423,0.0010032293,0.0013796749,0.0000045641673,0.0027553847,0.00035662166,0.00016908915,0.00041104094,0.9938881],"genre_scores_gemma":[0.016321793,0.000018239625,0.001515587,0.0003110852,0.0016355099,0.000030382267,0.00021507134,0.00016552533,0.9797868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99839336,0.000023928036,0.0004886965,0.00039642528,0.00027378113,0.00042379054],"domain_scores_gemma":[0.99909717,0.00016791583,0.00011587696,0.00039017256,0.00011934038,0.000109555],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00014245832,0.000587818,0.0005379538,0.00017681661,0.00013891389,0.00004897437,0.00020124383,0.00054974493,0.00037141546],"category_scores_gemma":[0.000015811305,0.00062677404,0.00028065604,0.000030445739,0.000059237474,0.00006448104,0.0000620076,0.0006026994,0.00005586462],"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":[0.000030854997,0.0000031458626,0.0000024755498,0.000083858125,0.0002232664,0.000029383118,0.00020332809,0.6451713,0.000053755135,0.0018772846,0.0019877933,0.3503336],"study_design_scores_gemma":[0.00040780252,0.000039969138,0.0000019035772,0.0005563631,0.00010702222,0.0000011051692,0.0000028742177,0.09497392,0.0001389757,0.0011797498,0.9020187,0.0005715561],"about_ca_topic_score_codex":6.0874214e-7,"about_ca_topic_score_gemma":0.00003141804,"teacher_disagreement_score":0.900031,"about_ca_system_score_codex":0.00015956664,"about_ca_system_score_gemma":0.00012107147,"threshold_uncertainty_score":0.99961835},"labels":[],"label_agreement":null},{"id":"W4410050098","doi":"10.1016/j.eswa.2025.127872","title":"Sequential methods for error correction of probabilistic wind power forecasts","year":2025,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Energy Load and Power Forecasting","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":"Queen's University","funders":"","keywords":"Computer science; Probabilistic logic; Wind power; Forecast error; Wind power forecasting; Power (physics); Artificial intelligence; Machine learning; Econometrics; Electric power system; Mathematics; Electrical engineering","score_opus":0.01932661802728094,"score_gpt":0.3107841474797758,"score_spread":0.29145752945249487,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410050098","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":[0.0018600291,0.0010366578,0.981084,0.000020258005,0.0010902528,0.0011646221,0.000015262132,0.00020550934,0.013523437],"genre_scores_gemma":[0.9589163,0.0000059826084,0.036944468,0.000013014514,0.00013021671,0.0026943746,0.00003746065,0.000037268837,0.0012209318],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992681,0.00002658829,0.00029579672,0.0001786479,0.00006378479,0.00016708975],"domain_scores_gemma":[0.9993451,0.00016870402,0.000063292304,0.00026684013,0.00011516448,0.000040895196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016513708,0.00013330586,0.0002081832,0.00010400694,0.00009380788,0.000021752363,0.0001110445,0.000078186116,0.0000067899864],"category_scores_gemma":[0.00002314475,0.000116234485,0.000051809766,0.0003101634,0.000037678717,0.000058808022,0.000011538664,0.00006382239,0.0000018656523],"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":[0.00013590087,0.0002225579,0.00028793162,0.0017219094,0.000676931,6.3504297e-7,0.00334709,0.7397568,0.07095639,0.078836426,0.016954124,0.08710329],"study_design_scores_gemma":[0.000653471,0.00010187147,0.00006068625,0.00045984937,0.000060222774,0.000018135603,0.00058189954,0.52023435,0.019710368,0.00035971688,0.4574162,0.00034323134],"about_ca_topic_score_codex":0.000047968904,"about_ca_topic_score_gemma":0.00001562822,"teacher_disagreement_score":0.9570563,"about_ca_system_score_codex":0.000070685135,"about_ca_system_score_gemma":0.00004053019,"threshold_uncertainty_score":0.4739905},"labels":[],"label_agreement":null},{"id":"W4410219855","doi":"10.1016/j.energy.2025.136498","title":"LSTM and Transformer-based framework for bias correction of ERA5 hourly wind speeds","year":2025,"lang":"en","type":"article","venue":"Energy","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Alliance de recherche numérique du Canada; Simon Fraser University; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Transformer; Wind speed; Environmental science; Wind power; Computer science; Meteorology; Engineering; Electrical engineering; Physics; Voltage","score_opus":0.013870482039335311,"score_gpt":0.2288128524399899,"score_spread":0.2149423704006546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410219855","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":[0.26295716,0.0010185258,0.7146253,0.00008510408,0.0024899393,0.00006435077,0.000015732574,0.00017683035,0.018567067],"genre_scores_gemma":[0.9953249,0.00004756015,0.0038852957,0.00007406089,0.000092449234,0.000007552068,0.000015719637,0.000019085473,0.0005333564],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954295,0.000008152536,0.00015570053,0.00009532833,0.000053287175,0.00014457235],"domain_scores_gemma":[0.9996464,0.00019593128,0.000019145105,0.00008607238,0.00002127296,0.0000311408],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006259261,0.000101545666,0.00014093326,0.00011383454,0.000041528936,0.000013394779,0.000047508485,0.000104652136,0.00001614599],"category_scores_gemma":[0.000031830838,0.00010178568,0.00005486926,0.00018406007,0.000021292317,0.000043750282,0.0000027391618,0.00007219632,2.4366443e-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":[0.000107603504,0.000050453615,0.0011803043,0.0003319286,0.00016868756,0.0000019519937,0.00043692213,0.4795472,0.012418931,0.06924622,0.0029526965,0.4335571],"study_design_scores_gemma":[0.0009826892,0.0002025134,0.00087441894,0.0006354833,0.00007155748,0.0000026685034,0.000085583175,0.31489593,0.51255846,0.006187207,0.16314198,0.0003615349],"about_ca_topic_score_codex":0.000080670005,"about_ca_topic_score_gemma":0.0000692314,"teacher_disagreement_score":0.73236775,"about_ca_system_score_codex":0.000016352009,"about_ca_system_score_gemma":0.000017805398,"threshold_uncertainty_score":0.41506997},"labels":[],"label_agreement":null},{"id":"W4410428247","doi":"10.1109/access.2025.3570719","title":"Dynamic Modeling and Quantum-Enhanced Forecasting of Multi-Seasonal Energy Prices in Simulated Microgrid Environments","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Microgrid; Computer science; Energy (signal processing); Quantum; Environmental science; Artificial intelligence; Statistics; Control (management); Mathematics","score_opus":0.020512712017113504,"score_gpt":0.2602556084013847,"score_spread":0.23974289638427118,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410428247","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":[0.8711762,0.0015605038,0.126684,0.0000033146796,0.00025683746,0.000052286472,0.000006332872,0.00004724905,0.00021330836],"genre_scores_gemma":[0.9985836,0.0003279392,0.0009939458,0.000016880209,0.000013077987,0.0000070783126,0.000010061341,0.00002330939,0.000024075582],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990995,0.000012298681,0.00033148873,0.00020789528,0.00009097521,0.00025783342],"domain_scores_gemma":[0.99969995,0.00008146677,0.00005027566,0.00011792677,0.000012481475,0.00003787292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008607457,0.00016620658,0.00022145371,0.00018164741,0.000041840074,0.000038499882,0.00020085709,0.00009000294,0.000004489597],"category_scores_gemma":[0.000017445986,0.0001765156,0.000034330733,0.00025872837,0.000026853415,0.00025995306,0.00006638881,0.00012098857,3.2340228e-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":[0.000012648308,0.000023946732,0.0013501135,0.00009991122,0.00003332075,0.0000036123695,0.00011493627,0.94632137,0.039190438,0.000033723674,0.0000017273335,0.012814243],"study_design_scores_gemma":[0.0005328395,0.0000075683984,0.00047323736,0.0002934176,0.00001128011,0.0000010024929,0.000022322258,0.96729356,0.031040095,0.00013159959,0.00004393857,0.00014913449],"about_ca_topic_score_codex":0.00016346312,"about_ca_topic_score_gemma":0.00020157023,"teacher_disagreement_score":0.12740746,"about_ca_system_score_codex":0.00004857112,"about_ca_system_score_gemma":0.00001156016,"threshold_uncertainty_score":0.7198098},"labels":[],"label_agreement":null},{"id":"W4410459149","doi":"10.2139/ssrn.5258801","title":"Machine Learning for Sustainable Urban Energy Planning: A Comparative Model Analysis","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sustainable energy; Energy (signal processing); Environmental planning; Computer science; Energy analysis; Environmental economics; Geography; Engineering; Economics; Renewable energy; Mathematics","score_opus":0.01451877092215177,"score_gpt":0.25509519523307256,"score_spread":0.2405764243109208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410459149","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":[0.0062725465,0.023674138,0.9515947,0.000032603442,0.00021200343,0.00013937528,0.000022277822,0.00026224257,0.017790133],"genre_scores_gemma":[0.9580161,0.0022119875,0.00074904185,0.000020112553,0.0003135877,0.000060216986,0.00025553207,0.00005196655,0.038321458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99610066,0.00007281553,0.0005156986,0.0003607868,0.00022042305,0.0027296166],"domain_scores_gemma":[0.9990989,0.000121005054,0.00021917297,0.00023175789,0.00021580493,0.00011334037],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00092246337,0.00047484515,0.0008011569,0.0008370564,0.00037460122,0.00018126525,0.00047713885,0.0003063625,0.00000692944],"category_scores_gemma":[0.00004100636,0.00048411862,0.0006000425,0.0005090164,0.000023161354,0.00012596701,0.0001829106,0.0043204054,4.6726606e-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":[0.000040286162,0.000014732078,0.00036942546,0.000085237305,0.0038243486,0.000004937203,0.00061805395,0.86807734,0.000007647248,0.12630187,0.00021896299,0.0004371813],"study_design_scores_gemma":[0.00034838417,0.0000717776,0.0000031316638,0.00008564637,0.0008854487,0.000014603788,0.0008154479,0.90368813,0.00007136289,0.08849525,0.0051117926,0.0004090029],"about_ca_topic_score_codex":0.00015658505,"about_ca_topic_score_gemma":0.0004253186,"teacher_disagreement_score":0.95174354,"about_ca_system_score_codex":0.0013911825,"about_ca_system_score_gemma":0.0015504579,"threshold_uncertainty_score":0.99976104},"labels":[],"label_agreement":null},{"id":"W4410521164","doi":"10.71070/es.v5i1.88","title":"Energy Consumption Prediction using Support Vector Regression","year":2025,"lang":"en","type":"article","venue":"Energy & System","topic":"Energy Load and Power Forecasting","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":"Laurentian University; Cape Breton University; Thompson Rivers University","funders":"","keywords":"Regression analysis; Regression; Statistics; Support vector machine; Energy consumption; Econometrics; Computer science; Mathematics; Machine learning; Biology; Ecology","score_opus":0.013268037753203038,"score_gpt":0.21996425181686,"score_spread":0.20669621406365696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410521164","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":[0.45529446,0.0044778027,0.38237837,0.00003109641,0.01762338,0.00013131923,0.000066829874,0.004186773,0.13580997],"genre_scores_gemma":[0.9974133,0.000072430594,0.00027036402,0.000026815898,0.00034834753,0.000022258868,0.00006431801,0.000037736318,0.0017444342],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998906,0.000055366803,0.0003662334,0.00022821008,0.00016521287,0.0002789808],"domain_scores_gemma":[0.9995366,0.000038290367,0.00005830365,0.0002549062,0.000039188257,0.00007273687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000119700926,0.00020938642,0.00022966573,0.0001990627,0.00014286063,0.000042543525,0.000113031165,0.00017230911,0.00004668785],"category_scores_gemma":[0.000005460049,0.00019767461,0.00008119974,0.00023556552,0.000020289977,0.00014533101,0.000034436776,0.00008822476,0.000007765919],"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":[0.00010144068,0.00008470678,0.0108066825,0.0022556414,0.00069219095,0.00016199952,0.0005074311,0.41430783,0.23996551,0.22972275,0.023566397,0.07782741],"study_design_scores_gemma":[0.0007364062,0.00004520483,0.000726651,0.0025705118,0.00010652054,0.00011196112,0.00014509418,0.7342128,0.13174379,0.000049677492,0.12906322,0.00048812805],"about_ca_topic_score_codex":0.00015263874,"about_ca_topic_score_gemma":0.000039397968,"teacher_disagreement_score":0.54211885,"about_ca_system_score_codex":0.00027296782,"about_ca_system_score_gemma":0.000034851648,"threshold_uncertainty_score":0.80609363},"labels":[],"label_agreement":null},{"id":"W4410600994","doi":"10.3390/en18112675","title":"Quarter-Hourly Power Load Forecasting Based on a Hybrid CNN-BiLSTM-Attention Model with CEEMDAN, K-Means, and VMD","year":2025,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":20,"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":"Chinese Academy of Sciences","keywords":"Quarter (Canadian coin); Power (physics); Computer science; Meteorology; Environmental science; Artificial intelligence; Geography; Physics","score_opus":0.006862433688610103,"score_gpt":0.18447970025946211,"score_spread":0.177617266570852,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410600994","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":[0.9162014,0.0004068914,0.042413823,0.00014255459,0.0003167286,0.000091750815,0.000016643911,0.00050706643,0.03990312],"genre_scores_gemma":[0.9950419,0.0000151328095,0.003833187,0.0001472186,0.000046824283,0.000027741395,0.000018317403,0.00004373732,0.00082593923],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892545,0.000019581403,0.00022273418,0.0002793905,0.00021114435,0.00034170665],"domain_scores_gemma":[0.99951506,0.00008957243,0.000039057635,0.00023815116,0.000054437285,0.00006373245],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014097554,0.0002687517,0.00021927766,0.00017652364,0.000136115,0.00010179172,0.000108710374,0.000069335336,0.000012332889],"category_scores_gemma":[0.00002354831,0.00023650615,0.000058915746,0.0001882923,0.00004603804,0.00018288422,0.000022978906,0.0001937504,0.0000029513974],"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":[0.000044568103,0.00002077642,0.0009706038,0.000084967854,0.000040747076,0.00001644481,0.00017923261,0.9896431,0.0015021208,0.00087703863,0.0016813892,0.0049390276],"study_design_scores_gemma":[0.00064388657,0.00011500083,0.0003466743,0.00048388762,0.000028548835,0.000006793366,0.00015259314,0.991583,0.004563307,0.00025400176,0.0015309704,0.00029133802],"about_ca_topic_score_codex":0.000039284518,"about_ca_topic_score_gemma":0.00007090656,"teacher_disagreement_score":0.07884048,"about_ca_system_score_codex":0.000073785006,"about_ca_system_score_gemma":0.00004690205,"threshold_uncertainty_score":0.9644441},"labels":[],"label_agreement":null},{"id":"W4410629681","doi":"10.1016/j.rineng.2025.105425","title":"A hybrid machine learning and optimization framework for energy forecasting and management","year":2025,"lang":"en","type":"article","venue":"Results in Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":29,"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":"International Development Research Centre; Botswana International University of Science and Technology","keywords":"Computer science; Energy (signal processing); Energy management; Artificial intelligence; Machine learning; Industrial engineering; Management science; Engineering; Mathematics","score_opus":0.006953919177652187,"score_gpt":0.2046102709511928,"score_spread":0.19765635177354063,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410629681","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":[0.00572835,0.0022323893,0.9890205,0.000024331108,0.00025487726,0.000102935686,0.000010409846,0.00023372435,0.002392446],"genre_scores_gemma":[0.8142404,0.0015319694,0.18392752,0.000012462808,0.00005274246,0.000047443868,0.00003154444,0.00003637934,0.0001195231],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929416,0.000005883844,0.00023165617,0.00018446487,0.0000479558,0.00023586348],"domain_scores_gemma":[0.99964374,0.00022108442,0.00001947613,0.00006969956,0.000009753874,0.000036233192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015608466,0.00014756205,0.00014524747,0.0002373965,0.000051504314,0.0000432231,0.000046430272,0.000055878932,7.5824795e-7],"category_scores_gemma":[0.0001343145,0.00016754116,0.000018530682,0.00017132767,0.000007890259,0.0000724927,0.000048270744,0.00014258413,4.8455664e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000014754848,0.0000027353117,0.00008942886,0.00036250672,0.00002696026,0.000006169134,0.00007197024,0.9632588,0.000035160454,0.007861394,0.000010708729,0.028259434],"study_design_scores_gemma":[0.000543071,0.000013207021,0.000031073683,0.0006904116,0.000011010239,0.0000058538885,0.000017564855,0.99147826,0.00045239483,0.00030285824,0.0063034547,0.0001508647],"about_ca_topic_score_codex":0.000008121284,"about_ca_topic_score_gemma":0.000005272015,"teacher_disagreement_score":0.8085121,"about_ca_system_score_codex":0.000036496265,"about_ca_system_score_gemma":0.0000022056397,"threshold_uncertainty_score":0.68321306},"labels":[],"label_agreement":null},{"id":"W4410854586","doi":"10.1109/icdsaai65575.2025.11011862","title":"LSTM-Based Deep Learning Long Term Electric Demand Prediction for Karnataka","year":2025,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Horizon College and Seminary","funders":"","keywords":"Term (time); Computer science; Artificial intelligence; Deep learning; Long short term memory; Machine learning; Demand forecasting; Artificial neural network; Operations research; Engineering; Recurrent neural network","score_opus":0.00552999169208333,"score_gpt":0.20608632481946507,"score_spread":0.20055633312738175,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410854586","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":[0.16831705,0.0008682218,0.7985875,0.000022914819,0.0005187415,0.00014535773,0.0000020548741,0.00080374954,0.030734427],"genre_scores_gemma":[0.99704075,0.000029305249,0.0012025366,0.000043998087,0.00009813756,0.000038218113,0.000042590626,0.000021424423,0.0014830362],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941814,0.000009956195,0.0001544242,0.00012716971,0.000059461054,0.00023083249],"domain_scores_gemma":[0.9997288,0.000112314214,0.000015518877,0.00008386324,0.000025553603,0.00003397889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011021999,0.000108053675,0.000101756865,0.00015587929,0.000105847765,0.00003647815,0.00007062452,0.00007409156,0.000030892017],"category_scores_gemma":[0.00004429414,0.00010626988,0.000054874756,0.00023941016,0.000005525873,0.000077443045,0.000007866463,0.0001355895,0.000003953091],"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":[0.000022239754,0.000018056508,0.04654028,0.0003058826,0.00008762455,0.0000029102625,0.00005594981,0.8242907,0.011738299,0.0014575386,0.0006663746,0.11481414],"study_design_scores_gemma":[0.0005245432,0.000046220408,0.007852868,0.000074867115,0.0000321811,0.0000016292489,0.0000068271347,0.95667166,0.029361052,0.000082934486,0.005215033,0.00013018049],"about_ca_topic_score_codex":0.000003840596,"about_ca_topic_score_gemma":0.000022324324,"teacher_disagreement_score":0.8287237,"about_ca_system_score_codex":0.000048016278,"about_ca_system_score_gemma":0.000013113178,"threshold_uncertainty_score":0.433356},"labels":[],"label_agreement":null},{"id":"W4410861711","doi":"10.2139/ssrn.5274165","title":"Machine Learning for Sustainable Urban Energy Planning: A Comparative Model Analysis","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Sustainable energy; Energy (signal processing); Computer science; Energy planning; Artificial intelligence; Environmental economics; Engineering; Economics; Renewable energy; Mathematics; Statistics","score_opus":0.01451877092215177,"score_gpt":0.25509519523307256,"score_spread":0.2405764243109208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410861711","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":[0.0062725465,0.023674138,0.9515947,0.000032603442,0.00021200343,0.00013937528,0.000022277822,0.00026224257,0.017790133],"genre_scores_gemma":[0.9580161,0.0022119875,0.00074904185,0.000020112553,0.0003135877,0.000060216986,0.00025553207,0.00005196655,0.038321458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99610066,0.00007281553,0.0005156986,0.0003607868,0.00022042305,0.0027296166],"domain_scores_gemma":[0.9990989,0.000121005054,0.00021917297,0.00023175789,0.00021580493,0.00011334037],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00092246337,0.00047484515,0.0008011569,0.0008370564,0.00037460122,0.00018126525,0.00047713885,0.0003063625,0.00000692944],"category_scores_gemma":[0.00004100636,0.00048411862,0.0006000425,0.0005090164,0.000023161354,0.00012596701,0.0001829106,0.0043204054,4.6726606e-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":[0.000040286162,0.000014732078,0.00036942546,0.000085237305,0.0038243486,0.000004937203,0.00061805395,0.86807734,0.000007647248,0.12630187,0.00021896299,0.0004371813],"study_design_scores_gemma":[0.00034838417,0.0000717776,0.0000031316638,0.00008564637,0.0008854487,0.000014603788,0.0008154479,0.90368813,0.00007136289,0.08849525,0.0051117926,0.0004090029],"about_ca_topic_score_codex":0.00015658505,"about_ca_topic_score_gemma":0.0004253186,"teacher_disagreement_score":0.95174354,"about_ca_system_score_codex":0.0013911825,"about_ca_system_score_gemma":0.0015504579,"threshold_uncertainty_score":0.99976104},"labels":[],"label_agreement":null},{"id":"W4410960494","doi":"10.3390/en18112908","title":"Long-Term Multi-Resolution Probabilistic Load Forecasting Using Temporal Hierarchies","year":2025,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Probabilistic logic; Term (time); Probabilistic forecasting; Time horizon; Computer science; Econometrics; Temporal resolution; Variance (accounting); Consistency (knowledge bases); Hierarchy; Reliability (semiconductor); Electricity; Scaling; Mathematical optimization; Economics; Mathematics; Engineering; Artificial intelligence","score_opus":0.032928198040195415,"score_gpt":0.25379108711194737,"score_spread":0.22086288907175194,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410960494","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":[0.95974827,0.0024757164,0.03161704,0.000013651416,0.0010605137,0.000100651545,0.0000061545893,0.00060956134,0.004368423],"genre_scores_gemma":[0.98407227,0.00003736859,0.0148299225,0.00001675185,0.00015813873,0.000019181893,0.000019420659,0.000035506782,0.00081144157],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891627,0.00002887492,0.0003000795,0.00021721478,0.00014708312,0.0003904577],"domain_scores_gemma":[0.9995634,0.00008815211,0.000040007963,0.00020548979,0.00005388912,0.000049054575],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014197965,0.00023021245,0.00021163867,0.00017544447,0.0001999914,0.000085793035,0.00015553116,0.000094105504,0.000014702585],"category_scores_gemma":[0.00010656754,0.00022972895,0.00008021986,0.00032529837,0.00008604834,0.0002066913,0.00008187608,0.00017499704,0.0000038449284],"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":[0.000008432051,0.00001766171,0.011174384,0.00026033746,0.000049519473,0.00001564916,0.00031676132,0.9775118,0.0026016221,0.0011552848,0.000105579355,0.006782917],"study_design_scores_gemma":[0.00047643686,0.000018833889,0.005292845,0.00066324865,0.000042882035,0.000018526036,0.00006432266,0.98665416,0.005092061,0.00034814127,0.0009514203,0.00037709455],"about_ca_topic_score_codex":0.000117889285,"about_ca_topic_score_gemma":0.00035004568,"teacher_disagreement_score":0.02432398,"about_ca_system_score_codex":0.00018852165,"about_ca_system_score_gemma":0.000068369896,"threshold_uncertainty_score":0.93680745},"labels":[],"label_agreement":null},{"id":"W4411008360","doi":"10.1038/s41598-025-04301-z","title":"Optimizing load demand forecasting in educational buildings using quantum-inspired particle swarm optimization (QPSO) with recurrent neural networks (RNNs):a seasonal approach","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":14,"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 Moncton","funders":"","keywords":"Particle swarm optimization; Recurrent neural network; Computer science; Quantum; Artificial neural network; Artificial intelligence; Machine learning","score_opus":0.02146177831163238,"score_gpt":0.23385986085695545,"score_spread":0.21239808254532308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411008360","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":[0.74262345,0.0008834826,0.25167683,0.00002917652,0.0034363426,0.00022090891,9.565242e-7,0.00013221717,0.0009966546],"genre_scores_gemma":[0.95226383,0.000005456793,0.047359064,0.00001317948,0.00011824283,0.00003683094,0.000062315696,0.000033205586,0.00010785695],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99768245,0.000041508574,0.0006291176,0.0006577059,0.00041394262,0.00057528255],"domain_scores_gemma":[0.99912417,0.00006966108,0.00017573024,0.00034128904,0.00016615712,0.00012298443],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010378036,0.00025455447,0.00025210195,0.00020885568,0.00037980182,0.00038790848,0.00013526814,0.00009162252,0.000016758204],"category_scores_gemma":[0.00011726171,0.00024673846,0.00006723605,0.0014022555,0.00011684145,0.00044693655,0.000067660934,0.0002546263,3.2529624e-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":[0.0000142787,0.000054417982,0.011798238,0.00007095387,0.000022270044,0.00003221434,0.00037530626,0.9849778,0.00038372586,0.00013668282,0.00016463925,0.0019695049],"study_design_scores_gemma":[0.0002485615,0.000011792503,0.0002748026,0.00035887933,0.000028486047,0.00014272779,0.0000847991,0.9974442,0.0008253201,0.00011985729,0.00019767921,0.0002628705],"about_ca_topic_score_codex":0.000033033408,"about_ca_topic_score_gemma":0.00003239255,"teacher_disagreement_score":0.20964041,"about_ca_system_score_codex":0.00030023287,"about_ca_system_score_gemma":0.00021903473,"threshold_uncertainty_score":0.9999985},"labels":[],"label_agreement":null},{"id":"W4411037420","doi":"10.18280/jesa.580416","title":"Optimization of a Hybrid PV-Wind Power System for Enhancing Efficiency and Power Quality Using MATLAB/SIMULINK Simulations","year":2025,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"MATLAB; Power quality; Computer science; Power (physics); Wind power; Quality (philosophy); Photovoltaic system; Automotive engineering; Environmental science; Control theory (sociology); Electrical engineering; Engineering; Artificial intelligence; Physics","score_opus":0.016823811045694456,"score_gpt":0.26698903559031845,"score_spread":0.250165224544624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411037420","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":[0.46378088,0.0008571464,0.53339624,0.0000063420653,0.00054772646,0.00015267878,0.000028445113,0.00015206763,0.0010784453],"genre_scores_gemma":[0.977912,0.000019526278,0.021886388,0.000011752282,0.00005533548,0.0000020745683,0.0000050041754,0.00004713612,0.00006077546],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981415,0.00012286085,0.0010039689,0.00018564773,0.00021920331,0.000326789],"domain_scores_gemma":[0.998756,0.0003890716,0.00030465124,0.00018717477,0.00026599184,0.00009713161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063137064,0.00023317127,0.00043663403,0.00032446705,0.00035076158,0.00017130219,0.00015026274,0.00007054493,0.000023261555],"category_scores_gemma":[0.00028120432,0.00022278426,0.00012322901,0.0003627569,0.000056702094,0.0003085437,0.000046484773,0.00017718371,9.4420744e-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":[0.000010843594,0.00001860278,0.00039885516,0.0008391636,0.00009804281,0.000007000714,0.00042756033,0.9904574,0.005253603,0.00084524375,0.00002414761,0.0016195384],"study_design_scores_gemma":[0.0005540221,0.00004826977,0.0028810033,0.001630547,0.000074222145,0.00015550188,0.00024493845,0.9908516,0.003120452,0.00014685788,0.00007908786,0.00021353601],"about_ca_topic_score_codex":0.000009322223,"about_ca_topic_score_gemma":0.0000033911626,"teacher_disagreement_score":0.5141311,"about_ca_system_score_codex":0.00021829591,"about_ca_system_score_gemma":0.000078824094,"threshold_uncertainty_score":0.90848786},"labels":[],"label_agreement":null},{"id":"W4411070469","doi":"10.3390/en18112975","title":"Ensemble of Artificial Neural Networks for Seasonal Forecasting of Wind Speed in Eastern Canada","year":2025,"lang":"en","type":"article","venue":"Energies","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial neural network; Meteorology; Wind speed; Climatology; Artificial intelligence; Environmental science; Computer science; Geography; Geology","score_opus":0.016917242063704194,"score_gpt":0.2129449475703652,"score_spread":0.196027705506661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411070469","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":[0.99500096,0.00041045208,0.0014538274,0.000014849728,0.0008600981,0.00005559082,0.000011734716,0.000020830808,0.0021716284],"genre_scores_gemma":[0.99935025,0.000003009331,0.00031930965,0.000012262429,0.00009751778,0.000001933972,0.000011610793,0.000013528262,0.00019060016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993144,0.000010037924,0.00028752047,0.00009073981,0.00008206417,0.00021520528],"domain_scores_gemma":[0.9996492,0.00016917847,0.000043530425,0.00008312646,0.00003372735,0.000021218219],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000080228056,0.000102786864,0.00020459728,0.00007252666,0.00002203299,0.000007403411,0.00009075575,0.000047026107,0.0000045223455],"category_scores_gemma":[0.00003949054,0.00010848116,0.000044946464,0.0001912388,0.000020529296,0.00004580133,0.000024376066,0.0000716749,2.6171078e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00002274251,0.000005444353,0.004875698,0.00010411615,0.000021185904,0.000002262563,0.00007631399,0.98097944,0.0015377955,0.0009694262,0.00026206576,0.011143491],"study_design_scores_gemma":[0.00018097594,0.000014219929,0.0006891309,0.00012861389,0.000008537835,8.23658e-7,0.000117403346,0.98078114,0.017471382,0.00011280734,0.00040903557,0.000085944106],"about_ca_topic_score_codex":0.020134037,"about_ca_topic_score_gemma":0.18418926,"teacher_disagreement_score":0.16405521,"about_ca_system_score_codex":0.0000334361,"about_ca_system_score_gemma":0.00006487896,"threshold_uncertainty_score":0.98639095},"labels":[],"label_agreement":null},{"id":"W4411162674","doi":"10.1177/17442591251333144","title":"Comparative analysis of deep learning and tree-based models in power demand prediction: Accuracy, interpretability, and computational efficiency","year":2025,"lang":"en","type":"article","venue":"Journal of Building Physics","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Computer science; Boosting (machine learning); Gradient boosting; Random forest; Machine learning; Artificial intelligence; Predictive modelling; Extreme learning machine; Tree (set theory); Mean squared error; Data mining; Artificial neural network; Deep learning; Statistics; Mathematics","score_opus":0.011007708105954746,"score_gpt":0.26564654091853557,"score_spread":0.2546388328125808,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411162674","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":[0.5977216,0.00054404687,0.4012764,0.000006670525,0.00004709059,0.00001809963,0.0000019894094,0.0000068374734,0.0003772722],"genre_scores_gemma":[0.9968566,0.000025290365,0.0030906377,0.0000063416633,0.000013901022,5.7665613e-7,0.0000018088887,0.000003923761,9.5359326e-7],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929464,0.000041520227,0.00037318707,0.00008126144,0.00012244876,0.00008693806],"domain_scores_gemma":[0.99924916,0.00045428143,0.0001345776,0.000038485607,0.0000918514,0.00003166092],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020594026,0.00009055023,0.00034309368,0.00029091813,0.000037085665,0.000025590745,0.00005494998,0.000032699216,0.0000021324406],"category_scores_gemma":[0.000031819494,0.00008633372,0.00007331358,0.00056332524,0.000050397975,0.0001876593,0.00002255967,0.00020874946,1.39839536e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000019932684,0.00002945609,0.03459741,0.00003886015,0.00023353852,9.465406e-7,0.0011768731,0.9594813,0.00066403,0.00084437465,0.0000015316713,0.0029117733],"study_design_scores_gemma":[0.00035586156,0.000051842107,0.014127163,0.00017765607,0.00016356523,0.0000014942574,0.00012762359,0.9821433,0.0007705479,0.0020160724,0.0000073852843,0.00005751442],"about_ca_topic_score_codex":0.0000031995448,"about_ca_topic_score_gemma":0.000007189928,"teacher_disagreement_score":0.39913496,"about_ca_system_score_codex":0.000037088903,"about_ca_system_score_gemma":0.000020740452,"threshold_uncertainty_score":0.35205868},"labels":[],"label_agreement":null},{"id":"W4411194077","doi":"10.1016/j.ijepes.2025.110779","title":"Federated Online Learning for adaptive load forecasting across decentralized nodes","year":2025,"lang":"en","type":"article","venue":"International Journal of Electrical Power & Energy Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"London Hydro; Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Computer science; Adaptive learning; Distributed computing; Artificial intelligence; Machine learning","score_opus":0.017348397033278274,"score_gpt":0.2717127887254524,"score_spread":0.2543643916921741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411194077","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":[0.25553906,0.010001851,0.71960616,0.00016774095,0.009148332,0.00013989798,0.000036651327,0.00020751372,0.0051527997],"genre_scores_gemma":[0.99770296,0.00017559489,0.00065578014,0.00008152494,0.0005047915,0.00000977138,0.000020764655,0.000038422586,0.0008103879],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99793434,0.000069845395,0.0008908088,0.00016938776,0.00048651558,0.00044907434],"domain_scores_gemma":[0.9978764,0.0004887562,0.00029569573,0.00005785894,0.0011694737,0.00011183152],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039114893,0.00023564356,0.0004155144,0.00023099719,0.000127983,0.00021259801,0.00035959645,0.00014788711,0.000009305157],"category_scores_gemma":[0.00046325472,0.00021266475,0.00024060823,0.0003347681,0.000026187965,0.00022534638,0.00003689755,0.0003729931,8.836833e-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":[0.0009987367,0.00020549468,0.0015101478,0.000038375965,0.0022182544,0.0001658021,0.00038052216,0.91711855,0.009223326,0.010188356,0.0043347515,0.053617667],"study_design_scores_gemma":[0.0023776242,0.00025023223,0.00014633975,0.00064199365,0.000043270542,0.00026511654,0.00022234544,0.89616704,0.0042084916,0.00028002894,0.09508832,0.0003091831],"about_ca_topic_score_codex":0.000088857465,"about_ca_topic_score_gemma":0.00003519785,"teacher_disagreement_score":0.7421639,"about_ca_system_score_codex":0.0005372373,"about_ca_system_score_gemma":0.00013030534,"threshold_uncertainty_score":0.8672217},"labels":[],"label_agreement":null},{"id":"W4411270252","doi":"10.1109/tpwrd.2025.3579678","title":"Internal Voltage Equivalent RMS Models of Wind Generation for Transient Stability Assessment","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Power Delivery","topic":"Energy Load and Power Forecasting","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":"Polytechnique Montréal","funders":"","keywords":"Transient (computer programming); Transient analysis; Voltage; Control theory (sociology); Wind power; Stability (learning theory); Engineering; Transient response; Electrical engineering; Environmental science; Materials science; Computer science","score_opus":0.030025119633754115,"score_gpt":0.253968307324256,"score_spread":0.2239431876905019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411270252","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":[0.32348195,0.00007515575,0.67221725,0.000016197717,0.0013942196,0.00019166608,0.00012025136,0.000077860546,0.0024254366],"genre_scores_gemma":[0.9979404,0.00006379433,0.001660112,0.000042810883,0.000023482904,0.00004452719,0.000010046794,0.000023121062,0.00019166559],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989637,0.000021319363,0.00039618002,0.00022936093,0.00017076205,0.00021866015],"domain_scores_gemma":[0.99949336,0.00008656268,0.000032700256,0.00023841983,0.000095619835,0.000053320167],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018250612,0.00018095011,0.00020983026,0.00017108812,0.00008653576,0.000023314808,0.00011927448,0.000094724935,0.00009603656],"category_scores_gemma":[0.0000012451187,0.00018624094,0.00020597942,0.00014499616,0.000034884513,0.00020443248,0.0000010169546,0.00018447379,0.0000012704681],"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":[0.000049653143,0.0001588206,0.0000076466595,0.00011348271,0.00014558766,8.007634e-7,0.00041903116,0.8783154,0.112489924,0.0003084764,0.00021669996,0.007774474],"study_design_scores_gemma":[0.0006539971,0.00015403626,0.00004904641,0.00012120897,0.00007231389,0.0000011311075,0.00009234346,0.7259569,0.2719891,0.00015682171,0.0005722823,0.0001808161],"about_ca_topic_score_codex":0.000033850236,"about_ca_topic_score_gemma":0.0000618092,"teacher_disagreement_score":0.6744585,"about_ca_system_score_codex":0.0001740947,"about_ca_system_score_gemma":0.00005180878,"threshold_uncertainty_score":0.7594685},"labels":[],"label_agreement":null},{"id":"W4411271901","doi":"10.1109/ias55788.2024.11023721","title":"Q-Learning-Based Approach for Mitigating Peak Shaving Impact on Total Demand Load Forecasting","year":2024,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Peaking power plant; Computer science; Demand forecasting; Peak load; Load management; On demand; Peak demand; Operations research; Automotive engineering; Engineering; Electrical engineering","score_opus":0.01645245664835152,"score_gpt":0.23626417366157001,"score_spread":0.2198117170132185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411271901","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":[0.5646186,0.0006623909,0.36792073,0.000018544199,0.0005360781,0.00029516668,0.000020674,0.0020339368,0.063893884],"genre_scores_gemma":[0.984228,0.0000016961717,0.014513431,0.000021956608,0.00044103095,0.0000568047,0.00006529847,0.000117562115,0.0005541881],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985971,0.000020874557,0.00028840057,0.0003321858,0.00021319622,0.0005482535],"domain_scores_gemma":[0.9991937,0.0004801938,0.000026501863,0.00013316274,0.00003869697,0.0001277377],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004263327,0.0003113465,0.00023106876,0.00013456507,0.00018075977,0.00024448984,0.000105894396,0.000114720366,0.00007525668],"category_scores_gemma":[0.00014277615,0.00025609357,0.0002546335,0.00024929526,0.000019954101,0.00016703672,0.000021978009,0.00037787523,0.0000110682495],"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":[0.000011571595,0.000010466123,0.00024372015,0.00039260558,0.00006168299,0.000008943411,0.0003714824,0.9821028,0.0016054858,0.0002268892,0.0003867207,0.01457762],"study_design_scores_gemma":[0.00025534842,0.00018841043,0.00007574882,0.0003837467,0.000026343876,0.000028962986,0.00011747857,0.9942426,0.0038588312,0.00004033296,0.00045283412,0.00032933286],"about_ca_topic_score_codex":0.000026058493,"about_ca_topic_score_gemma":0.000004742956,"teacher_disagreement_score":0.41960943,"about_ca_system_score_codex":0.00018973424,"about_ca_system_score_gemma":0.000055641663,"threshold_uncertainty_score":0.99998915},"labels":[],"label_agreement":null},{"id":"W4411336598","doi":"10.1109/ojcs.2025.3580107","title":"BOL-LPP: A Bayesian-Optimized LSTM Model for Day-Ahead Load Price Forecasting in the ERCOT Market","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of the Computer Society","topic":"Energy Load and Power Forecasting","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":"Western University","funders":"","keywords":"Bayesian probability; Computer science; Econometrics; Artificial intelligence; Economics","score_opus":0.027087850093494865,"score_gpt":0.2527907204034885,"score_spread":0.22570287030999361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411336598","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":[0.014243478,0.00037981223,0.97674567,0.0010659683,0.0018309328,0.0004662349,0.000008706309,0.000023807628,0.0052354187],"genre_scores_gemma":[0.6841855,0.000115707335,0.31094548,0.0024707231,0.00077763235,0.000041243355,0.0000016439601,0.000062696134,0.0013993976],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99850124,0.00012651666,0.0005999708,0.00015510728,0.00025557642,0.00036156608],"domain_scores_gemma":[0.99874073,0.0005463334,0.00020618291,0.0003180624,0.00013800587,0.000050695813],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025094128,0.00021726362,0.00038135034,0.000037920097,0.00022897492,0.00030097924,0.001794888,0.00010474798,0.000006928027],"category_scores_gemma":[0.000048743907,0.00013683867,0.00047805152,0.0003534839,0.000037234677,0.0003383243,0.00021612142,0.00055084954,3.486498e-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":[0.000045330537,0.000035129695,0.00007156695,0.000076405406,0.00016705028,0.0000036122121,0.0036939103,0.8477374,0.00008780222,0.000094191964,0.14036275,0.007624844],"study_design_scores_gemma":[0.0015607347,0.000027665288,0.000096208125,0.00048919633,0.000048624086,0.000046363937,0.00011090076,0.99171835,0.00017593251,0.0010481786,0.0045268917,0.00015097231],"about_ca_topic_score_codex":0.0000088015995,"about_ca_topic_score_gemma":0.000014117422,"teacher_disagreement_score":0.669942,"about_ca_system_score_codex":0.00018077709,"about_ca_system_score_gemma":0.00017263262,"threshold_uncertainty_score":0.5580119},"labels":[],"label_agreement":null},{"id":"W4411336765","doi":"10.1109/jiot.2025.3580378","title":"Toward Efficient Federated Load Forecasting: Personalization Mechanisms and Their Impact","year":2025,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Energy Load and Power Forecasting","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":"Concordia University","funders":"","keywords":"Computer science; Personalization; Load management; Distributed computing; Computer network; World Wide Web","score_opus":0.01808902386511395,"score_gpt":0.23066857557409082,"score_spread":0.21257955170897685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411336765","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":[0.58481455,0.0005890366,0.40987387,0.00003308244,0.00083349476,0.000038434566,0.0000021052404,0.00006275223,0.0037526684],"genre_scores_gemma":[0.99864995,0.000032543227,0.00097601954,0.000055703895,0.0000576234,0.0000010882602,0.0000014052332,0.000019510877,0.00020616985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919695,0.00002346356,0.0003176538,0.000106224106,0.00014479843,0.00021089696],"domain_scores_gemma":[0.9995876,0.000059244565,0.000101906415,0.00004425552,0.00013162673,0.000075384465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003349519,0.00017828573,0.00021601365,0.00014920023,0.000065510336,0.00015058441,0.00013156727,0.0000807265,0.000045590834],"category_scores_gemma":[0.000059880455,0.00013227832,0.000110484725,0.0001351072,0.000025330937,0.00014006166,0.00002663927,0.00028036712,0.0000010321762],"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":[0.0003057354,0.00014710474,0.000965327,0.00086419145,0.0016322543,0.000097430944,0.04571239,0.5058446,0.36389682,0.0031467995,0.008533235,0.06885409],"study_design_scores_gemma":[0.0003720064,0.00008628959,0.000054706787,0.000725103,0.000020165353,0.00033178428,0.0002821968,0.9057537,0.09106712,0.000983513,0.00018663914,0.00013678674],"about_ca_topic_score_codex":0.000046624024,"about_ca_topic_score_gemma":0.0000028998745,"teacher_disagreement_score":0.41383538,"about_ca_system_score_codex":0.00014540827,"about_ca_system_score_gemma":0.000046997964,"threshold_uncertainty_score":0.5394153},"labels":[],"label_agreement":null},{"id":"W4411434766","doi":"10.19184/cerimre.v8i1.53686","title":"Optimizing energy forecasts at Boma for 2023 to 2053 Using machine learning techniques of the PSO algorithm","year":2025,"lang":"en","type":"article","venue":"Computational And Experimental Research In Materials And Renewable Energy","topic":"Energy Load and Power Forecasting","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":"Cégep de l'Abitibi Témiscamingue; Université du Québec en Abitibi-Témiscamingue","funders":"","keywords":"Particle swarm optimization; Energy consumption; Computer science; Data collection; Consumption (sociology); Energy (signal processing); Machine learning; Correlation coefficient; Pearson product-moment correlation coefficient; Artificial intelligence; Environmental economics; Algorithm; Operations research; Statistics; Engineering; Economics; Mathematics","score_opus":0.03125574644744362,"score_gpt":0.31609711646836675,"score_spread":0.28484137002092313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411434766","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":[0.94420063,0.007524273,0.04574376,0.0001026759,0.000582,0.00024782724,0.000077232755,0.0000644382,0.0014571627],"genre_scores_gemma":[0.97255605,0.00017418037,0.02630377,0.00003174613,0.00008099747,0.000083727624,0.00004065169,0.00002150824,0.0007073708],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990616,0.00007644254,0.00024868158,0.00018847841,0.00015991031,0.0002649323],"domain_scores_gemma":[0.99965376,0.00014994029,0.000026455788,0.0000643522,0.000049284718,0.00005623436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003120649,0.000122023135,0.00018933543,0.00020659677,0.00026114075,0.000060256814,0.000097851895,0.00006460915,0.000014356163],"category_scores_gemma":[0.000014661064,0.000098106444,0.000026175727,0.00024342226,0.00007708345,0.00006219073,0.0002771023,0.000047534864,6.501028e-8],"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":[0.000059400438,0.000019299414,0.000037547463,0.00007349233,0.000021618816,0.0000016606484,0.00012516987,0.43587193,0.5559524,0.0015072603,0.00014934578,0.0061808424],"study_design_scores_gemma":[0.00022027276,0.00004853321,0.000013146989,0.00021617362,0.0000018195813,0.000005417953,0.00007070109,0.26791766,0.725515,0.0016589089,0.004248374,0.000083972074],"about_ca_topic_score_codex":0.0034296422,"about_ca_topic_score_gemma":0.0001830721,"teacher_disagreement_score":0.16956256,"about_ca_system_score_codex":0.00008754369,"about_ca_system_score_gemma":0.000025930287,"threshold_uncertainty_score":0.5184616},"labels":[],"label_agreement":null},{"id":"W4411580073","doi":"10.1016/j.engappai.2025.111442","title":"A decomposition–integration interval prediction strategy for iron ore shipping freight rates with reinforcement learning","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; Key Technologies Research and Development Program; National Natural Science Foundation of China","keywords":"Computer science; Reinforcement learning; Interval (graph theory); Decomposition; Iron ore; Artificial intelligence; Reinforcement; Operations research; Composite material; Mathematics","score_opus":0.017439931627158803,"score_gpt":0.26740276989058004,"score_spread":0.24996283826342125,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411580073","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":[0.024493558,0.0001446381,0.9737056,0.000021352285,0.00011482207,0.0003807184,0.000007864854,0.00029315086,0.00083828584],"genre_scores_gemma":[0.9856826,0.000027618,0.013561978,0.0000034755365,0.000085259744,0.00044808185,0.0001013397,0.000023253217,0.00006636967],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991175,0.000008370902,0.00042057183,0.00017703173,0.00009067257,0.00018585449],"domain_scores_gemma":[0.99948967,0.00014023106,0.0000562181,0.00016088823,0.00011513157,0.00003788164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016093139,0.00015909641,0.00015046858,0.00020955986,0.00013066953,0.000048905244,0.00013841873,0.0000720199,0.000011699467],"category_scores_gemma":[0.000032096435,0.00016137365,0.00005090306,0.0004109096,0.000028512186,0.00013840418,0.000014228871,0.00020324858,0.000003247723],"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":[0.000015464238,0.0000145990825,0.000035944773,0.00013863113,0.000031340864,1.0201842e-7,0.00017619524,0.9138367,0.017804073,0.034417953,0.000014312705,0.03351468],"study_design_scores_gemma":[0.000027274025,0.000074527954,0.000047573783,0.00024183413,0.000023681268,0.0000011471941,0.00024664906,0.79554856,0.20253928,0.00056261074,0.00057280983,0.00011406134],"about_ca_topic_score_codex":0.000014861629,"about_ca_topic_score_gemma":0.000021375565,"teacher_disagreement_score":0.9611891,"about_ca_system_score_codex":0.00007403688,"about_ca_system_score_gemma":0.00002075239,"threshold_uncertainty_score":0.6580627},"labels":[],"label_agreement":null},{"id":"W4411617986","doi":"10.51847/6wpo9qrlnz","title":"10.51847/6wpo9qRlNz","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Genetic algorithm; Productivity; Computer science; Distribution (mathematics); Algorithm; Mathematical optimization; Machine learning; Mathematics; Economics","score_opus":0.005054032991344181,"score_gpt":0.15621822441206207,"score_spread":0.15116419142071788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411617986","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047106976,0.00008508904,0.0000032008338,0.00001940465,0.000009800337,0.000034464247,0.000005080168,0.00043538515,0.99469686],"genre_scores_gemma":[0.0037252,5.8270155e-7,0.00015861256,0.000021875776,0.00014860679,0.00000617654,0.000008314754,0.00003081081,0.9958998],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995243,0.0000056195336,0.00009751526,0.00009018354,0.00007225942,0.00021012902],"domain_scores_gemma":[0.9997353,0.000017607465,0.000004119663,0.00014108792,0.000007681556,0.0000941985],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000043010856,0.000097016324,0.00008901552,0.00003831482,0.000031189473,0.000021154197,0.00010220811,0.0000395756,0.98117954],"category_scores_gemma":[0.000005550622,0.000100590165,0.000031011357,0.00012812542,0.000006989933,0.000055630237,0.000009726404,0.00006397502,0.95556843],"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":[0.00000532795,0.000004830596,1.5487208e-7,0.000005629682,0.000010537287,0.000004540936,0.000018081848,0.039007157,0.0001798213,0.0000029926903,0.024808904,0.935952],"study_design_scores_gemma":[0.00007166167,0.000023430684,0.000009156561,0.000016769374,0.0000043170458,0.0000056761105,2.0457705e-7,0.0079871,0.00017328495,0.0000012015149,0.9915744,0.00013277384],"about_ca_topic_score_codex":0.000004686742,"about_ca_topic_score_gemma":1.2947994e-7,"teacher_disagreement_score":0.9667655,"about_ca_system_score_codex":0.000016620366,"about_ca_system_score_gemma":0.000003664616,"threshold_uncertainty_score":0.41019478},"labels":[],"label_agreement":null},{"id":"W4411731065","doi":"10.1016/j.jhydrol.2025.133789","title":"Climatic a priori information for the GEV distribution’s shape parameter of annual maximum flow series","year":2025,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; University of Sydney","keywords":"Series (stratigraphy); A priori and a posteriori; Flow (mathematics); Distribution (mathematics); Mathematics; Meteorology; Climatology; Statistical physics; Environmental science; Geology; Geography; Geometry; Physics; Mathematical analysis; Philosophy","score_opus":0.005705991540492805,"score_gpt":0.2142584145502051,"score_spread":0.2085524230097123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411731065","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":[0.78226376,0.000958495,0.21205859,0.0016481405,0.0020711604,0.00017596215,0.00013829052,0.00003315832,0.0006524458],"genre_scores_gemma":[0.99742895,0.000077508776,0.0022821086,0.000093355164,0.00007745514,0.0000057082225,0.00001197559,0.000004318632,0.000018593046],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993349,0.000015566515,0.0004321532,0.000024749865,0.00006443727,0.00012824868],"domain_scores_gemma":[0.9993948,0.0002695795,0.00014209085,0.00007186577,0.00010358546,0.000018103005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025994907,0.0000678987,0.00017546542,0.000066978224,0.00004396415,0.000012266333,0.00011900977,0.00006411089,0.00001464718],"category_scores_gemma":[0.00017326139,0.00004625526,0.00008989063,0.000088049106,0.000036007368,0.00029788274,0.000016529884,0.00013320331,0.0000011190778],"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":[0.0005682082,0.000052160227,0.0019431522,0.0007505267,0.0008264552,0.000006850384,0.0032680659,0.8085772,0.00075590983,0.006418707,0.014493943,0.16233884],"study_design_scores_gemma":[0.0009908497,0.00043835558,0.0019943563,0.00010943835,0.00015958486,0.00018466682,0.00024821455,0.8635521,0.0018339836,0.006239075,0.12413317,0.00011620248],"about_ca_topic_score_codex":0.0000011338745,"about_ca_topic_score_gemma":0.000003734513,"teacher_disagreement_score":0.21516521,"about_ca_system_score_codex":0.000025942636,"about_ca_system_score_gemma":0.000026491958,"threshold_uncertainty_score":0.18862347},"labels":[],"label_agreement":null},{"id":"W4411749825","doi":"10.1029/2024ms004742","title":"A Model‐Independent Strategy for the Targeted Observation Analysis and Its Application in ENSO Prediction","year":2025,"lang":"en","type":"article","venue":"Journal of Advances in Modeling Earth Systems","topic":"Energy Load and Power Forecasting","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 Northern British Columbia","funders":"","keywords":"El Niño Southern Oscillation; Climatology; Environmental science; Computer science; Meteorology; Geology; Geography","score_opus":0.02362390853547186,"score_gpt":0.26273745743660026,"score_spread":0.2391135489011284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411749825","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":[0.35001156,0.0117728785,0.63782495,0.000010202513,0.00018966303,0.00013017518,0.0000042868073,0.000009960448,0.000046302866],"genre_scores_gemma":[0.9973567,0.0015844543,0.0009362915,0.000004323435,0.000062966574,0.000026810116,0.0000040658733,0.00000643194,0.000017964803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991421,0.00002058589,0.0005247453,0.00008389843,0.00012297055,0.000105691535],"domain_scores_gemma":[0.99962205,0.000079657046,0.00011149489,0.0000667114,0.00010257186,0.000017512224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005570772,0.00007654488,0.0001927655,0.00030193053,0.000039462688,0.000029174638,0.00007424704,0.000054424276,1.5428252e-7],"category_scores_gemma":[0.000027682312,0.000060099697,0.000045855766,0.00041339247,0.000003855859,0.00034638756,0.0000054621255,0.000148222,4.2340638e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017843877,0.0000054262628,0.0038451557,0.00009228429,0.00005018506,2.9762896e-7,0.00011769786,0.9913817,0.00094140024,0.0003675294,6.643391e-7,0.0031797835],"study_design_scores_gemma":[0.00029218578,0.000014985297,0.0006331189,0.0001607955,0.000053307962,0.000001929627,0.00015290278,0.9980949,0.00012054554,0.00036370664,0.000065446795,0.00004615249],"about_ca_topic_score_codex":0.000022520546,"about_ca_topic_score_gemma":0.00028917167,"teacher_disagreement_score":0.6473451,"about_ca_system_score_codex":0.000041660252,"about_ca_system_score_gemma":0.000020214524,"threshold_uncertainty_score":0.24507944},"labels":[],"label_agreement":null},{"id":"W4411866950","doi":"10.1080/03081079.2025.2522707","title":"Interpretable incubation period prediction with gradient boosting acceleration and disjoint region optimization based on generalized additive model","year":2025,"lang":"en","type":"article","venue":"International Journal of General Systems","topic":"Energy Load and Power Forecasting","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":"York University","funders":"Beijing Municipal Natural Science Foundation; National Natural Science Foundation of China","keywords":"Disjoint sets; Mathematics; Boosting (machine learning); Gradient boosting; Incubation period; Mathematical optimization; Range (aeronautics); Artificial intelligence; Pattern recognition (psychology); Applied mathematics; Computer science; Algorithm; Combinatorics; Incubation; Biology","score_opus":0.009157433963809539,"score_gpt":0.21166523797504297,"score_spread":0.20250780401123342,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411866950","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":[0.13919884,0.0001565048,0.85545427,0.0001697641,0.0015774135,0.00012571708,0.000019735318,0.00005063748,0.0032471102],"genre_scores_gemma":[0.9952555,0.00006204302,0.00395,0.00006224978,0.0003734376,0.000017168346,0.00006750606,0.000018167595,0.00019395805],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989278,0.00004742132,0.00046777437,0.0001278575,0.00031485513,0.00011431585],"domain_scores_gemma":[0.99925995,0.000029430263,0.00021747967,0.00007056668,0.00037221963,0.000050336035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018499799,0.00014721969,0.00017770492,0.00033746334,0.00007482262,0.00019641685,0.00011050646,0.00006553444,0.0000044546236],"category_scores_gemma":[0.000037483605,0.00012298289,0.000047939295,0.00011299032,0.000018942228,0.00041457015,0.0000148849185,0.00014432578,2.058046e-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":[0.00015427846,0.00002013763,0.00069220766,0.000019172347,0.00010175607,0.00000938336,0.00021945538,0.9950086,0.0007012317,0.0009003921,0.00054396095,0.0016294266],"study_design_scores_gemma":[0.0008993926,0.00012440747,0.00015432997,0.000689866,0.00002890234,0.00007430752,0.000057477595,0.9968822,0.0006877767,0.000055977922,0.00024384112,0.000101515085],"about_ca_topic_score_codex":0.000022891229,"about_ca_topic_score_gemma":0.000008202756,"teacher_disagreement_score":0.85605663,"about_ca_system_score_codex":0.00029161162,"about_ca_system_score_gemma":0.000047863807,"threshold_uncertainty_score":0.50150967},"labels":[],"label_agreement":null},{"id":"W4412033942","doi":"10.18280/isi.300522","title":"Comparative Analysis of ANN and LSTM Models for Photovoltaic Panel Temperature Prediction in Hot Climates","year":2025,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":5,"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":"Photovoltaic system; Artificial neural network; Environmental science; Meteorology; Computer science; Artificial intelligence; Engineering; Geography; Electrical engineering","score_opus":0.01895672895017217,"score_gpt":0.23375407090388936,"score_spread":0.2147973419537172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412033942","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":[0.96923417,0.00034142612,0.024650974,0.0000022246186,0.00013457141,0.0002349244,0.0001881416,0.00009996404,0.0051135863],"genre_scores_gemma":[0.99866474,0.00005127847,0.00083268824,0.0000193397,0.000008784948,0.00006555687,0.00033510153,0.000004004649,0.0000185068],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992667,0.000012573058,0.00044106372,0.00006827102,0.00006935836,0.00014202246],"domain_scores_gemma":[0.9996373,0.00007526301,0.000075992044,0.0000856415,0.00010420882,0.000021560676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016427663,0.00011663226,0.0002930518,0.00062951323,0.000063569256,0.000056096032,0.000051691313,0.00009908646,0.0000031150723],"category_scores_gemma":[0.000025908552,0.000115458206,0.00005424677,0.00082199386,0.0000386767,0.0011024259,0.000014103826,0.000076386554,3.5838002e-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":[0.000065874956,0.000010391057,0.004647485,0.00081581407,0.00038529513,1.3570128e-7,0.011602118,0.973283,0.0049082516,0.0016989715,0.00016473814,0.002417951],"study_design_scores_gemma":[0.00028797978,0.00002622332,0.011998511,0.00021979399,0.000118299555,7.5319446e-7,0.0011044936,0.97732407,0.007940851,0.0007423858,0.00014230472,0.000094353236],"about_ca_topic_score_codex":0.00004243935,"about_ca_topic_score_gemma":0.00007548445,"teacher_disagreement_score":0.029430551,"about_ca_system_score_codex":0.000081917715,"about_ca_system_score_gemma":0.00001563068,"threshold_uncertainty_score":0.4708249},"labels":[],"label_agreement":null},{"id":"W4412161507","doi":"10.46254/af6.20250213","title":"Enhancing Hybrid Model for Photovoltaic Power Prediction: A Case Study of Morocco","year":2025,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Hydro-Québec; Polytechnique Montréal","keywords":"Photovoltaic system; Power (physics); Computer science; Engineering; Electrical engineering; Physics","score_opus":0.012734137126627966,"score_gpt":0.2344571694827463,"score_spread":0.22172303235611834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412161507","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":[0.7798902,0.00006281731,0.212735,0.0000014767926,0.00028175514,0.00022041125,0.000014046503,0.00019424135,0.006600062],"genre_scores_gemma":[0.9966115,0.0000021879725,0.002037238,0.000018924457,0.00002276361,0.00007027868,0.0000027219464,0.000017479257,0.0012169384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937475,0.0000048765182,0.00026560292,0.00013115144,0.00006644376,0.00015718341],"domain_scores_gemma":[0.9996792,0.00005537611,0.000017992834,0.00016636422,0.000050042938,0.00003100299],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000952385,0.000109689274,0.00015488907,0.00010099536,0.00005878003,0.000011357758,0.000059542363,0.000029365334,0.00002119748],"category_scores_gemma":[0.000015652997,0.000106193555,0.00005003377,0.00012428248,0.0000069910225,0.000077987555,0.00002314878,0.00006670151,5.97317e-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":[0.000018136961,0.00015294041,0.000854641,0.00018473541,0.00017380183,0.00008312374,0.002579502,0.97171587,0.021789867,0.00022810143,0.0013182292,0.00090107013],"study_design_scores_gemma":[0.00056770054,0.0000801589,0.000011644239,0.000041685376,0.000033748744,0.00006347954,0.0015991328,0.95418453,0.04310268,0.00007852564,0.00015039569,0.000086287866],"about_ca_topic_score_codex":0.00013654768,"about_ca_topic_score_gemma":0.00046975052,"teacher_disagreement_score":0.21672128,"about_ca_system_score_codex":0.000027066922,"about_ca_system_score_gemma":0.000018749606,"threshold_uncertainty_score":0.43304476},"labels":[],"label_agreement":null},{"id":"W4412448367","doi":"10.1016/j.segan.2025.101794","title":"Personalized poison attack tolerant federated learning for residential load forecasting","year":2025,"lang":"en","type":"article","venue":"Sustainable Energy Grids and Networks","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer security; Computer science; Engineering","score_opus":0.00903893137782898,"score_gpt":0.22026929406605392,"score_spread":0.21123036268822493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412448367","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":[0.23179504,0.026370592,0.6983076,0.00023119748,0.0019913486,0.00042593587,0.000003949189,0.0010337272,0.03984062],"genre_scores_gemma":[0.97284037,0.00044681155,0.00028905613,0.000098934084,0.0005312751,0.000084443986,0.000053542622,0.000058181045,0.025597405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834967,0.000040356095,0.0003278932,0.00032487672,0.00012800483,0.0008291984],"domain_scores_gemma":[0.9993287,0.00020798795,0.000052406995,0.00010681085,0.00020165445,0.0001024675],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00033891667,0.0002916924,0.00033574432,0.00012621963,0.00072959776,0.00026472373,0.00010558284,0.00021216259,0.000021707376],"category_scores_gemma":[0.00008437982,0.00029493953,0.0001122823,0.0003893707,0.00005455668,0.0001905764,0.00007918794,0.0002758755,2.315369e-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":[0.00014872484,0.000010555715,0.00021658908,0.00027198254,0.00012792452,0.000042444586,0.00017472505,0.95052546,0.00008812537,0.013950288,0.0072736647,0.027169522],"study_design_scores_gemma":[0.0009659876,0.00006446591,0.00003960585,0.000176666,0.00004257806,0.000009417568,0.0007795773,0.82629496,0.0002724129,0.00043495078,0.17062758,0.00029178945],"about_ca_topic_score_codex":0.00028332087,"about_ca_topic_score_gemma":0.000141322,"teacher_disagreement_score":0.7410453,"about_ca_system_score_codex":0.00016521724,"about_ca_system_score_gemma":0.00008469372,"threshold_uncertainty_score":0.9999503},"labels":[],"label_agreement":null},{"id":"W4412454847","doi":"10.1016/j.enconman.2025.120185","title":"Intelligent algorithm-assisted short-term load economic distribution at the unit level of a cascaded hydropower station","year":2025,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Term (time); Hydropower; Unit (ring theory); Algorithm; Computer science; Engineering; Environmental science; Electrical engineering; Mathematics; Physics","score_opus":0.02360528906585494,"score_gpt":0.2388357530135332,"score_spread":0.21523046394767825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412454847","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":[0.45598882,0.0020015093,0.49170783,0.00045481842,0.0030996208,0.00041736712,0.0002376254,0.0002748996,0.045817524],"genre_scores_gemma":[0.99116796,0.0015195585,0.0001177183,0.000058865306,0.00001537068,0.000012531944,0.00021401179,0.000009013276,0.006884956],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99941677,0.000020646203,0.0001974091,0.00014119338,0.000090944326,0.00013304065],"domain_scores_gemma":[0.9997409,0.000028234439,0.000029088433,0.00014862348,0.000019228872,0.000033953955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010323149,0.00011707222,0.00010956819,0.000062423176,0.00009253516,0.000018267303,0.00008696583,0.000043534365,0.0000900347],"category_scores_gemma":[0.0000013299107,0.000097877426,0.0000434262,0.000094270064,0.000044066983,0.00005164669,0.00012008146,0.00004450425,0.0000054574257],"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":[0.000079999474,0.00006590281,0.0005103971,0.00039524393,0.0007098703,0.000026033287,0.00043126894,0.044602457,0.0014391639,0.04091433,0.030649344,0.880176],"study_design_scores_gemma":[0.000954836,0.000050316936,0.0055545056,0.00021400585,0.00016575574,0.000005907356,0.00058172556,0.26853392,0.055233333,0.00015956507,0.66820073,0.00034540353],"about_ca_topic_score_codex":0.00014441015,"about_ca_topic_score_gemma":0.00021195703,"teacher_disagreement_score":0.8798306,"about_ca_system_score_codex":0.00022296772,"about_ca_system_score_gemma":0.000009939375,"threshold_uncertainty_score":0.39913255},"labels":[],"label_agreement":null},{"id":"W4412525713","doi":"10.2139/ssrn.5360013","title":"Forecasting Renewable Energy Consumption in Angola, Canada, France, and Nigeria Using Arima and Grey-Box Hybrid Models","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Autoregressive integrated moving average; Renewable energy; Box–Jenkins; Consumption (sociology); Energy consumption; Environmental economics; Business; Geography; Economics; Economy; Agricultural economics; Time series; Engineering; Statistics; Sociology; Electrical engineering; Mathematics; Social science","score_opus":0.013125194329894884,"score_gpt":0.20384447241309137,"score_spread":0.19071927808319647,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412525713","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":[0.8884365,0.042990286,0.06609806,0.00002278509,0.0008980728,0.00010325015,0.000052774354,0.00006150909,0.001336761],"genre_scores_gemma":[0.98743194,0.0111238025,0.0006277395,0.00003578248,0.00023933296,0.0000082549495,0.000023672686,0.00004924648,0.00046020586],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971547,0.000060103237,0.0005187286,0.00035988053,0.00020175146,0.0017048103],"domain_scores_gemma":[0.99943465,0.000059019254,0.00016358457,0.0001714725,0.00005314974,0.000118127835],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00061355316,0.0003930127,0.00048136126,0.00030456253,0.00018715484,0.0001350838,0.0001797514,0.00020194656,0.000005252463],"category_scores_gemma":[0.000024199493,0.00044341446,0.00005784884,0.00012290418,0.00003558328,0.00021148958,0.00016954799,0.002167253,4.0300076e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00001748249,0.000006704215,0.005654052,0.00021919809,0.00016963287,0.00003271679,0.00011213866,0.980751,0.00017336568,0.001137125,0.00006685966,0.011659723],"study_design_scores_gemma":[0.00055518793,0.000022896354,0.00010771545,0.0010058008,0.00004989043,0.0007941321,0.00009819934,0.9485673,0.00033088512,0.04740556,0.0005321556,0.0005303075],"about_ca_topic_score_codex":0.22581078,"about_ca_topic_score_gemma":0.7314852,"teacher_disagreement_score":0.5056744,"about_ca_system_score_codex":0.0013161337,"about_ca_system_score_gemma":0.0021848253,"threshold_uncertainty_score":0.99980175},"labels":[],"label_agreement":null},{"id":"W4412665583","doi":"10.1016/j.energy.2025.137752","title":"Enhancing power grid stability with a hybrid framework for wind power forecasting: Integrating Kalman Filtering, Deep Residual Learning, and Bidirectional LSTM","year":2025,"lang":"en","type":"article","venue":"Energy","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Residual; Kalman filter; Stability (learning theory); Deep learning; Artificial intelligence; Wind power; Computer science; Power (physics); Power grid; Grid; Wind power forecasting; Machine learning; Electric power system; Engineering; Algorithm; Electrical engineering; Mathematics","score_opus":0.008773525209154918,"score_gpt":0.21791848905074818,"score_spread":0.20914496384159326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412665583","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":[0.71038103,0.0010008296,0.27015027,0.00004773688,0.0012331468,0.00013335135,0.000019066918,0.0005312683,0.016503299],"genre_scores_gemma":[0.97357583,0.00002009645,0.025353778,0.000067916764,0.0002646981,0.00004976917,0.000036988207,0.00007109224,0.00055980205],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99844974,0.00004851001,0.00037560897,0.00044081887,0.0001765042,0.0005087971],"domain_scores_gemma":[0.99895537,0.00056253147,0.000081538325,0.00019574133,0.000094744806,0.00011006228],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00030067028,0.00033897313,0.00031899798,0.00017474644,0.00032548822,0.00011306032,0.00012696412,0.00013442687,0.00013588452],"category_scores_gemma":[0.00040664937,0.00031401563,0.00007783913,0.0002596106,0.000075539414,0.00017097036,0.00007805077,0.00042479465,7.5697966e-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":[0.0018855686,0.00055594527,0.070541926,0.00242037,0.0029374498,0.0002472144,0.016393533,0.46285766,0.07157293,0.26301637,0.006379754,0.101191275],"study_design_scores_gemma":[0.0028339953,0.0016711113,0.0028640444,0.0039115385,0.00020880984,0.0002886914,0.003467593,0.16958152,0.43728185,0.011438943,0.36340225,0.003049648],"about_ca_topic_score_codex":0.00006161167,"about_ca_topic_score_gemma":0.00039548735,"teacher_disagreement_score":0.36570892,"about_ca_system_score_codex":0.000095733965,"about_ca_system_score_gemma":0.00005141853,"threshold_uncertainty_score":0.9999312},"labels":[],"label_agreement":null},{"id":"W4412921088","doi":"10.1007/978-3-031-94862-6_6","title":"Forecasting Energy Prices Using Machine Learning Algorithms: A Comparative Analysis","year":2025,"lang":"en","type":"book-chapter","venue":"International series in management science/operations research/International series in operations research & management science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Machine learning; Energy (signal processing); Artificial intelligence; Algorithm; Mathematics; Statistics","score_opus":0.08620425709324156,"score_gpt":0.38722175020879845,"score_spread":0.3010174931155569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412921088","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037265834,0.00040259783,0.009419581,0.0012759176,0.0022900233,0.001555295,0.00019349213,0.00022009065,0.98091644],"genre_scores_gemma":[0.44128767,0.009097593,0.050090056,0.00009071863,0.00045590603,0.0011284135,0.00095133245,0.000112395755,0.4967859],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9871751,0.0002383769,0.0016103595,0.0020850145,0.007115664,0.0017754531],"domain_scores_gemma":[0.9959341,0.0002761407,0.00009701491,0.0009971227,0.0023951067,0.00030049495],"candidate_categories":["metaepi_narrow","bibliometrics","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["sts"],"category_scores_codex":[0.009439671,0.00071459677,0.0006769236,0.021207582,0.0035344632,0.004043658,0.005605642,0.00020064885,0.0011541385],"category_scores_gemma":[0.00051771814,0.00077036466,0.00020658917,0.010138443,0.004601003,0.005819792,0.0041826386,0.0018910096,0.000045680048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":true,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004183367,0.00007469292,0.00016698231,0.00005471203,0.00039490423,0.00013177679,0.0006289336,0.59160715,0.00015741313,0.4043435,0.00013416742,0.002263947],"study_design_scores_gemma":[0.00043130392,0.000074083924,0.00023101005,0.0007043893,0.000050030718,0.000015731843,0.0022382643,0.91093326,0.00036674863,0.0030314815,0.08127736,0.0006463153],"about_ca_topic_score_codex":0.0020501576,"about_ca_topic_score_gemma":0.02021058,"teacher_disagreement_score":0.4841305,"about_ca_system_score_codex":0.0050685084,"about_ca_system_score_gemma":0.00050351274,"threshold_uncertainty_score":0.9997745},"labels":[],"label_agreement":null},{"id":"W4413079221","doi":"10.2172/2568419","title":"Graph-Learning-Assisted State and Event Tracking for Solar-Penetrated Power Grids with Heterogeneous Data Sources","year":2025,"lang":"en","type":"report","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Office of Energy Efficiency; Solar Energy Technologies Office; Northeastern University; Stony Brook University; Harvard University; York University; Office of Energy Efficiency and Renewable Energy; U.S. Department of Energy","keywords":"Computer science; Graph; Tracking (education); Artificial intelligence; Theoretical computer science; Psychology","score_opus":0.030637718377171712,"score_gpt":0.261788333581993,"score_spread":0.23115061520482127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413079221","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":[0.54315114,0.04600418,0.20986556,0.0000958497,0.008677998,0.0038786319,0.0037504218,0.005508267,0.17906794],"genre_scores_gemma":[0.97143096,0.0029289243,0.0022269725,0.000031401418,0.00022459021,0.000074599375,0.0036114946,0.00027934113,0.019191746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99768364,0.000036953956,0.00059116376,0.00070249906,0.00041959767,0.00056616665],"domain_scores_gemma":[0.9986555,0.0001819175,0.00017787567,0.0006079955,0.00023436856,0.0001423745],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005142472,0.000587833,0.0006580631,0.00029312103,0.00019465915,0.0002523612,0.0004038429,0.00030558268,0.00007288308],"category_scores_gemma":[0.00008588483,0.00048834,0.00011010018,0.00027206363,0.000050223254,0.0001641109,0.00016098474,0.0005743768,0.0000014450509],"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":[0.00032748128,0.00018828444,0.010042162,0.008337763,0.008081772,0.00053285377,0.00095051434,0.61769694,0.0011436718,0.00003814085,0.022784926,0.3298755],"study_design_scores_gemma":[0.0022298102,0.0009579344,0.0027791127,0.0044995183,0.0011602915,0.0011272623,0.0002683251,0.0777302,0.0041741813,0.000075105134,0.9015357,0.0034625712],"about_ca_topic_score_codex":0.00016805295,"about_ca_topic_score_gemma":0.0011552678,"teacher_disagreement_score":0.87875074,"about_ca_system_score_codex":0.000081591315,"about_ca_system_score_gemma":0.00024004477,"threshold_uncertainty_score":0.9997568},"labels":[],"label_agreement":null},{"id":"W4413159459","doi":"10.1007/978-3-031-94937-1_7","title":"Advanced Forecasting of CCPP Output Power Using Regression and Neural Network Models","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial neural network; Regression; Computer science; Econometrics; Statistics; Artificial intelligence; Mathematics","score_opus":0.052464129468286484,"score_gpt":0.26830660703749637,"score_spread":0.21584247756920988,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413159459","genre_codex":"other","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":[0.013099133,0.0061033685,0.23696613,0.00008196496,0.0012250966,0.0005332631,0.000036884085,0.0001946581,0.7417595],"genre_scores_gemma":[0.8488423,0.0026326792,0.14792514,0.0001198661,0.000032562944,0.0000059233334,0.00003608516,0.000015369798,0.00039007413],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990794,0.000009173523,0.00049295626,0.0001070973,0.00015230419,0.0001590407],"domain_scores_gemma":[0.99899364,0.00013565384,0.00015965327,0.00053159555,0.00013526529,0.000044180353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034010308,0.00015677254,0.00021131727,0.00038728243,0.00022878888,0.000087370565,0.00047099846,0.0000891955,0.0000013747201],"category_scores_gemma":[0.000015519963,0.00015309168,0.000025012014,0.00021165986,0.00030024862,0.0022546523,0.0006514228,0.00026365998,2.2793799e-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":[0.0000020758812,0.0000016319606,0.000024559431,0.0000694603,0.00000429038,1.101956e-7,0.00080108165,0.76790535,0.0000046124073,0.043947667,0.000039172846,0.18719997],"study_design_scores_gemma":[0.000118062715,0.000011759847,0.00002851911,0.0010603301,0.0000048288503,0.000008366569,0.000014995537,0.99254155,0.000008627301,0.0016933357,0.004373295,0.00013630102],"about_ca_topic_score_codex":0.0000039659626,"about_ca_topic_score_gemma":0.000003414501,"teacher_disagreement_score":0.8357432,"about_ca_system_score_codex":0.000046253,"about_ca_system_score_gemma":0.000048447524,"threshold_uncertainty_score":0.62428975},"labels":[],"label_agreement":null},{"id":"W4413177390","doi":"10.18280/ts.420414","title":"Enhanced Signal Processing-Based Load Forecasting in Smart Grids Using Artificial Neural Networks and Heuristic Optimization","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Energy Load and Power Forecasting","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":"Artificial neural network; Heuristic; Computer science; Artificial intelligence; Signal processing; Digital signal processing; Computer hardware","score_opus":0.018565907430427204,"score_gpt":0.22379108171771406,"score_spread":0.20522517428728684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413177390","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":[0.3112231,0.00027598173,0.6874937,0.000015031849,0.0001720355,0.00014552753,0.000002690049,0.00012255064,0.00054942374],"genre_scores_gemma":[0.99623775,0.000003296165,0.0034093882,0.000076852535,0.0001838842,0.000024051573,0.000026055646,0.000031069085,0.00000763346],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862766,0.000041032923,0.00047329464,0.00026504422,0.00018311056,0.00040985757],"domain_scores_gemma":[0.99965113,0.00009313529,0.00006312336,0.00006876303,0.000058485974,0.0000653309],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002628314,0.00024380801,0.00023343207,0.00018445312,0.00015300575,0.00012127833,0.00009051966,0.00009803212,0.00006499874],"category_scores_gemma":[0.000016127718,0.0002648904,0.000043461387,0.00044330859,0.00004786608,0.00017692262,0.000024153775,0.00021963626,3.369432e-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":[0.000053921827,0.00003219139,0.0007695303,0.00013239878,0.000013212639,0.000010377085,0.000100641446,0.9656041,0.0022891238,0.000060458602,0.000010409796,0.030923627],"study_design_scores_gemma":[0.0005985256,0.00004676716,0.00022126966,0.00036257535,0.00003233146,0.0000031080724,0.00003301045,0.9959301,0.0024692751,0.00004698024,0.000016895514,0.00023916166],"about_ca_topic_score_codex":0.000024362194,"about_ca_topic_score_gemma":0.00006913602,"teacher_disagreement_score":0.68501467,"about_ca_system_score_codex":0.00014114981,"about_ca_system_score_gemma":0.00006552015,"threshold_uncertainty_score":0.99998033},"labels":[],"label_agreement":null},{"id":"W4413193012","doi":"10.70389/pjai.100018","title":"Energy Load Forecasting with Machine Learning: Models, Metrics, and Future Directions","year":2025,"lang":"en","type":"article","venue":"Premier Journal of Artificial Intelligence","topic":"Energy Load and Power Forecasting","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Energy (signal processing); Computer science; Machine learning; Artificial intelligence; Mathematics; Statistics","score_opus":0.03195770946066959,"score_gpt":0.235228198383294,"score_spread":0.20327048892262442,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413193012","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":[0.057279225,0.027613258,0.8977212,0.00022165572,0.0015622393,0.00005873849,0.000004153795,0.00013604763,0.015403482],"genre_scores_gemma":[0.9909072,0.0031328462,0.0053824075,0.000020227382,0.00038292064,0.0000019425504,0.0000010772397,0.000021443791,0.00014991884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989687,0.0000419821,0.00044443418,0.00012368365,0.00020509765,0.00021605354],"domain_scores_gemma":[0.9992817,0.00017440622,0.00011830568,0.0000859846,0.0002429016,0.00009669325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038412912,0.0001622209,0.0002244532,0.00030671674,0.00017740669,0.00008046411,0.00014095196,0.00007921774,0.000016318696],"category_scores_gemma":[0.00009123616,0.00013339588,0.00005921174,0.0006584221,0.000047175112,0.00026949114,0.00003222964,0.000524455,5.337215e-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":[0.000061010593,0.000027432474,0.0004906313,0.000039867195,0.00013109007,0.000021673013,0.0011958154,0.32383966,0.00040538862,0.008304555,0.000052165502,0.6654307],"study_design_scores_gemma":[0.000046371584,0.0001000184,0.000011929133,0.00018928509,0.00006590007,0.00013916753,0.00051080267,0.93228143,0.01625524,0.0066793133,0.0435385,0.00018202669],"about_ca_topic_score_codex":0.00005089522,"about_ca_topic_score_gemma":0.00018552937,"teacher_disagreement_score":0.93362796,"about_ca_system_score_codex":0.00010632499,"about_ca_system_score_gemma":0.00005490148,"threshold_uncertainty_score":0.5439726},"labels":[],"label_agreement":null},{"id":"W4413271117","doi":"10.1016/j.segan.2025.101865","title":"Forecasting electricity prices with deep learning and dynamic sparse training","year":2025,"lang":"en","type":"article","venue":"Sustainable Energy Grids and Networks","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electricity; Training (meteorology); Artificial intelligence; Economics; Computer science; Engineering; Meteorology; Electrical engineering; Geography","score_opus":0.004315526837164558,"score_gpt":0.18450544106184336,"score_spread":0.1801899142246788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413271117","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":[0.56451553,0.028403651,0.36520016,0.00004123293,0.00025655614,0.0001355577,5.325479e-7,0.00057455304,0.0408722],"genre_scores_gemma":[0.99514586,0.0016811292,0.00074577413,0.000039692837,0.00010427629,0.000023660281,0.0000114380055,0.000039707385,0.0022084892],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986917,0.000024291354,0.00019821967,0.00028054873,0.00008338326,0.00072185224],"domain_scores_gemma":[0.99955046,0.00016292649,0.00004534958,0.00008586101,0.000055039356,0.00010038733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022266236,0.0002540149,0.00026554716,0.00017846172,0.0004264582,0.00014044465,0.00007381643,0.00013262856,0.0000044891785],"category_scores_gemma":[0.00002829274,0.000228433,0.000027511687,0.0005748841,0.00006387639,0.00019432504,0.00006871925,0.00034807634,4.3172513e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000033761276,0.0000065883987,0.001977158,0.00016588597,0.00009548256,0.00008057939,0.00038785025,0.7648736,0.000020755198,0.012268022,0.000033509514,0.22005676],"study_design_scores_gemma":[0.00036919472,0.00009564196,0.00028783936,0.00014709464,0.00003995408,0.0000364919,0.0016939185,0.98067284,0.00004813235,0.00067909615,0.015641872,0.00028793144],"about_ca_topic_score_codex":0.00009769732,"about_ca_topic_score_gemma":0.00018388261,"teacher_disagreement_score":0.4306303,"about_ca_system_score_codex":0.00006516853,"about_ca_system_score_gemma":0.00002919443,"threshold_uncertainty_score":0.9315227},"labels":[],"label_agreement":null},{"id":"W4413836895","doi":"10.3390/forecast7030046","title":"Improving Dry-Bulb Air Temperature Prediction Using a Hybrid Model Integrating Genetic Algorithms with a Fourier–Bessel Series Expansion-Based LSTM Model","year":2025,"lang":"en","type":"article","venue":"Forecasting","topic":"Energy Load and Power Forecasting","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 Prince Edward Island","funders":"King Faisal University","keywords":"Bessel function; Fourier series; Series (stratigraphy); Algorithm; Genetic algorithm; Fourier transform; Computer science; Mathematics; Machine learning; Mathematical analysis; Geology","score_opus":0.010978955020368197,"score_gpt":0.19803432357589396,"score_spread":0.18705536855552576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413836895","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":[0.4836207,0.0002309099,0.51425874,0.000015687132,0.00030439775,0.00020507345,0.00006819754,0.0005940663,0.0007022233],"genre_scores_gemma":[0.69845414,0.0000045010192,0.30091983,0.00006833146,0.00018899808,0.00006324454,0.000053258616,0.00011090212,0.00013680084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976186,0.000035270965,0.0006339233,0.0005818359,0.00035315676,0.0007771865],"domain_scores_gemma":[0.99898124,0.00010112884,0.00015102729,0.00040695872,0.00021236377,0.0001472683],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00026038993,0.0005837288,0.0004490205,0.0003606678,0.0006295724,0.00018439295,0.0002533721,0.00020856137,0.000003156136],"category_scores_gemma":[0.00013215067,0.00053678965,0.00013337596,0.00062259624,0.00007808892,0.0005689092,0.00008780695,0.000692726,6.6852834e-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":[0.0000395102,0.000018542318,0.0005505031,0.00035615393,0.000058452257,0.000028040573,0.00046145686,0.9610099,0.01915741,0.00007006297,0.00005584373,0.01819409],"study_design_scores_gemma":[0.00060997385,0.00007443785,0.000015875361,0.00159243,0.00010258072,0.00005931397,0.00027590635,0.97880477,0.01758634,0.00036325675,0.000017671251,0.00049746013],"about_ca_topic_score_codex":0.00009014849,"about_ca_topic_score_gemma":0.00009175787,"teacher_disagreement_score":0.21483344,"about_ca_system_score_codex":0.0002537167,"about_ca_system_score_gemma":0.00036436305,"threshold_uncertainty_score":0.99970835},"labels":[],"label_agreement":null},{"id":"W4413978672","doi":"10.1109/incet64471.2025.11140948","title":"AI-Powered Decentralized Energy Trading Platform with BiLSTM-based Generation Prediction and Carbon Footprint Analysis","year":2025,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Carbon footprint; Footprint; Computer science; Energy (signal processing); Electricity generation; Carbon fibers; Artificial intelligence; Power (physics); Greenhouse gas; Ecology; Geology","score_opus":0.008929226695031696,"score_gpt":0.19638366046706354,"score_spread":0.18745443377203186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413978672","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":[0.77958983,0.00044242272,0.20767511,0.00007514703,0.0002484118,0.00007218842,0.0000045027464,0.00044371944,0.011448649],"genre_scores_gemma":[0.9981684,0.00007104518,0.0014555772,0.00008056825,0.000039395574,0.000018791075,0.000062911495,0.000015675074,0.000087645465],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99926436,0.000011168197,0.00021316101,0.00019849026,0.000108226646,0.00020458856],"domain_scores_gemma":[0.99969995,0.000027139562,0.000022154705,0.00014369654,0.000027796083,0.000079269004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007445103,0.00015932643,0.00019626762,0.0003425494,0.000068964546,0.000065313376,0.000046425863,0.00008273824,0.000020403379],"category_scores_gemma":[0.0000065357826,0.00013472015,0.000052802752,0.00066400203,0.000017621911,0.000055103217,0.000007918107,0.000076886565,4.8745232e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000970224,0.000047757505,0.039629877,0.00009754447,0.0018371468,0.000008728116,0.00027526816,0.85740006,0.0481377,0.011468306,0.00011317773,0.040887404],"study_design_scores_gemma":[0.0005724877,0.000030606127,0.00094525295,0.000028628596,0.00026105504,0.0000011444916,0.000017711269,0.9324235,0.064198114,0.000060038463,0.0013254229,0.00013604424],"about_ca_topic_score_codex":0.00024519232,"about_ca_topic_score_gemma":0.0013832949,"teacher_disagreement_score":0.21857853,"about_ca_system_score_codex":0.00007716502,"about_ca_system_score_gemma":0.000027601813,"threshold_uncertainty_score":0.5493728},"labels":[],"label_agreement":null},{"id":"W4414000613","doi":"10.18280/jesa.580712","title":"Evolution of AI in Grid-Connected Renewable Energy Systems: A Systematic Literature Mapping","year":2025,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Energy Load and Power Forecasting","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":"Renewable energy; Grid; Computer science; Engineering; Geography; Electrical engineering","score_opus":0.007924193968329354,"score_gpt":0.20878981757700854,"score_spread":0.20086562360867918,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414000613","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":[0.36022896,0.27741954,0.30362588,0.000094384195,0.015529709,0.0010781117,0.00006454734,0.0019416256,0.040017225],"genre_scores_gemma":[0.99752307,0.0003373601,0.00077239715,0.000020409245,0.00024510632,0.000026948734,0.0000065178015,0.000046245368,0.001021975],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742657,0.00036180098,0.0013213177,0.00018185267,0.00028892505,0.00041952357],"domain_scores_gemma":[0.99891675,0.00019662005,0.00028959758,0.00027845582,0.00022666143,0.00009190759],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00064651616,0.00029041406,0.0007476929,0.0010500342,0.00012920778,0.00025523637,0.00030307227,0.0001734234,0.000008136678],"category_scores_gemma":[0.00026461878,0.00025637174,0.00014654057,0.0016593725,0.00003555297,0.00042001938,0.000047365684,0.0003839891,0.0000035366484],"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":[0.000016288534,0.00005754325,0.0023908594,0.08425174,0.00041078668,0.000243155,0.0016944259,0.8870807,0.009732131,0.0072145173,0.0053745285,0.0015333128],"study_design_scores_gemma":[0.0007706056,0.00005677581,0.015979426,0.17000623,0.0000874038,0.0009441331,0.0005526345,0.80802006,0.00065010105,0.0019504648,0.00058410765,0.00039807832],"about_ca_topic_score_codex":0.00029794383,"about_ca_topic_score_gemma":0.00013189392,"teacher_disagreement_score":0.63729405,"about_ca_system_score_codex":0.0005988992,"about_ca_system_score_gemma":0.00010872286,"threshold_uncertainty_score":0.99998885},"labels":[],"label_agreement":null},{"id":"W4414015618","doi":"10.11159/eee25.134","title":"Effectiveness of Time Series Models in Consumption Forecasting for Photovoltaic Energy with Limited Historical Data: SARIMA and Prophet","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Universidade Federal de Itajubá; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Photovoltaic system; Time series; Energy consumption; Series (stratigraphy); Computer science; Consumption (sociology); Data modeling; Energy (signal processing); Autoregressive integrated moving average; Machine learning; Engineering; Statistics; Electrical engineering; Mathematics; Geology","score_opus":0.012764622805241464,"score_gpt":0.19401736148345577,"score_spread":0.18125273867821431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414015618","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":[0.9887166,0.0014169122,0.009041883,0.000010674419,0.00038706104,0.00026601725,0.000004387891,0.00005650273,0.000099934754],"genre_scores_gemma":[0.9989005,0.00003855005,0.00095088035,0.0000023447749,0.000019434596,0.000024261612,5.1773753e-7,0.000008388121,0.000055164262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992474,0.0000048896836,0.00019421478,0.00024562207,0.00012131672,0.00018657863],"domain_scores_gemma":[0.99956095,0.00019039263,0.000051555908,0.00007642424,0.00008136023,0.000039309518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003654569,0.00012705424,0.00026777663,0.00025582185,0.00006654919,0.00006208756,0.0001867035,0.00003227062,2.8617285e-8],"category_scores_gemma":[0.000029307801,0.00009105858,0.000012205761,0.00057352975,0.00008308194,0.00024183707,0.00009284526,0.0000920725,4.1760826e-9],"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":[0.00073271076,0.00013211051,0.017565481,0.013352134,0.00020354475,0.000003612931,0.00032135443,0.6510361,0.16589259,0.123703614,0.0002579495,0.026798828],"study_design_scores_gemma":[0.000301702,0.00010734424,0.0007054972,0.0014415575,0.000011459772,0.000015001594,0.0000016643911,0.989388,0.007771978,0.0000610524,0.00009457026,0.00010020305],"about_ca_topic_score_codex":0.000032448723,"about_ca_topic_score_gemma":0.0000030138906,"teacher_disagreement_score":0.3383519,"about_ca_system_score_codex":0.000057020457,"about_ca_system_score_gemma":0.000016721358,"threshold_uncertainty_score":0.37132612},"labels":[],"label_agreement":null},{"id":"W4414015832","doi":"10.11159/eee25.112","title":"Deep Learning-Based Approaches for Short-Term Residential Electricity Consumption Prediction: A Comparative Study of LSTM, CNN-LSTM, and CNN-GRU Models","year":2025,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Computer science; Deep learning; Term (time); Artificial intelligence; Long short term memory; Consumption (sociology); Machine learning; Electricity; Convolutional neural network; Artificial neural network; Recurrent neural network; Engineering; Electrical engineering","score_opus":0.025090844441802814,"score_gpt":0.22952050533610435,"score_spread":0.20442966089430153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414015832","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":[0.96911776,0.0009291066,0.028780522,0.000009063623,0.00048567285,0.00045303168,0.0000014727369,0.00008787878,0.00013546302],"genre_scores_gemma":[0.9995848,0.000021578802,0.00022304831,0.0000027752506,0.0000521933,0.00004957554,3.5923802e-7,0.0000082319,0.00005742566],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99892896,0.00000896168,0.00030571473,0.00029716227,0.00022197801,0.0002372455],"domain_scores_gemma":[0.9995407,0.00013422804,0.00007217319,0.00006752081,0.0001206257,0.00006476613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033726584,0.00017337919,0.00032650275,0.00033340076,0.00019853127,0.00013326068,0.00019239061,0.00004340202,9.368847e-8],"category_scores_gemma":[0.000019125393,0.00013825724,0.000031827058,0.0006372325,0.00010951512,0.00016221598,0.0000598999,0.0002047931,1.5609672e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006877366,0.00008707081,0.011157078,0.00073111436,0.00008821623,2.829699e-7,0.0005074997,0.96928567,0.0020026655,0.012989932,0.000049169295,0.0030325525],"study_design_scores_gemma":[0.0004293089,0.00028002687,0.005560873,0.00032772543,0.00003513443,0.0000040567274,0.000030362333,0.99082786,0.0023365319,0.000025726287,0.000023008148,0.000119390315],"about_ca_topic_score_codex":0.000008782671,"about_ca_topic_score_gemma":0.0000036184456,"teacher_disagreement_score":0.030467024,"about_ca_system_score_codex":0.00003247834,"about_ca_system_score_gemma":0.000014894698,"threshold_uncertainty_score":0.5637967},"labels":[],"label_agreement":null},{"id":"W4414053898","doi":"10.1175/waf-d-25-0069.1","title":"Comparative Analysis of Ensemble and Deterministic Models for Fire Weather Index (FWI) System Forecasting","year":2025,"lang":"en","type":"article","venue":"Weather and Forecasting","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Environment and Climate Change Canada; Natural Resources Canada; Canadian Forest Service; Thompson Rivers University","funders":"","keywords":"Ensemble forecasting; Lead time; Forecast skill; Ensemble average; Index (typography); Numerical weather prediction; Forecast verification; Global Forecast System","score_opus":0.03507422548803228,"score_gpt":0.24541251838313968,"score_spread":0.21033829289510741,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414053898","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":[0.7702545,0.0020878667,0.17621249,0.000005317062,0.00019867231,0.000318992,0.0000641793,0.00017719668,0.050680816],"genre_scores_gemma":[0.9967393,0.000017872808,0.002829581,0.000010079241,0.000049011975,0.000052518408,0.000014966161,0.000029702616,0.00025691863],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986828,0.000026741902,0.00051744457,0.00030921967,0.000107198175,0.00035659454],"domain_scores_gemma":[0.99902934,0.0005200742,0.00012092043,0.00017083956,0.0000823572,0.00007645169],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002864764,0.0002799871,0.00072371983,0.00031632982,0.00019382242,0.00005938922,0.000101166,0.00012445121,0.0000037639547],"category_scores_gemma":[0.000035605495,0.00026290413,0.0001453292,0.00053642265,0.00007151526,0.0001401597,0.0000587439,0.00012249149,1.7354944e-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":[0.00014375674,0.000043815882,0.01663185,0.0026148667,0.0033756113,0.00001174215,0.007184904,0.8225208,0.0018976498,0.020460062,0.00007948746,0.12503548],"study_design_scores_gemma":[0.00047861278,0.000047248912,0.00046183894,0.00050327904,0.0006030478,0.0000115042285,0.001216412,0.9951856,0.000465269,0.00069079566,0.00009423301,0.00024213985],"about_ca_topic_score_codex":0.000052835716,"about_ca_topic_score_gemma":0.00009558676,"teacher_disagreement_score":0.22648488,"about_ca_system_score_codex":0.000039125385,"about_ca_system_score_gemma":0.000016709157,"threshold_uncertainty_score":0.9999823},"labels":[],"label_agreement":null},{"id":"W4414172042","doi":"10.1016/j.compeleceng.2025.110689","title":"Wind power forecasting: A hybrid multi-layer perceptron framework with adaptive noise reduction and error correction","year":2025,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Noise (video); Reduction (mathematics); Control theory (sociology); Noise reduction; Wind power; Power (physics); Perceptron","score_opus":0.012237322858127607,"score_gpt":0.20792539296737456,"score_spread":0.19568807010924694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414172042","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":[0.29425213,0.0005044925,0.7028484,0.000020101506,0.0014313275,0.00014023334,0.0000010155305,0.00053306913,0.00026923028],"genre_scores_gemma":[0.9616403,0.000020729693,0.038001966,0.000024004725,0.00015085528,0.000014070889,0.0000051719826,0.00005541585,0.00008745932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99881536,0.000014914308,0.00023567368,0.00033996897,0.0001321761,0.00046192692],"domain_scores_gemma":[0.9994965,0.00014283435,0.00003071969,0.00015943623,0.000047233072,0.00012327444],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007864529,0.0003127374,0.0002849705,0.0002980061,0.00010639538,0.000071110204,0.00010884644,0.00012267704,0.000005334376],"category_scores_gemma":[0.00004840694,0.00031035216,0.000059106686,0.0005970802,0.000026921341,0.00017660801,0.000038790655,0.00059817557,0.0000023777911],"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":[0.00005170733,0.000029166513,0.00012121638,0.000039684728,0.00010819162,0.000017707289,0.00035721154,0.96792346,0.0029669227,0.00031483395,0.00051528215,0.027554609],"study_design_scores_gemma":[0.0004240962,0.00016453222,0.001277759,0.00037780782,0.00003569463,0.00014332955,0.000024943023,0.9944846,0.0019665556,0.000019551533,0.0007471144,0.00033403313],"about_ca_topic_score_codex":0.000009418899,"about_ca_topic_score_gemma":9.645198e-7,"teacher_disagreement_score":0.6673882,"about_ca_system_score_codex":0.00018689934,"about_ca_system_score_gemma":0.00002157103,"threshold_uncertainty_score":0.99993485},"labels":[],"label_agreement":null},{"id":"W4414184187","doi":"10.38088/jise.1635104","title":"Short-Term Electricity Load Forecasting and Seasonality Analysis Using Temperature and Artificial Intelligence Methods in the Southeastern Anatolia Region","year":2025,"lang":"en","type":"article","venue":"Journal of Innovative Science and Engineering (JISE)","topic":"Energy Load and Power Forecasting","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":"Safran Electronics (Canada)","funders":"","keywords":"Autoregressive integrated moving average; Artificial neural network; Electricity; Energy consumption; Demand forecasting; Linear regression; Time series; Seasonality","score_opus":0.045142551349661185,"score_gpt":0.32213193885823715,"score_spread":0.276989387508576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414184187","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":[0.8961861,0.0008010657,0.1027059,0.000050744773,0.00012529358,0.000046077283,0.000001017987,0.000011221016,0.000072566494],"genre_scores_gemma":[0.99159014,0.000060652754,0.008260303,0.000029046594,0.000051438084,9.989382e-7,2.2728261e-7,0.000005988255,0.0000011910863],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988258,0.00004080736,0.0004300258,0.00017215397,0.00026916363,0.00026205578],"domain_scores_gemma":[0.99922425,0.00017984181,0.000079511934,0.00009105031,0.00036934862,0.000055979166],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025768697,0.00015914037,0.00028251266,0.0006090979,0.00014870946,0.00019590408,0.00017579063,0.00006245522,4.4297477e-7],"category_scores_gemma":[0.00042365654,0.00011761463,0.000033163178,0.005107327,0.00015214983,0.00037535437,0.00004997936,0.00046215195,1.4040319e-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":[0.000071527815,0.00005264528,0.2640488,0.0004209176,0.00048753,0.00012590554,0.010217093,0.22971793,0.22561333,0.0075339302,0.000009316555,0.26170105],"study_design_scores_gemma":[0.0000857367,0.00004585732,0.13248186,0.00037739857,0.000091595415,0.00020257155,0.0010759224,0.85207206,0.012770826,0.00053433987,0.000021450907,0.00024040703],"about_ca_topic_score_codex":0.0000116766305,"about_ca_topic_score_gemma":0.000008880404,"teacher_disagreement_score":0.6223541,"about_ca_system_score_codex":0.000081946215,"about_ca_system_score_gemma":0.00008478794,"threshold_uncertainty_score":0.47961855},"labels":[],"label_agreement":null},{"id":"W4414305124","doi":"10.2139/ssrn.5500798","title":"Temporal Deep Explainer: A Model-Agnostic Feature Attribution Approach for Interpretable Time-Series Load Forecasting","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Robustness (evolution); Deep learning; Energy (signal processing); Feature (linguistics); Artificial neural network; Feature learning; Convolution (computer science); Pattern recognition (psychology)","score_opus":0.011838480560692765,"score_gpt":0.2150521319787387,"score_spread":0.20321365141804595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414305124","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":[0.004072247,0.012232869,0.9766149,0.00009193181,0.0008487148,0.0004965871,0.0001366514,0.0003658169,0.005140318],"genre_scores_gemma":[0.92184746,0.003378735,0.057348322,0.00005071048,0.0016648653,0.0003532543,0.0012244043,0.00022790206,0.013904345],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9955903,0.000059708316,0.0006161531,0.00050163036,0.00034603354,0.0028861393],"domain_scores_gemma":[0.99887276,0.000112955764,0.00026029957,0.00035085424,0.0002685083,0.00013463378],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0014688482,0.000675412,0.0007213846,0.00025684197,0.0003611646,0.00022837463,0.0006176936,0.00065368327,0.000008475329],"category_scores_gemma":[0.00020379362,0.00067110267,0.00046989138,0.0002162138,0.000041093645,0.00033433316,0.00026723684,0.0050381436,0.000002850569],"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":[0.0001152208,0.000028795726,0.000059416576,0.00044702194,0.0005091492,0.0000029474022,0.00031291944,0.9841772,0.00011446841,0.004607857,0.0009032834,0.008721746],"study_design_scores_gemma":[0.00053474994,0.00012643277,8.9858935e-7,0.00058531266,0.00021507044,0.0003260274,0.00014511227,0.95968956,0.00025023453,0.035842728,0.0016715509,0.00061230775],"about_ca_topic_score_codex":0.000022877162,"about_ca_topic_score_gemma":0.00022221215,"teacher_disagreement_score":0.9192665,"about_ca_system_score_codex":0.0027841646,"about_ca_system_score_gemma":0.002311754,"threshold_uncertainty_score":0.999574},"labels":[],"label_agreement":null},{"id":"W4414435037","doi":"10.3233/faia250569","title":"Energy Audits Based on Risk Prediction and AI","year":2025,"lang":"en","type":"book-chapter","venue":"Frontiers in artificial intelligence and applications","topic":"Energy Load and Power Forecasting","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Audit; Energy consumption; Efficient energy use; Process (computing); Sustainability; Energy (signal processing); Energy management; Consumption (sociology)","score_opus":0.012786001726242023,"score_gpt":0.21652149267409634,"score_spread":0.20373549094785431,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414435037","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":[0.000009976455,0.0013549201,0.83080846,0.000060398106,0.00043781646,0.00016199386,0.00016206133,0.00011560509,0.16688877],"genre_scores_gemma":[0.660499,0.072234884,0.033200335,0.0022310491,0.0051475307,0.0025642838,0.002055427,0.0006120847,0.22145545],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918544,0.000007050318,0.00029977562,0.00028141032,0.0000846299,0.00014171039],"domain_scores_gemma":[0.99962324,0.00005939996,0.000048960545,0.00018811488,0.000023530485,0.00005675456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000073887946,0.00020313753,0.00019526617,0.00028126902,0.00011387425,0.00003962437,0.00008264136,0.00022493758,0.000018402669],"category_scores_gemma":[0.0000072831854,0.00022630199,0.000037547747,0.00008283521,0.00008582199,0.000040450897,0.000017788656,0.00030642818,0.000004544658],"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":[0.000008391417,0.00001156524,0.000087736946,0.000042768024,0.00002367073,9.553909e-7,0.000034374658,0.03704096,0.0000058752094,0.18867788,0.004967335,0.7690985],"study_design_scores_gemma":[0.000019814792,0.000027206592,0.000010404339,0.00023632808,0.000040670115,5.253221e-7,0.000022526296,0.5136576,0.0005214263,0.13849954,0.3467336,0.00023033176],"about_ca_topic_score_codex":0.000018413972,"about_ca_topic_score_gemma":0.00006501429,"teacher_disagreement_score":0.79760814,"about_ca_system_score_codex":0.000045865658,"about_ca_system_score_gemma":0.00002063056,"threshold_uncertainty_score":0.9228327},"labels":[],"label_agreement":null},{"id":"W4414545923","doi":"10.1007/978-981-96-6932-5_45","title":"Evaluating the Scalability and Suitability of Deep Learning Models for Energy Consumption Forecasting in Smart Homes","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Scalability; Energy consumption; Mean squared error; Deep learning; Artificial neural network; Inference; Consumption (sociology); Resource (disambiguation)","score_opus":0.04335464863000997,"score_gpt":0.2555485406625829,"score_spread":0.21219389203257294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414545923","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":[0.07634418,0.12201524,0.7889974,0.000022256268,0.0014045386,0.001282934,0.000027977429,0.00013045642,0.00977504],"genre_scores_gemma":[0.998406,0.0007786176,0.0003221541,0.000007431606,0.0001638863,0.000062661384,0.000033622113,0.000038583472,0.00018702209],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842656,0.00010485919,0.0006829859,0.00035348683,0.00013492558,0.00029720098],"domain_scores_gemma":[0.99666756,0.0028722351,0.00016249012,0.00019660914,0.00006622228,0.000034889603],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0014250277,0.00031533322,0.00063259475,0.00012324861,0.000101403544,0.000054476193,0.00009332623,0.0004564094,0.0000031923464],"category_scores_gemma":[0.00023228614,0.0002524989,0.000077252975,0.00006883313,0.00009199019,0.00005961859,0.00006324555,0.0005343952,1.7738872e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000023224437,0.0000022710437,0.0052427268,0.0010255761,0.00003081282,5.697828e-7,0.0002313312,0.853267,0.000006825807,0.0027006904,9.908593e-7,0.13746797],"study_design_scores_gemma":[0.000278496,0.000055797726,0.00017451342,0.0017411301,0.000034715707,0.0000064026403,0.00001085098,0.9908295,0.0000052056016,0.0065252134,0.00012415585,0.00021398734],"about_ca_topic_score_codex":0.00017353882,"about_ca_topic_score_gemma":0.0015566663,"teacher_disagreement_score":0.92206186,"about_ca_system_score_codex":0.000081955935,"about_ca_system_score_gemma":0.000015852444,"threshold_uncertainty_score":0.9999927},"labels":[],"label_agreement":null},{"id":"W4414730989","doi":"10.1007/978-3-032-02728-3_18","title":"SEF-Net: A Hybrid Deep Learning Architecture for Multi-step Forecasting in Sustainable Energy Markets","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Deep learning; Convolutional neural network; Energy (signal processing); Architecture; Artificial neural network; Time series; Transformer; Demand forecasting; Sustainable energy","score_opus":0.0120697148881671,"score_gpt":0.21484109084034114,"score_spread":0.20277137595217404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414730989","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00023087143,0.0015858228,0.9867689,0.000035067056,0.0008884458,0.00031015874,0.0000069905623,0.0002224307,0.00995132],"genre_scores_gemma":[0.45614195,0.0001532011,0.53163064,0.0005494182,0.0011059258,0.00012312306,0.000092037786,0.00027714818,0.0099265985],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99704605,0.000025701984,0.0005374245,0.0009251592,0.0003333451,0.0011323213],"domain_scores_gemma":[0.9984039,0.00081352354,0.00012537048,0.0004029897,0.0001357017,0.00011848083],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006716449,0.00062031293,0.00059203146,0.0012128646,0.00025024422,0.00021619906,0.0008121956,0.00031008813,0.00001419968],"category_scores_gemma":[0.00029421854,0.00063644437,0.00015661666,0.00048651887,0.00017929927,0.00017871795,0.0004009492,0.0010964129,9.3512557e-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":[0.000009880894,0.000004911327,0.000039203012,0.00023792629,0.000010489686,0.00009132317,0.00022581247,0.5712831,0.000021104477,0.00088369136,0.000011052893,0.4271815],"study_design_scores_gemma":[0.00045462223,0.000060471335,0.000016237593,0.0010773296,0.0000105843355,0.000053858443,0.0000014680257,0.97092736,0.00063874095,0.0066088936,0.019528864,0.0006215682],"about_ca_topic_score_codex":0.000080808575,"about_ca_topic_score_gemma":0.0008765536,"teacher_disagreement_score":0.45591107,"about_ca_system_score_codex":0.00041949848,"about_ca_system_score_gemma":0.00017419875,"threshold_uncertainty_score":0.9996087},"labels":[],"label_agreement":null},{"id":"W4414805080","doi":"10.1016/j.rineng.2025.107470","title":"Adaptive filter-driven optimized attention-based CNN-LSTM for load forecasting in microgrids","year":2025,"lang":"en","type":"article","venue":"Results in Engineering","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":11,"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 Regina","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Hyperparameter; Estimator; Adaptive sampling; Microgrid; Mean squared error; Artificial neural network; Gradient descent; Hyperparameter optimization; Adaptive filter","score_opus":0.0159964544400479,"score_gpt":0.2206482240702754,"score_spread":0.2046517696302275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414805080","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":[0.31985554,0.0014063416,0.6608336,0.00015460026,0.002861001,0.0012492943,0.00020201095,0.0011224672,0.012315113],"genre_scores_gemma":[0.9327216,0.000020771888,0.06674045,0.000018455275,0.000098453536,0.0001616575,0.000057520327,0.00005712303,0.00012396999],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99837875,0.000012994475,0.00061184214,0.00031698815,0.00012325408,0.0005561425],"domain_scores_gemma":[0.99912155,0.00050565164,0.00004285205,0.00022318208,0.00005132023,0.000055442873],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037071705,0.0002834121,0.00034739423,0.00047952723,0.00003791801,0.000040566978,0.00020541456,0.00015965803,0.0000034010197],"category_scores_gemma":[0.00031626664,0.0003284955,0.0001190733,0.0006295848,0.000015281703,0.00012326999,0.000037194644,0.00031235814,0.00000207564],"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":[0.000108657216,0.000022468215,0.00026118432,0.00022936192,0.000029788294,0.00001536788,0.0001561217,0.9919782,0.0025188508,0.00011868413,0.0001279668,0.004433296],"study_design_scores_gemma":[0.003483418,0.00003023335,0.0005243186,0.0014444708,0.000010968675,0.0000015740007,0.000044938643,0.989091,0.0034793478,0.000019554544,0.0015671537,0.00030303077],"about_ca_topic_score_codex":0.00004301697,"about_ca_topic_score_gemma":0.000092230555,"teacher_disagreement_score":0.61286604,"about_ca_system_score_codex":0.00039198677,"about_ca_system_score_gemma":0.000055321154,"threshold_uncertainty_score":0.99991673},"labels":[],"label_agreement":null},{"id":"W4414863177","doi":"10.22214/ijraset.2025.74382","title":"An Optimized Machine Learning Approach for Electricity Price Prediction in Cloud Data Centers","year":2025,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Energy Load and Power Forecasting","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":"Cloud computing; Electricity; Scheduling (production processes); Boosting (machine learning); Data center; Schedule; Electricity price; Dynamism","score_opus":0.04385689680858317,"score_gpt":0.3456688824358052,"score_spread":0.301811985627222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414863177","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":[0.122137055,0.000362786,0.8740123,0.00036110033,0.00090397586,0.0004899503,0.000029086861,0.0002248438,0.0014788868],"genre_scores_gemma":[0.9663271,0.00018537442,0.033246808,0.000007061777,0.000084719744,0.00009274069,0.000030464384,0.000012381392,0.000013403441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986989,0.000009105894,0.00024585528,0.0002741614,0.00030836533,0.0004636485],"domain_scores_gemma":[0.9994951,0.00012557291,0.000021203154,0.00015196782,0.0001495814,0.000056575936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003211737,0.00009168359,0.00012556366,0.0023387077,0.00014514959,0.00013368261,0.0010891018,0.00009333685,7.9901287e-7],"category_scores_gemma":[0.00043124368,0.000093050636,0.000012399619,0.0012530915,0.0001038434,0.00024515233,0.00016565264,0.00073407055,1.2969834e-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":[0.0001019615,0.000048973958,0.0007992945,0.000051490148,0.000025229163,0.0000037118853,0.000075189026,0.904215,0.050539643,0.014824625,0.00008875811,0.029226117],"study_design_scores_gemma":[0.0007958996,0.00004557306,0.00008089744,0.000054222,0.0000014488622,0.000020644666,0.000073271214,0.9912941,0.0024243174,0.0011082854,0.004029574,0.00007179692],"about_ca_topic_score_codex":0.000009208169,"about_ca_topic_score_gemma":0.000004293277,"teacher_disagreement_score":0.84419,"about_ca_system_score_codex":0.00032595007,"about_ca_system_score_gemma":0.00007294109,"threshold_uncertainty_score":0.3794495},"labels":[],"label_agreement":null},{"id":"W4414892236","doi":"10.1007/978-3-032-06665-7_33","title":"A GAN Approach for Energy Consumption Forecasting in Built Environment","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Range (aeronautics); Energy (signal processing); Mean squared error; Energy consumption; Ensemble forecasting; Time series; Ensemble learning; Predictive modelling","score_opus":0.022694002098754384,"score_gpt":0.19890452046836946,"score_spread":0.17621051836961507,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414892236","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":[0.00013673944,0.039105248,0.91711396,0.0000047102862,0.00076911197,0.00041926472,0.00002289204,0.00007429919,0.04235379],"genre_scores_gemma":[0.9913155,0.0027032145,0.0011200873,0.000036742797,0.00085207896,0.00018327705,0.0002885473,0.000107117354,0.003393428],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99864835,0.000019897856,0.0004993903,0.00036990835,0.00010331841,0.00035912992],"domain_scores_gemma":[0.99929935,0.00035919974,0.00009137988,0.00019049359,0.000009238971,0.00005035869],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00022222858,0.00039465263,0.00055789,0.00021090919,0.000049957212,0.0000577582,0.00010022093,0.0006677667,0.000006745791],"category_scores_gemma":[0.000013829013,0.0003855881,0.000080833015,0.000038292143,0.00003133624,0.000031239164,0.000031439697,0.0004169302,2.0782326e-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":[0.000011766588,0.0000040136233,0.00028442827,0.00060237915,0.00004205592,0.000007195763,0.00006328486,0.95712745,0.0000049536748,0.0027633202,0.000067692396,0.039021432],"study_design_scores_gemma":[0.00033627343,0.000023822406,0.000008132511,0.0014543685,0.00002496706,0.000017685561,0.0000021138671,0.9789232,0.0000065143763,0.0005898708,0.018258126,0.00035493026],"about_ca_topic_score_codex":0.00007773238,"about_ca_topic_score_gemma":0.00024825032,"teacher_disagreement_score":0.99117875,"about_ca_system_score_codex":0.00013085395,"about_ca_system_score_gemma":0.000009505816,"threshold_uncertainty_score":0.99985963},"labels":[],"label_agreement":null},{"id":"W4415176790","doi":"10.18280/jesa.580808","title":"Short-Term Wind Power Forecasting under Data Loss Conditions: A Case Study in Vietnam","year":2025,"lang":"en","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Đại học Huế; Hanoi University of Mining and Geology","keywords":"Wind power; Power loss; Wind speed; Weather forecasting; Power (physics)","score_opus":0.04441686336203442,"score_gpt":0.29564392291801056,"score_spread":0.25122705955597613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415176790","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":[0.9896529,0.001392589,0.0024592213,0.00002591649,0.0010356023,0.00029911808,0.000044500408,0.00031620642,0.004773989],"genre_scores_gemma":[0.9985832,0.000055713044,0.00090149476,0.00003862035,0.00013378206,0.000009588283,0.000018666404,0.00007146818,0.00018743983],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99761903,0.00020769,0.0009889891,0.00033997637,0.00029162195,0.0005526845],"domain_scores_gemma":[0.9987915,0.00023810474,0.00011613486,0.00061233406,0.00008942115,0.00015246472],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00081941066,0.00034856613,0.0004582205,0.00051719596,0.00039395763,0.0003941244,0.00057262956,0.0001027303,0.00010558403],"category_scores_gemma":[0.0001460769,0.00032955315,0.00008617313,0.00071474776,0.00007676071,0.0007990311,0.000271143,0.0006514503,0.000015914564],"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":[0.000039543756,0.00082498,0.33301997,0.00090097747,0.0014986866,0.09343724,0.008012601,0.30259272,0.0006208572,0.0007991053,0.0045714374,0.2536819],"study_design_scores_gemma":[0.0021635352,0.0002331149,0.5384076,0.0031907954,0.00024388894,0.05934319,0.0067082164,0.38679972,0.000039082894,0.0013055208,0.00061104615,0.0009542813],"about_ca_topic_score_codex":0.000038440056,"about_ca_topic_score_gemma":0.00041297302,"teacher_disagreement_score":0.2527276,"about_ca_system_score_codex":0.00027338712,"about_ca_system_score_gemma":0.00008452358,"threshold_uncertainty_score":0.99991566},"labels":[],"label_agreement":null},{"id":"W4415229228","doi":"10.1080/17477778.2025.2574719","title":"Uncertainty-aware energy forecasting and environmental impact simulation using Monte Carlo and deep learning","year":2025,"lang":"en","type":"article","venue":"Journal of Simulation","topic":"Energy Load and Power Forecasting","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deep learning; Monte Carlo method; Energy (signal processing); Environmental impact assessment; Simulation modeling; Technology forecasting","score_opus":0.013154731052139905,"score_gpt":0.2461647799513169,"score_spread":0.23301004889917698,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415229228","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":[0.91146445,0.0013657423,0.08694079,0.0000027784988,0.00013393626,0.000023635834,0.0000014020271,0.000017353272,0.000049882543],"genre_scores_gemma":[0.99941546,0.00008124227,0.00031293213,0.000007655391,0.00014893254,2.111855e-7,0.0000033131323,0.00001603576,0.000014245478],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993429,0.000030377169,0.00030597817,0.000077854835,0.00011165065,0.00013123234],"domain_scores_gemma":[0.9995353,0.00020889923,0.00013386461,0.00003858418,0.000028910319,0.000054396758],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015196268,0.00012095706,0.000164291,0.00018066609,0.00011837135,0.00005035927,0.000028594088,0.0000706424,0.000006116247],"category_scores_gemma":[0.00004694826,0.000113076814,0.00004975047,0.00009153088,0.000017314671,0.0003288736,0.000019454652,0.00015652242,4.7099263e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000017391209,0.0000035515775,0.011807357,0.000016981425,0.000043867014,0.0000042970523,0.00028621196,0.9311758,0.001363364,0.0000032911664,7.021353e-7,0.055277135],"study_design_scores_gemma":[0.00038988778,0.00004857714,0.002501544,0.00012975972,0.000042917058,0.00001955024,0.00013689455,0.9962619,0.00009649084,0.000034044173,0.00024334216,0.00009510304],"about_ca_topic_score_codex":0.00002523532,"about_ca_topic_score_gemma":0.000008956205,"teacher_disagreement_score":0.08795095,"about_ca_system_score_codex":0.00013544019,"about_ca_system_score_gemma":0.000008788508,"threshold_uncertainty_score":0.46111387},"labels":[],"label_agreement":null},{"id":"W4415305060","doi":"10.1016/j.scsadv.2025.100003","title":"Explainable Hybrid Deep Learning Model with Attention Mechanism for Short-Term Load Forecasting","year":2025,"lang":"en","type":"article","venue":"Sustainable Cities and Society Advances","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":4,"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":"Mitacs","keywords":"Interpretability; Deep learning; Energy consumption; Artificial neural network; Convolutional neural network; Mechanism (biology); Novelty; Smart grid; Energy (signal processing)","score_opus":0.007381336320361928,"score_gpt":0.20880067310177478,"score_spread":0.20141933678141286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415305060","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":[0.25847545,0.0062672053,0.7203342,0.00004230061,0.00015203493,0.00035026492,0.0000062708714,0.0003542255,0.014018043],"genre_scores_gemma":[0.97376615,0.0017346943,0.011808519,0.000051398354,0.00007043592,0.00018615031,0.000024203395,0.000041174786,0.012317296],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99882853,0.000008667326,0.00019051558,0.00024681413,0.00012611847,0.0005993402],"domain_scores_gemma":[0.9994996,0.000090750254,0.000034456756,0.000091994654,0.00023167256,0.000051484156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020144071,0.00022026002,0.00022888133,0.00004102033,0.0007670623,0.00013264111,0.000084564985,0.0000646238,0.0000026765283],"category_scores_gemma":[0.00003178894,0.00020910804,0.0001034231,0.00013900487,0.00006882164,0.0006156309,0.000051569365,0.0001741716,8.697343e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000034300167,0.00001097379,0.0004040098,0.003669433,0.00012970537,0.000011766966,0.00238089,0.8717253,0.00018853814,0.104871415,0.00017717264,0.016396502],"study_design_scores_gemma":[0.00043687262,0.0000796927,0.0000076213396,0.00021147922,0.000045839068,0.0000075127473,0.039017238,0.92479306,0.0009796871,0.030490752,0.003637208,0.0002930469],"about_ca_topic_score_codex":0.00001256914,"about_ca_topic_score_gemma":0.000013817974,"teacher_disagreement_score":0.71529067,"about_ca_system_score_codex":0.00017514986,"about_ca_system_score_gemma":0.000051445157,"threshold_uncertainty_score":0.8527179},"labels":[],"label_agreement":null},{"id":"W4415329317","doi":"10.1177/09596518251380952","title":"Hybrid quantum-inspired proximal policy optimization for fault detection in wind turbine on supervisory control and data acquisition system","year":2025,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering","topic":"Energy Load and Power Forecasting","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"SCADA; Fault detection and isolation; Wind power; Reinforcement learning; Turbine; Supervisory control; Curse of dimensionality; Robustness (evolution); Hyperparameter","score_opus":0.01093292176950869,"score_gpt":0.2062802527561609,"score_spread":0.19534733098665222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415329317","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":[0.5006029,0.0021241643,0.49504772,0.00011405406,0.0012706844,0.00066491,0.00006620614,0.00006606619,0.0000433163],"genre_scores_gemma":[0.99931955,0.00008264536,0.0003508312,0.000008279409,0.00020236857,0.000014822917,0.00000203718,0.000017487357,0.0000019920897],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986892,0.000009110488,0.00077481335,0.00014907595,0.00019443248,0.00018341368],"domain_scores_gemma":[0.99930304,0.00009759613,0.00023295506,0.000099266035,0.00020497026,0.00006217014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077217317,0.00018690014,0.0004877616,0.00036993253,0.00004905034,0.000035075063,0.00020871332,0.000110645124,1.5417929e-7],"category_scores_gemma":[0.00029049747,0.00015195353,0.00007276128,0.00022182227,0.00002482004,0.000345151,0.00002801863,0.00020305242,2.3852888e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016749049,0.000017632787,0.00002594893,0.0017005041,0.000118710166,6.1158113e-7,0.000028746472,0.9425699,0.036745105,0.018096067,0.000006251713,0.00052300707],"study_design_scores_gemma":[0.0027101007,0.00015203805,0.00007564986,0.0028760575,0.00009507641,0.000051107454,0.00009380731,0.98612225,0.00757295,0.00001732499,0.00011796372,0.00011567091],"about_ca_topic_score_codex":0.000014865313,"about_ca_topic_score_gemma":4.8763576e-7,"teacher_disagreement_score":0.49871665,"about_ca_system_score_codex":0.00011483561,"about_ca_system_score_gemma":0.000036014364,"threshold_uncertainty_score":0.61964846},"labels":[],"label_agreement":null},{"id":"W4415357618","doi":"10.3390/jrfm18100591","title":"Using Markov Chains and Entropy to Explain Value at Risk in European Electricity Markets","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Volatility (finance); Electricity; Diversification (marketing strategy); Value at risk; Markov chain; Financial market; Entropy (arrow of time); Systemic risk; Brent Crude; Panel data","score_opus":0.005437394614645684,"score_gpt":0.19852959065709144,"score_spread":0.19309219604244576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415357618","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":[0.9303359,0.0014549235,0.0641826,0.000016121885,0.0003258599,0.000078983,0.000004480051,0.000012025263,0.0035891316],"genre_scores_gemma":[0.9891862,0.0061449036,0.0043949927,0.00005306249,0.00010560189,8.9666383e-7,3.342312e-7,0.000011822676,0.00010215557],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992031,0.000089199624,0.00030394067,0.000106307816,0.00009696438,0.00020048661],"domain_scores_gemma":[0.9997224,0.000047704027,0.00008152065,0.000068259105,0.000014973818,0.00006513607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007076794,0.00011867353,0.00018672888,0.00035300554,0.000101300066,0.000031611755,0.00007757927,0.00002891151,0.000003397276],"category_scores_gemma":[0.000070543305,0.00011264497,0.000039250957,0.0002757183,0.000010921481,0.000065337415,0.00009918188,0.00019784638,6.146078e-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":[0.00036581678,0.00005786478,0.10620982,0.00019686231,0.00008147821,0.00039752017,0.0011684704,0.04698994,0.00047173962,0.0036647788,0.0015385429,0.8388572],"study_design_scores_gemma":[0.0021728466,0.00013309316,0.8647826,0.00070414064,0.00017186011,0.000033095683,0.0001339281,0.041547734,0.00039723364,0.00097276684,0.08857246,0.00037825646],"about_ca_topic_score_codex":0.000026701484,"about_ca_topic_score_gemma":0.000047739737,"teacher_disagreement_score":0.8384789,"about_ca_system_score_codex":0.00009979009,"about_ca_system_score_gemma":0.0000057804245,"threshold_uncertainty_score":0.45935285},"labels":[],"label_agreement":null},{"id":"W4415366813","doi":"10.1109/sege65970.2025.11203501","title":"Long-term Provincial Load Forecasting In the Context of DERs: A Hybrid Approach","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Energie NB Power (Canada); University of New Brunswick","funders":"","keywords":"Probabilistic logic; Mean absolute percentage error; Residual; Monte Carlo method; Mean squared error; Probabilistic forecasting; Artificial neural network; Context (archaeology); Electric power system; Electricity","score_opus":0.022284469434226892,"score_gpt":0.22960417333710506,"score_spread":0.20731970390287818,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415366813","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":[0.67783237,0.0031317498,0.036552884,0.0001500101,0.0008950184,0.00084030756,0.000021125119,0.00009720939,0.2804793],"genre_scores_gemma":[0.9976472,0.000053976648,0.0009810504,0.00020046472,0.00020387086,0.00005445686,0.000015830408,0.000038454735,0.0008046658],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99727213,0.00013001615,0.0010239037,0.00044133211,0.0004016372,0.00073095964],"domain_scores_gemma":[0.99874604,0.0004400164,0.00015382907,0.00048187556,0.00011579582,0.000062427185],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0009936143,0.0004443576,0.00057697686,0.00026900973,0.00013772014,0.00012716684,0.0006700893,0.00017129556,0.00006946989],"category_scores_gemma":[0.00027823038,0.00035489496,0.00023782592,0.00079445756,0.00021896008,0.00024512186,0.00017002673,0.0006233973,0.000005258185],"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":[0.0004007008,0.0012602421,0.11209726,0.008449534,0.00080290023,0.00028548768,0.026778094,0.046154115,0.0009946774,0.05475326,0.0018664495,0.7461573],"study_design_scores_gemma":[0.0057955543,0.00037667726,0.009291607,0.0049908226,0.00040140637,0.00023232879,0.015180334,0.939082,0.020905288,0.0005725565,0.0015197315,0.001651637],"about_ca_topic_score_codex":0.0010127694,"about_ca_topic_score_gemma":0.0018865471,"teacher_disagreement_score":0.89292794,"about_ca_system_score_codex":0.00021456755,"about_ca_system_score_gemma":0.00033573588,"threshold_uncertainty_score":0.9998903},"labels":[],"label_agreement":null},{"id":"W4415368043","doi":"10.1109/sege65970.2025.11203747","title":"Advanced and Optimized Forecasting Techniques for Wind Power Generation: A Comparative Analysis","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Boosting (machine learning); Wind power; Stability (learning theory); Renewable energy; Time horizon; Gradient boosting; Point (geometry); Event (particle physics)","score_opus":0.0305753242430447,"score_gpt":0.2824461845654454,"score_spread":0.2518708603224007,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415368043","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":[0.05999346,0.00340913,0.8395172,0.00018605427,0.00057232374,0.0009151834,0.00005348089,0.00033926955,0.09501393],"genre_scores_gemma":[0.75324106,0.00011821949,0.24371958,0.00010864493,0.000113892995,0.000089687186,0.000058938087,0.000021908041,0.0025280875],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979646,0.000050129234,0.00075404654,0.0005894064,0.00013354945,0.00050824945],"domain_scores_gemma":[0.9987932,0.00041833607,0.00013335641,0.0003009696,0.00023554235,0.000118604665],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003592889,0.00047284467,0.00092676404,0.0006281985,0.0004043218,0.00024234813,0.000152862,0.00022714863,0.00018943007],"category_scores_gemma":[0.00008106391,0.00047738818,0.00031976055,0.0013613728,0.00008407449,0.00034892556,0.00009559888,0.0002146562,9.181543e-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":[0.0002506925,0.00008667219,0.00045573027,0.00023953532,0.0059051546,0.000005547793,0.0028000644,0.9105256,0.008789154,0.030173352,0.0017027833,0.03906572],"study_design_scores_gemma":[0.0011508797,0.00013616253,0.00004936463,0.00018547059,0.0011763045,0.0000029456887,0.00047403606,0.9470428,0.03721414,0.0001835404,0.01187721,0.0005071358],"about_ca_topic_score_codex":0.000020456047,"about_ca_topic_score_gemma":0.000094130286,"teacher_disagreement_score":0.69324756,"about_ca_system_score_codex":0.000093305745,"about_ca_system_score_gemma":0.000054589844,"threshold_uncertainty_score":0.9997678},"labels":[],"label_agreement":null},{"id":"W4415368348","doi":"10.1109/sege65970.2025.11203773","title":"Hybrid Long Short-Term Memory (LSTM) and Exponentially Weighted Moving Average (EWMA) Model for Accurate and Scalable Electricity Price Forecasting","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Memorial University of Newfoundland","funders":"","keywords":"Scalability; Electricity; Electricity price forecasting; Toolbox; Moving average; EWMA chart; Process (computing); Volatility (finance); Electricity market","score_opus":0.020165869740575135,"score_gpt":0.23074257616494906,"score_spread":0.21057670642437393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415368348","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":[0.4904509,0.0027952723,0.49685204,0.00003908406,0.00044245573,0.000604387,0.000029858782,0.0002312964,0.008554705],"genre_scores_gemma":[0.9777237,0.0009950516,0.015374268,0.000120747034,0.00023091893,0.00009233219,0.00004567143,0.00013876347,0.0052785473],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99571645,0.00007005748,0.0011759613,0.001170443,0.00031137836,0.001555726],"domain_scores_gemma":[0.99804705,0.00072380586,0.00017897738,0.0004516666,0.00022617953,0.00037232178],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008527435,0.0009011677,0.00091963477,0.0005431313,0.0009433592,0.0006889299,0.00034577007,0.00032814645,0.000055984165],"category_scores_gemma":[0.00021136156,0.0009803696,0.0002054311,0.000543306,0.0001108338,0.0010847994,0.00045758224,0.0006480426,0.000001432717],"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":[0.00061121274,0.00027996255,0.005717223,0.0075565088,0.0015763912,0.00018912707,0.0022158322,0.6540505,0.05732591,0.005817792,0.00086456083,0.26379493],"study_design_scores_gemma":[0.0012937875,0.000086093045,0.00035381637,0.0008754933,0.00026224134,0.000074101976,0.000040830626,0.95341766,0.041612025,0.0009850515,0.000067075016,0.0009318317],"about_ca_topic_score_codex":0.00006957364,"about_ca_topic_score_gemma":0.00010218137,"teacher_disagreement_score":0.4872728,"about_ca_system_score_codex":0.00019831436,"about_ca_system_score_gemma":0.00019621647,"threshold_uncertainty_score":0.99926466},"labels":[],"label_agreement":null},{"id":"W4415398222","doi":"10.1109/smartgridcomm65349.2025.11204624","title":"Adaptive Reinforcement-Learning Based Automatic Generation Control for Smart Grid Cyber-Resilience","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Automatic Generation Control; Smart grid; Exploit; Resilience (materials science); Adaptive control; Control (management); Grid; SCADA; Electric power system","score_opus":0.012271454746069587,"score_gpt":0.22639511465078968,"score_spread":0.2141236599047201,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415398222","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":[0.0048782937,0.00042109576,0.961042,0.00023597355,0.002683373,0.0008838665,0.000013063651,0.00041452804,0.029427836],"genre_scores_gemma":[0.97998476,0.000019454486,0.010218328,0.00052335364,0.00043357819,0.0002465798,0.00008290953,0.000048739184,0.008442302],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975814,0.00009767814,0.000851992,0.0004737765,0.00026294775,0.00073216256],"domain_scores_gemma":[0.9985364,0.00066062744,0.00015965781,0.00031489856,0.0001925774,0.0001358161],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005599605,0.00045821583,0.00047358655,0.000290305,0.00054043275,0.0001984083,0.00024240802,0.00022374032,0.00062968687],"category_scores_gemma":[0.00032351774,0.00048249387,0.00023801734,0.00038762708,0.00005873416,0.0003329407,0.00003911397,0.00033469358,0.000040842424],"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":[0.000042998126,0.000023166423,0.00019348298,0.00024738593,0.00017600121,0.000001955789,0.00014933047,0.9658182,0.0037122027,0.008467756,0.0022608864,0.018906582],"study_design_scores_gemma":[0.0024420435,0.0002909027,0.00011215167,0.00044485743,0.00018530058,9.0897186e-7,0.00011511746,0.97553164,0.012138844,0.00004921608,0.008237482,0.00045152634],"about_ca_topic_score_codex":0.00012408565,"about_ca_topic_score_gemma":0.00010321912,"teacher_disagreement_score":0.9751065,"about_ca_system_score_codex":0.0002469855,"about_ca_system_score_gemma":0.0002173234,"threshold_uncertainty_score":0.99976265},"labels":[],"label_agreement":null},{"id":"W4415473080","doi":"10.1063/5.0285012","title":"Improving short-term wind speed forecasts using regime-switching spatio-temporal covariance models","year":2025,"lang":"en","type":"article","venue":"Journal of Renewable and Sustainable Energy","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Calgary; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wind speed; Covariance; Wind power; Covariance function; Parametric statistics; Wind direction; Constraint (computer-aided design); Covariance mapping","score_opus":0.012360197761553367,"score_gpt":0.2219628155456535,"score_spread":0.20960261778410014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415473080","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":[0.538077,0.0059591117,0.44921365,0.000039559625,0.00081265543,0.00006426652,0.0000014184548,0.00006383617,0.005768481],"genre_scores_gemma":[0.99117863,0.00042150656,0.0043965825,0.000052156764,0.00032058384,6.093357e-7,0.0000042944016,0.00004463148,0.0035809828],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998252,0.000039310402,0.0006429793,0.00019798159,0.00022752112,0.0006402452],"domain_scores_gemma":[0.99911195,0.000073434305,0.00018893393,0.00019804138,0.00027029603,0.00015734442],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00055067387,0.0002765025,0.0004708442,0.0004610751,0.000277129,0.00020965289,0.00021243958,0.00016114081,0.000008931026],"category_scores_gemma":[0.000048249593,0.0002644503,0.0001240806,0.0004432189,0.00003049898,0.0010495774,0.00010450687,0.0002515317,5.951771e-8],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006830724,0.000016925942,0.00064818584,0.0002878857,0.00011720023,0.00027394222,0.00018769826,0.97911865,0.004284147,0.0032932404,0.0003897282,0.011314097],"study_design_scores_gemma":[0.0007461403,0.0000879462,0.000017968268,0.0004905416,0.000095075105,0.0002178327,0.0010583157,0.9724819,0.007461364,0.00803911,0.008957441,0.00034635514],"about_ca_topic_score_codex":0.0016283584,"about_ca_topic_score_gemma":0.00013898747,"teacher_disagreement_score":0.45310163,"about_ca_system_score_codex":0.00024580874,"about_ca_system_score_gemma":0.00026139908,"threshold_uncertainty_score":0.99998075},"labels":[],"label_agreement":null},{"id":"W4415532456","doi":"10.1016/j.engappai.2025.112877","title":"Electric bus energy prediction and factors interactions using explainable machine learning models","year":2025,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Energy Load and Power Forecasting","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":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Anhui University of Technology; Fundamental Research Funds for the Central Universities; Hubei Provincial Key Laboratory of Metallurgical Industry Process System Science","keywords":"Energy consumption; HVAC; Interpretability; Energy (signal processing); Boosting (machine learning); Gradient boosting; Ensemble learning; Energy management","score_opus":0.02294546671825876,"score_gpt":0.23514720621090884,"score_spread":0.2122017394926501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415532456","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":[0.0944011,0.0006755534,0.90364474,0.000005074001,0.00014414583,0.00007734345,0.000006510896,0.00027850372,0.0007670481],"genre_scores_gemma":[0.9952334,0.00018122882,0.0043930267,0.0000015411395,0.00004286366,0.000053625346,0.000016161734,0.000022682983,0.000055442142],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992284,0.000010360421,0.00033121966,0.00016679452,0.000073687996,0.00018952454],"domain_scores_gemma":[0.9995735,0.00013687306,0.00003680255,0.00015013757,0.000055210396,0.00004747074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000091103546,0.00014629198,0.00014568279,0.00037267536,0.00014857067,0.00003128345,0.000108449836,0.000061019593,0.0000069482858],"category_scores_gemma":[0.000034044133,0.00016883646,0.000038078328,0.000726241,0.000019218947,0.00018133796,0.000029838662,0.000233839,8.5688765e-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":[0.0000016940695,0.000014067299,0.00009596294,0.00004388776,0.00002683493,1.08151994e-7,0.00019476119,0.9258064,0.030662054,0.030232236,0.000002554129,0.01291942],"study_design_scores_gemma":[0.000008558899,0.000008413788,0.000025623149,0.00004830526,0.000017727949,0.0000021190006,0.00012889877,0.86629,0.1309128,0.0016198744,0.00083925994,0.00009846547],"about_ca_topic_score_codex":0.00022784996,"about_ca_topic_score_gemma":0.000023873208,"teacher_disagreement_score":0.90083236,"about_ca_system_score_codex":0.00006247107,"about_ca_system_score_gemma":0.000013322157,"threshold_uncertainty_score":0.6884951},"labels":[],"label_agreement":null},{"id":"W4415548616","doi":"10.2139/ssrn.5660227","title":"DEPICT: Explainable Energy Forecasting with Pattern Integration and Temporal Attention","year":2025,"lang":"","type":"preprint","venue":"SSRN Electronic Journal","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Interpretability; Deep learning; Energy (signal processing); Autoencoder; Demand forecasting; Convolution (computer science); Ranking (information retrieval); Key (lock)","score_opus":0.00965924358948157,"score_gpt":0.20300099889068865,"score_spread":0.1933417553012071,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415548616","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":[0.18329415,0.009937894,0.80035293,0.00019147519,0.0013006326,0.00022586036,0.000016651595,0.00014377327,0.0045366315],"genre_scores_gemma":[0.9792406,0.014315562,0.0009920227,0.00004159256,0.0009955164,0.00004923164,0.00013793941,0.00011698509,0.0041105435],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9934178,0.00020481604,0.0011357444,0.0007867549,0.0005022485,0.003952643],"domain_scores_gemma":[0.9983022,0.00012956833,0.0006690097,0.0003820834,0.0002820536,0.00023506564],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0018553238,0.0009762433,0.0008113172,0.00061916275,0.0007928383,0.00053672265,0.00045585554,0.0005830262,0.000029178498],"category_scores_gemma":[0.000061085084,0.0009026799,0.00027746058,0.00041293577,0.00008522543,0.0006148241,0.0002903607,0.0062760497,0.0000023475727],"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":[0.00019696806,0.00009216425,0.0146918455,0.0005234228,0.0015674345,0.00007583565,0.00093540206,0.046520345,0.00025822045,0.035384186,0.00003949239,0.8997147],"study_design_scores_gemma":[0.005349594,0.002559304,0.0009489736,0.0147453565,0.0015170346,0.0092775915,0.011095151,0.8204521,0.0014574825,0.12270378,0.005805597,0.0040880553],"about_ca_topic_score_codex":0.0016128306,"about_ca_topic_score_gemma":0.013621466,"teacher_disagreement_score":0.8956266,"about_ca_system_score_codex":0.0022156795,"about_ca_system_score_gemma":0.0021443863,"threshold_uncertainty_score":0.9993424},"labels":[],"label_agreement":null},{"id":"W4415711025","doi":"10.36227/techrxiv.176185136.63566306/v1","title":"GRU-Based Load Forecasting for Microgrid Systems: Modeling and Simulation Using Simulink","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Microgrid; Renewable energy; Modeling and simulation; Data modeling; Simulation modeling","score_opus":0.046832653321970126,"score_gpt":0.273841333978464,"score_spread":0.22700868065649388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415711025","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":[0.20482536,0.010579778,0.78043693,0.000029372522,0.0018938191,0.00072817877,0.000033319615,0.00025581138,0.0012173968],"genre_scores_gemma":[0.9673891,0.000057161375,0.031687226,0.00008275023,0.0004559363,0.000027859345,0.000020852805,0.00010479179,0.00017431458],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9970692,0.000053584048,0.0011244307,0.00066114,0.0002392168,0.0008524641],"domain_scores_gemma":[0.9978678,0.0011097894,0.00015562511,0.0003240521,0.00038390886,0.00015880015],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007340455,0.0005921371,0.0006399984,0.0003621356,0.00059066864,0.0004540634,0.00017133038,0.00041127863,0.000012273288],"category_scores_gemma":[0.00032478495,0.00066185225,0.0002076906,0.0004939458,0.000047177957,0.0003605412,0.00008985185,0.00030280882,0.0000013146662],"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":[0.000074124815,0.000023399281,0.0002445643,0.0025896467,0.0001494766,0.0000029916682,0.00018018615,0.97678566,0.0012238667,0.0005283016,0.000011376544,0.018186413],"study_design_scores_gemma":[0.001673002,0.000056132474,9.2811484e-7,0.00292168,0.0002856562,0.000004256021,0.00027684766,0.9913436,0.0007202478,0.00013730809,0.0019641118,0.0006161949],"about_ca_topic_score_codex":0.0003373923,"about_ca_topic_score_gemma":0.00003662023,"teacher_disagreement_score":0.76256377,"about_ca_system_score_codex":0.00043382432,"about_ca_system_score_gemma":0.00026974283,"threshold_uncertainty_score":0.99958324},"labels":[],"label_agreement":null},{"id":"W4415749882","doi":"10.1007/978-981-95-0441-1_6","title":"Short Term Load Forecasting for Effective Trading in Energy Market Using Artificial Neural Networks and ADAM Optimizer","year":2025,"lang":"en","type":"book-chapter","venue":"Green energy and technology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Artificial neural network; Term (time); Probabilistic forecasting; Energy (signal processing); Task (project management); Energy market; Electric power system; Demand forecasting","score_opus":0.013903225495702934,"score_gpt":0.20203669788649822,"score_spread":0.18813347239079528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415749882","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":[0.046104986,0.061999183,0.45342612,0.0004443258,0.0056484975,0.0019595036,0.00029714714,0.003481004,0.42663923],"genre_scores_gemma":[0.9884189,0.00074201456,0.002023942,0.00006287419,0.0005331816,0.00014226713,0.000067182555,0.0001838073,0.007825833],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99828976,0.000015986025,0.0004749611,0.00056715566,0.00009485085,0.0005572839],"domain_scores_gemma":[0.9993101,0.0002622466,0.00008273508,0.00022248446,0.0000512016,0.00007123431],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00016308254,0.00055572303,0.0007201355,0.000721331,0.00016080635,0.0000377632,0.00016711118,0.0012126209,0.000011162161],"category_scores_gemma":[0.000023614479,0.0006111301,0.00009737552,0.0001607051,0.00018484257,0.00009363383,0.00015697457,0.00046556574,2.7339611e-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":[0.00009286758,0.00000804806,0.00017944805,0.00016305552,0.00023040896,0.00010459317,0.000023187376,0.041977294,0.00009265484,0.10701906,0.00013519448,0.8499742],"study_design_scores_gemma":[0.00035763587,0.00008925958,0.000011570114,0.0005748826,0.00011260662,0.00013165528,0.00000811345,0.9744386,0.00025151687,0.014624112,0.00881492,0.00058511295],"about_ca_topic_score_codex":0.00012236007,"about_ca_topic_score_gemma":0.0012767743,"teacher_disagreement_score":0.9423139,"about_ca_system_score_codex":0.00013390361,"about_ca_system_score_gemma":0.000024968956,"threshold_uncertainty_score":0.999634},"labels":[],"label_agreement":null},{"id":"W4415749885","doi":"10.1007/978-981-95-0441-1_2","title":"Electrical Energy Price Forecasting for Effective Energy Trading Using Deep Neural Networks With ADADELTA Optimizer","year":2025,"lang":"en","type":"book-chapter","venue":"Green energy and technology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Bidding; Artificial neural network; Electric potential energy; Profit (economics); Schedule; Energy (signal processing); Energy consumption; Electrical network; Electricity","score_opus":0.008580583165448331,"score_gpt":0.1816028310814397,"score_spread":0.17302224791599138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415749885","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":[0.00016318719,0.012001182,0.925619,0.00008527372,0.0005052181,0.00020012505,0.000025203874,0.0010602415,0.060340565],"genre_scores_gemma":[0.88648176,0.0021833112,0.034748573,0.000714561,0.0026099812,0.0008117363,0.00061772973,0.0012284986,0.07060384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974817,0.000023239329,0.00054182665,0.0008232554,0.00017105503,0.000958946],"domain_scores_gemma":[0.99873626,0.00036915677,0.00021898736,0.0003955605,0.0001352218,0.00014478243],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.000101234036,0.0009016949,0.0010081389,0.0010209219,0.00034117862,0.000052813066,0.0003716008,0.0016562096,0.000015837697],"category_scores_gemma":[0.000023823824,0.00087268,0.00017937557,0.0004242045,0.0002178453,0.0001292326,0.00016334698,0.000631254,7.475344e-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":[0.00015353461,0.000011692694,0.0000121921985,0.00009814135,0.0007096369,0.00011255087,0.000016531818,0.16676816,0.000045245586,0.35402143,0.000234966,0.4778159],"study_design_scores_gemma":[0.0007277358,0.00032093,3.649109e-7,0.00033625343,0.00027495483,0.00035962992,0.0000046649443,0.92793,0.000553712,0.0077405027,0.06089149,0.00085975404],"about_ca_topic_score_codex":0.00022120852,"about_ca_topic_score_gemma":0.0006758257,"teacher_disagreement_score":0.89087045,"about_ca_system_score_codex":0.00018283844,"about_ca_system_score_gemma":0.000044335644,"threshold_uncertainty_score":0.99963987},"labels":[],"label_agreement":null},{"id":"W4415819927","doi":"10.3390/a18110695","title":"Machine Learning Systems Tuned by Bayesian Optimization to Forecast Electricity Demand and Production","year":2025,"lang":"en","type":"article","venue":"Algorithms","topic":"Energy Load and Power Forecasting","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":"Concordia University","funders":"","keywords":"Hyperparameter; Bayesian optimization; Renewable energy; Wind power; Electricity generation; Convolutional neural network; Artificial neural network; Electricity; Hyperparameter optimization; Production (economics)","score_opus":0.004489227441855999,"score_gpt":0.19348468133431648,"score_spread":0.18899545389246047,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415819927","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":[0.014094829,0.003166326,0.9785046,0.00016621659,0.00078185456,0.0002286793,0.00000623276,0.00038054286,0.0026707232],"genre_scores_gemma":[0.9896653,0.00036514254,0.0076454724,0.00003399043,0.00016011679,0.00003961666,0.000066254965,0.000033783166,0.0019902675],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993325,0.000028282288,0.00016504685,0.00019340275,0.000079734025,0.00020104813],"domain_scores_gemma":[0.99976414,0.000027133105,0.000021469401,0.00008466058,0.000030293635,0.00007229999],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016400234,0.00012727929,0.000141485,0.00012875094,0.00015255707,0.000063704945,0.00005076394,0.00006305961,0.0000054375228],"category_scores_gemma":[0.000066782515,0.00013013947,0.000016253221,0.00039513814,0.000008997226,0.000106983156,0.000019140936,0.0001360479,0.000001595647],"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":[0.000004906273,0.0000065926065,0.0005015614,0.00006734576,0.0000300083,0.0000014723265,0.00010619789,0.9679863,0.001149592,0.00006003796,0.0007393078,0.029346716],"study_design_scores_gemma":[0.00014315202,0.00003452004,0.000026366915,0.000058270754,0.000015367475,0.000014801412,0.000023992057,0.9888816,0.0032492203,0.00001690054,0.007400325,0.0001355156],"about_ca_topic_score_codex":0.00009662361,"about_ca_topic_score_gemma":0.000011499999,"teacher_disagreement_score":0.9755705,"about_ca_system_score_codex":0.000053697506,"about_ca_system_score_gemma":0.000007874206,"threshold_uncertainty_score":0.53069335},"labels":[],"label_agreement":null},{"id":"W4415968980","doi":"10.1109/iecon58223.2025.11221133","title":"Self-Attention Transformer Based Short-Term Load Prediction for Electrical Distribution Feeders","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Carleton University","funders":"","keywords":"Transformer; Artificial neural network; Dependency (UML); Electric power system; Electrical load; Nonlinear system; Demand forecasting; Electricity; Population","score_opus":0.007849280460751241,"score_gpt":0.22326122148744892,"score_spread":0.2154119410266977,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415968980","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":[0.033973206,0.00048369775,0.9411407,0.0002484666,0.002169988,0.00085181056,0.00022125136,0.00073202536,0.020178854],"genre_scores_gemma":[0.99521476,0.00018549587,0.0022478735,0.000058941445,0.00023945906,0.0001642209,0.0010208327,0.00004437794,0.0008240575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769664,0.000038518276,0.000720612,0.0004896197,0.00030733927,0.0007472745],"domain_scores_gemma":[0.9992494,0.00015368414,0.00004042839,0.00022007711,0.00019435072,0.00014205],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00037153953,0.0004141298,0.00034013367,0.00017282428,0.00036285532,0.00014953544,0.00014993451,0.00044854303,0.00007532253],"category_scores_gemma":[0.000044029006,0.00044487783,0.0004110466,0.0007021051,0.000031115273,0.00033044184,0.000008049775,0.00032899107,0.000008120666],"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":[0.0018710438,0.0029170045,0.048977993,0.010569025,0.004175267,0.000017355775,0.00096045283,0.12710547,0.114488125,0.09524327,0.02768328,0.5659917],"study_design_scores_gemma":[0.0018358455,0.00029257007,0.004372117,0.00032587652,0.0005697252,0.000003339546,0.000049662827,0.9523129,0.019428734,0.00014807288,0.020206492,0.00045469616],"about_ca_topic_score_codex":0.000019792198,"about_ca_topic_score_gemma":0.00004205937,"teacher_disagreement_score":0.96124154,"about_ca_system_score_codex":0.0011694579,"about_ca_system_score_gemma":0.00024025288,"threshold_uncertainty_score":0.9998003},"labels":[],"label_agreement":null},{"id":"W4416078448","doi":"10.1109/pesgm52009.2025.11225094","title":"Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Hydro One (Canada); Toronto Metropolitan University","funders":"","keywords":"Aggregate (composite); Renewable energy; Power (physics); Estimation; Distributed generation; Wind power; Flow (mathematics); Component (thermodynamics)","score_opus":0.03506863981390326,"score_gpt":0.22931080249247718,"score_spread":0.19424216267857392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416078448","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":[0.8803552,0.000938867,0.095727906,0.00012970189,0.00078597886,0.0004171334,0.0001709138,0.0002928123,0.021181496],"genre_scores_gemma":[0.8337712,0.000023479644,0.16578749,0.000063055595,0.00004566126,0.000006736471,0.00006736347,0.000060215356,0.00017477876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964275,0.00013933708,0.0012515754,0.001027134,0.000334078,0.00082040636],"domain_scores_gemma":[0.9964338,0.0012351911,0.0003480441,0.001418012,0.00034658588,0.00021834469],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010328778,0.0006650737,0.0009204944,0.00028341872,0.00034603704,0.00018532637,0.0005718697,0.0003048184,0.00030682556],"category_scores_gemma":[0.0019013336,0.00060614024,0.00007958676,0.0010920321,0.00045139968,0.00070691865,0.0006219474,0.00062620174,0.0000028462475],"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":[0.00073433504,0.00044818447,0.09937185,0.009381982,0.0036975376,0.00006314796,0.0053881444,0.6850869,0.012638384,0.0035947198,0.0012173237,0.17837751],"study_design_scores_gemma":[0.0015596184,0.00012691318,0.003943343,0.0026563692,0.00023573372,0.000033216274,0.00043655196,0.98484707,0.0051542516,0.000152836,0.00021960873,0.0006344799],"about_ca_topic_score_codex":0.00075522857,"about_ca_topic_score_gemma":0.0008320036,"teacher_disagreement_score":0.2997602,"about_ca_system_score_codex":0.00008652014,"about_ca_system_score_gemma":0.00015640228,"threshold_uncertainty_score":0.999639},"labels":[],"label_agreement":null},{"id":"W4416136634","doi":"10.1109/pesgm52009.2025.11225003","title":"Online Dynamic Ensemble Framework for Improving Bulk System Operational Load Forecasting","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Independent Electricity System Operator","funders":"","keywords":"Baseline (sea); Probabilistic forecasting; Ensemble forecasting; Load profile; Electric power system; Power (physics); Time series","score_opus":0.014839178630498592,"score_gpt":0.2465219255782535,"score_spread":0.23168274694775493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416136634","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":[0.031036656,0.0031966423,0.937354,0.00030302512,0.0060033565,0.0007639097,0.00019316985,0.000646106,0.02050309],"genre_scores_gemma":[0.7803243,0.00002568489,0.21339647,0.00017872934,0.0006735046,0.00009665816,0.00012762922,0.00010795951,0.0050691096],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964328,0.000042705517,0.001253332,0.0007845414,0.00037699877,0.0011096699],"domain_scores_gemma":[0.9975538,0.0011283908,0.00017295372,0.00051999907,0.00043622218,0.0001886089],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00062567403,0.00068064383,0.00069349626,0.00027986732,0.0007236979,0.0004084371,0.00044270715,0.00061213336,0.000104081715],"category_scores_gemma":[0.0007363034,0.0007253098,0.00034836167,0.0006660454,0.00005103381,0.0003462423,0.0002079096,0.0007010912,0.000019151614],"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":[0.00009231752,0.00016258119,0.0001279738,0.0069461386,0.00046593428,0.000019916133,0.00055171916,0.4717079,0.004916868,0.37345433,0.00031731965,0.14123699],"study_design_scores_gemma":[0.0008583149,0.0001009392,0.000028426053,0.0033093574,0.00018157742,0.000031842977,0.0007945226,0.98712635,0.0024680472,0.0015300752,0.0028959026,0.00067464897],"about_ca_topic_score_codex":0.00019791246,"about_ca_topic_score_gemma":0.00038049935,"teacher_disagreement_score":0.7492876,"about_ca_system_score_codex":0.0011688714,"about_ca_system_score_gemma":0.00056863966,"threshold_uncertainty_score":0.9995198},"labels":[],"label_agreement":null},{"id":"W4416297297","doi":"10.1007/s44196-025-01045-6","title":"Energy-Efficient Optimal Scheduling of Renewable Energy Sources in Power Systems Using Genetic Algorithms and Support Vector Machines","year":2025,"lang":"en","type":"article","venue":"International Journal of Computational Intelligence Systems","topic":"Energy Load and Power Forecasting","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Renewable energy; Mean absolute percentage error; Scheduling (production processes); Wind power; Electricity; Support vector machine; Electric power system; Electricity generation; Genetic algorithm","score_opus":0.013765869191013164,"score_gpt":0.26152251965150225,"score_spread":0.2477566504604891,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416297297","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":[0.2305992,0.0048455633,0.76104313,0.000013633704,0.002981228,0.000036013367,0.000013709677,0.000016219412,0.00045130166],"genre_scores_gemma":[0.9920853,0.00009292039,0.007520143,0.0000134192305,0.00017742791,0.000003454482,0.0000068925697,0.000019147135,0.00008132855],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978951,0.00006712544,0.0011645322,0.00015247715,0.0005375811,0.00018317599],"domain_scores_gemma":[0.9986619,0.00025915794,0.00034964565,0.00007099298,0.0005896881,0.00006863402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035532386,0.00018627936,0.00035554892,0.0007320712,0.00003731265,0.00012046392,0.00032820288,0.00008411817,0.00001179224],"category_scores_gemma":[0.000037558893,0.000179369,0.0000901777,0.0002735845,0.00006141748,0.00014142931,0.00005574991,0.00013147818,4.148638e-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":[0.00002601775,0.00003898209,0.0011385619,0.000058477064,0.00019826963,0.000041864914,0.00024563342,0.99144053,0.0005348323,0.004592183,0.000020267957,0.0016644014],"study_design_scores_gemma":[0.00018449222,0.000047460617,0.0002804703,0.00088583643,0.00001568936,0.00036582266,0.00032059514,0.9955217,0.0014498873,0.00036961562,0.00041802134,0.00014040021],"about_ca_topic_score_codex":0.0008305883,"about_ca_topic_score_gemma":0.000012679491,"teacher_disagreement_score":0.76148605,"about_ca_system_score_codex":0.00015537228,"about_ca_system_score_gemma":0.00014087188,"threshold_uncertainty_score":0.73144555},"labels":[],"label_agreement":null},{"id":"W4416342154","doi":"10.1109/epec65543.2025.11230388","title":"Medium-Term Electric Load Forecasting: A Benchmark Study of Transformer-Based and Linear-Based Methods","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Hydro-Québec; York University; McGill University","funders":"","keywords":"Robustness (evolution); Benchmark (surveying); Electric power system; Linear model; Electrical load; Time series; Focus (optics); Key (lock); Model selection","score_opus":0.02638426672544635,"score_gpt":0.3010074963663232,"score_spread":0.2746232296408768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416342154","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":[0.64792675,0.0026032524,0.3220571,0.0001288013,0.0010328625,0.001118676,0.000014133092,0.00022960923,0.024888838],"genre_scores_gemma":[0.9653831,0.0000720864,0.033808786,0.00010609816,0.00010557142,0.00007579546,0.0000131410725,0.00008184819,0.00035356602],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9957475,0.00035725738,0.0015669652,0.00079561945,0.0005535636,0.0009791024],"domain_scores_gemma":[0.9968545,0.0017783078,0.00021132999,0.0005864126,0.00029217632,0.0002772647],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017781483,0.0008115515,0.0012016916,0.00089121924,0.0002624042,0.000082693834,0.00042145303,0.0003944002,0.00033158227],"category_scores_gemma":[0.00037615464,0.0007901584,0.00028993897,0.0024322574,0.00010740338,0.00017751161,0.00004458537,0.0007247874,0.0000014479359],"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":[0.0012992647,0.004250635,0.025060685,0.0072882883,0.0022428972,0.00010258837,0.005934286,0.2332861,0.08125001,0.00094522786,0.00026573398,0.6380743],"study_design_scores_gemma":[0.0057393187,0.0018775377,0.0019087923,0.00079917663,0.0006877663,0.000004870366,0.00044409605,0.83247966,0.1546158,0.00007244022,0.00065659435,0.00071392686],"about_ca_topic_score_codex":0.00036092935,"about_ca_topic_score_gemma":0.00043581703,"teacher_disagreement_score":0.63736033,"about_ca_system_score_codex":0.0001984552,"about_ca_system_score_gemma":0.00076660985,"threshold_uncertainty_score":0.9994549},"labels":[],"label_agreement":null},{"id":"W4416342801","doi":"10.1109/epec65543.2025.11230409","title":"An Optimized Long Short-Term Memory-Based Model for One Hour Ahead Wind Speed Prediction","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Saskatchewan","funders":"","keywords":"Wind speed; Mean squared error; Grid; Wind power; Callback; Approximation error; Function (biology)","score_opus":0.028802822588738484,"score_gpt":0.2605262840972216,"score_spread":0.23172346150848314,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416342801","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":[0.18459375,0.00024715054,0.8000866,0.00012179691,0.0020295894,0.00083452155,0.00015493478,0.00063848123,0.011293156],"genre_scores_gemma":[0.9482291,0.000043651045,0.045808446,0.00014053404,0.00047460417,0.000024041563,0.00026513642,0.00012580557,0.0048887106],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99706924,0.00005325697,0.0009379817,0.00074756396,0.00031295975,0.00087901595],"domain_scores_gemma":[0.9985082,0.00020375186,0.00007655058,0.0007200367,0.00018603913,0.00030540331],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005232354,0.0006073186,0.00069762155,0.00042345433,0.00032917308,0.00024720246,0.00037990246,0.00049276656,0.00025216062],"category_scores_gemma":[0.00006345785,0.0006889695,0.00034760812,0.00041668996,0.000064762964,0.0005417986,0.000050213883,0.0004093747,0.000006241911],"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":[0.00040529264,0.00026959062,0.0006508614,0.00053788157,0.0002911302,0.0000047327594,0.00030639852,0.9772979,0.011189394,0.0004131691,0.0003223893,0.0083112335],"study_design_scores_gemma":[0.0029901902,0.00020195406,0.00040239998,0.0008293534,0.0004096249,0.0000018590107,0.000058581398,0.9678622,0.026542453,0.00010417889,0.000030697654,0.00056649285],"about_ca_topic_score_codex":0.000040804225,"about_ca_topic_score_gemma":0.00012251506,"teacher_disagreement_score":0.76363534,"about_ca_system_score_codex":0.00023945991,"about_ca_system_score_gemma":0.0002401426,"threshold_uncertainty_score":0.9995561},"labels":[],"label_agreement":null},{"id":"W4416381323","doi":"10.1109/access.2025.3631586","title":"A Multi-Phase Deep Learning Framework for Multi-Step Short-Term Wind Power Forecasting in Presence of Uncertainties","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","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 du Québec en Outaouais","funders":"","keywords":"Wind power; Wind power forecasting; Wind speed; Artificial neural network; Feed forward; Deep learning; Convolutional neural network; Fuzzy logic; Electric power system","score_opus":0.060867054237078805,"score_gpt":0.3466598554669548,"score_spread":0.28579280122987605,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416381323","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":[0.5895285,0.0004925781,0.40857968,0.0000052474984,0.0007321242,0.00021936529,0.000007048465,0.000103347105,0.00033212456],"genre_scores_gemma":[0.9624014,0.000029406869,0.037269138,0.000016532915,0.00005921912,0.000057108828,0.000008017872,0.000041679607,0.000117517084],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866647,0.000031588235,0.00046826797,0.00027422028,0.00012048891,0.00043897922],"domain_scores_gemma":[0.99905497,0.00052222644,0.00007035384,0.00021671047,0.000081500446,0.00005423416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023038515,0.0002318251,0.00033619258,0.00026641245,0.000093884686,0.00009102875,0.00044179472,0.00018062547,0.000017909393],"category_scores_gemma":[0.00045061388,0.00024103594,0.00009930698,0.0004577269,0.00005551789,0.00036263443,0.00008281767,0.0003880489,6.7338397e-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":[0.00009463419,0.00016361164,0.091735624,0.0008098023,0.0001166928,0.000022656655,0.00293928,0.8400095,0.004247468,0.00042637932,0.000050320483,0.059383992],"study_design_scores_gemma":[0.0010913379,0.000048408187,0.0035969813,0.0011440655,0.00002149771,0.0000019875642,0.00031621737,0.98277664,0.010303428,0.00014402898,0.00028039957,0.0002750309],"about_ca_topic_score_codex":0.00008457135,"about_ca_topic_score_gemma":0.00038138428,"teacher_disagreement_score":0.37287292,"about_ca_system_score_codex":0.000055042445,"about_ca_system_score_gemma":0.000023656066,"threshold_uncertainty_score":0.98291606},"labels":[],"label_agreement":null},{"id":"W4416544980","doi":"10.1016/j.aei.2025.104095","title":"Recursive deep learning with multi-scale attention for energy demand dynamics","year":2025,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"","keywords":"Mean squared error; Residual; Robustness (evolution); Mean absolute percentage error; Perceptron; Deep learning; Artificial neural network; Noise reduction","score_opus":0.0025721393540580092,"score_gpt":0.1858386883290272,"score_spread":0.1832665489749692,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416544980","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":[0.025219174,0.00022982924,0.9718047,0.000005654608,0.00041215887,0.000112497866,0.000006511946,0.0005154787,0.0016940264],"genre_scores_gemma":[0.5290301,0.00021932027,0.46966535,0.000036467805,0.000046583034,0.00012850386,0.00019976115,0.00007546888,0.0005984785],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991708,0.000003118037,0.00032789615,0.00008099313,0.000084199804,0.0003329885],"domain_scores_gemma":[0.99960124,0.00008364543,0.00005157531,0.00014004209,0.000067040375,0.000056454504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000068182606,0.00021464915,0.00019895278,0.00016090876,0.00009093263,0.000037879672,0.000110120505,0.00009043354,0.0000012379447],"category_scores_gemma":[0.00002742544,0.00021584358,0.000055633263,0.00026215642,0.000014006099,0.0003490423,0.00002125052,0.00017776617,0.0000015323358],"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":[0.000008562922,0.000005817544,0.00018818988,0.0003302129,0.000054284,4.858521e-7,0.00026713035,0.9680997,0.00019810043,0.0032228585,0.000011019558,0.027613651],"study_design_scores_gemma":[0.00070036703,0.00004638939,0.00014494204,0.00029992504,0.000029066026,0.0000051814313,0.0002518093,0.9839831,0.001352116,0.00003699709,0.012895608,0.0002544853],"about_ca_topic_score_codex":0.0000016034068,"about_ca_topic_score_gemma":0.000037449736,"teacher_disagreement_score":0.5038109,"about_ca_system_score_codex":0.00013053209,"about_ca_system_score_gemma":0.000008879395,"threshold_uncertainty_score":0.8801846},"labels":[],"label_agreement":null},{"id":"W4416704830","doi":"10.26868/25222708.2025.1275","title":"Predictive and transactive controls for EVE park net-zero community with AI/ML models","year":2025,"lang":"","type":"article","venue":"Building Simulation Conference proceedings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Renewable energy; Wind power; Electricity; Transactive memory; Work (physics); Solar energy; Government (linguistics); Energy (signal processing)","score_opus":0.02495504092757478,"score_gpt":0.2670986202945515,"score_spread":0.24214357936697672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416704830","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":[0.27395067,0.0003778239,0.71928376,0.00021198485,0.00017962874,0.0011673579,0.00007257963,0.00026056299,0.0044956226],"genre_scores_gemma":[0.99471396,0.00007670967,0.004361511,0.0002129427,0.000095190015,0.0002494285,0.000020966605,0.00008206979,0.00018724249],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99754405,0.000051848674,0.0007237479,0.0006290284,0.00030974907,0.00074159994],"domain_scores_gemma":[0.9966692,0.0013059102,0.000270886,0.00020082458,0.0013449207,0.00020830003],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00069457706,0.000689398,0.0007951424,0.00035544304,0.0009884649,0.00060074154,0.0003410258,0.00042500818,0.000013103592],"category_scores_gemma":[0.00025385278,0.00070967694,0.00014985775,0.0005425814,0.000244128,0.0015862889,0.000072052855,0.0010566638,4.7205847e-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":[0.001181779,0.00008556747,0.0018364866,0.001033353,0.00065046997,5.609574e-7,0.011263327,0.90453464,0.0016634056,0.06817496,0.0000459327,0.00952955],"study_design_scores_gemma":[0.0033544716,0.00042632216,0.00060303276,0.0024921477,0.0004995631,0.000003087872,0.0012240853,0.94037926,0.0018341881,0.04809011,0.00046250134,0.000631252],"about_ca_topic_score_codex":0.000104882696,"about_ca_topic_score_gemma":0.000014991667,"teacher_disagreement_score":0.72076327,"about_ca_system_score_codex":0.00021096104,"about_ca_system_score_gemma":0.00020877662,"threshold_uncertainty_score":0.99953544},"labels":[],"label_agreement":null},{"id":"W4416773932","doi":"10.48550/arxiv.2511.20508","title":"Causal Feature Selection for Weather-Driven Residential Load Forecasting","year":2025,"lang":"","type":"preprint","venue":"ArXiv.org","topic":"Energy Load and Power Forecasting","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":"Feature selection; Robustness (evolution); Selection (genetic algorithm); Feature (linguistics); Probabilistic forecasting; Electricity; Electricity demand; Time series","score_opus":0.03602036197625031,"score_gpt":0.25909138342734267,"score_spread":0.22307102145109237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416773932","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":[0.8863899,0.0025739167,0.07273366,0.0005039864,0.01659965,0.0019951726,0.00037767092,0.0011408697,0.017685156],"genre_scores_gemma":[0.96149987,0.0004192708,0.009282223,0.00009969157,0.005788189,0.0004179353,0.00033276973,0.00026957708,0.021890456],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949577,0.00014114937,0.0011698651,0.0016114502,0.0005665762,0.0015532479],"domain_scores_gemma":[0.9972152,0.0005342816,0.00050919375,0.000810464,0.000632369,0.00029849244],"candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":["metaepi_narrow","research_integrity"],"category_scores_codex":[0.0006812808,0.0013205579,0.0011912391,0.0004290954,0.0008436904,0.00029149713,0.00081882515,0.0017987416,0.00019688852],"category_scores_gemma":[0.00063014263,0.0015492509,0.0008789236,0.000713204,0.00009476533,0.0003520634,0.00069532334,0.0025727435,0.00006211489],"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":[0.00052191055,0.00016246056,0.18773852,0.004994227,0.0023424143,0.000054446078,0.003703003,0.7538178,0.013234694,0.0007750265,0.0117918085,0.020863684],"study_design_scores_gemma":[0.003906428,0.0005390578,0.015911348,0.009634404,0.0019180247,0.0001536079,0.00034608852,0.7936591,0.03890757,0.0008886196,0.13002875,0.0041070264],"about_ca_topic_score_codex":0.00072946097,"about_ca_topic_score_gemma":0.0018412618,"teacher_disagreement_score":0.17182717,"about_ca_system_score_codex":0.0010731497,"about_ca_system_score_gemma":0.0007462177,"threshold_uncertainty_score":0.9999546},"labels":[],"label_agreement":null},{"id":"W4416778588","doi":"10.18280/jesa.581015","title":"Weather Based-Electricity Load Forecasting Using Empirical Wavelet Decomposition and Bi-LSTM With Attention, Case Study in Bali","year":2025,"lang":"","type":"article","venue":"Journal Européen des Systèmes Automatisés","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Hilbert–Huang transform; Wavelet; Weather forecasting; Decomposition; Wavelet transform","score_opus":0.03175287490812548,"score_gpt":0.29109693525280916,"score_spread":0.2593440603446837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416778588","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":[0.93681926,0.0025726547,0.058118414,0.0000631034,0.00064079836,0.00059079385,0.000011213394,0.00017757414,0.001006166],"genre_scores_gemma":[0.98584944,0.00006675838,0.013510046,0.00007196859,0.00023084704,0.00001352815,0.0000027657672,0.0001318253,0.00012282071],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949434,0.0009256051,0.0017350381,0.0006590395,0.00067737955,0.0010595422],"domain_scores_gemma":[0.9978837,0.00047090722,0.00054797216,0.0003458499,0.00042005075,0.00033157208],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0017628664,0.00078816345,0.0009804053,0.0011317533,0.0011102724,0.00092795625,0.0002569833,0.00024815145,0.00006212999],"category_scores_gemma":[0.00021774317,0.00071995717,0.00018814615,0.002116999,0.00014825343,0.0007655458,0.00014085442,0.0012382225,0.00000398436],"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":[0.00023797678,0.0010121363,0.5146332,0.0015543689,0.00095679244,0.035036657,0.0050030705,0.19876781,0.0012395664,0.000048566115,0.00017846808,0.2413314],"study_design_scores_gemma":[0.0028185528,0.00066326273,0.14159745,0.0043823123,0.0004311226,0.03257154,0.0012536778,0.81539494,0.00009583949,0.00012101633,0.000045755438,0.0006245209],"about_ca_topic_score_codex":0.0008090641,"about_ca_topic_score_gemma":0.0011448365,"teacher_disagreement_score":0.61662716,"about_ca_system_score_codex":0.0015747015,"about_ca_system_score_gemma":0.0005327649,"threshold_uncertainty_score":0.9995251},"labels":[],"label_agreement":null},{"id":"W4416833646","doi":"10.1007/s41870-025-02895-1","title":"A stacked long short-term memory model for electrical load forecasting in the Ontario electricity market","year":2025,"lang":"en","type":"article","venue":"International Journal of Information Technology","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Benchmarking; Electricity; Electrical load; Artificial neural network; Mean absolute percentage error; Mean squared error; Power (physics); Electric power system; Demand forecasting; Unit (ring theory)","score_opus":0.0113439023710049,"score_gpt":0.23651859049523777,"score_spread":0.22517468812423286,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416833646","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":[0.5310335,0.00011412215,0.45898685,0.00044044683,0.00050054403,0.00015083759,0.000004915568,0.000058349426,0.008710405],"genre_scores_gemma":[0.99590373,0.000029700832,0.0037224402,0.00017211963,0.00004433015,0.000019391364,0.0000072599832,0.0000052869996,0.000095758995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988052,0.000010410065,0.0006739694,0.00004842971,0.00026805841,0.00019390798],"domain_scores_gemma":[0.9992253,0.00013692344,0.00014382043,0.0000826967,0.00039449835,0.00001674107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005419322,0.00010787428,0.00015700847,0.0009262257,0.000040177736,0.00006330971,0.0005686947,0.0001381625,0.000010650116],"category_scores_gemma":[0.00027930134,0.000087627704,0.00008290444,0.00035688747,0.00002339709,0.00056080404,0.000031569893,0.0004665247,7.6224717e-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":[0.0005515423,0.00012964306,0.024511065,0.00009478995,0.00050874223,0.000052011954,0.0052913525,0.35858724,0.0006265372,0.018152865,0.008073419,0.5834208],"study_design_scores_gemma":[0.0008193144,0.00006469304,0.0016953375,0.00011876565,0.000017063581,0.00022898767,0.000111063506,0.9874405,0.0022467389,0.0049836305,0.0021597482,0.00011415084],"about_ca_topic_score_codex":0.00003149185,"about_ca_topic_score_gemma":0.0010837654,"teacher_disagreement_score":0.62885326,"about_ca_system_score_codex":0.00064743997,"about_ca_system_score_gemma":0.00020829665,"threshold_uncertainty_score":0.3573354},"labels":[],"label_agreement":null},{"id":"W4417026968","doi":"10.20944/preprints202512.0520.v1","title":"Trends in Wind Energy Forecasting: Umbrella Review of the Recent Advances","year":2025,"lang":"","type":"preprint","venue":"Preprints.org","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Wind power; Work (physics); Wind speed; Scopus; Energy (signal processing); Cube (algebra)","score_opus":0.1089841611202898,"score_gpt":0.3278900769499044,"score_spread":0.2189059158296146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417026968","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052628804,0.35278225,0.00026378635,0.0014003324,0.00915063,0.00085131155,0.00016291873,0.00024127075,0.5825187],"genre_scores_gemma":[0.5149103,0.47665727,0.00019551521,0.0003588211,0.00028650268,0.000114454066,0.000071147595,0.00009449278,0.007311521],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9932606,0.0005325948,0.0027991752,0.0016009777,0.00077773485,0.0010288688],"domain_scores_gemma":[0.99521226,0.0003793202,0.00112042,0.0027716854,0.00031446698,0.00020187192],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017621103,0.0011258675,0.0018131795,0.00063383824,0.00016157924,0.000021642674,0.0022475566,0.0006765132,0.0028348675],"category_scores_gemma":[0.0009299234,0.0010213361,0.00095335254,0.0023820293,0.00022068582,0.00019401117,0.003481723,0.001970752,0.000033887063],"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":[0.00009449275,0.00036244854,0.14238954,0.045182172,0.0005680773,0.000027291971,0.0013163155,0.26115212,0.00065470376,0.0023402413,0.0003367992,0.5455758],"study_design_scores_gemma":[0.0008276148,0.00003207342,0.021726746,0.17051728,0.00037983278,0.000032112308,0.00006410643,0.00934753,0.029459089,0.0011220308,0.76501137,0.0014802298],"about_ca_topic_score_codex":0.00037534293,"about_ca_topic_score_gemma":0.00030121888,"teacher_disagreement_score":0.76467454,"about_ca_system_score_codex":0.00048159758,"about_ca_system_score_gemma":0.00034120047,"threshold_uncertainty_score":0.9992237},"labels":[],"label_agreement":null},{"id":"W4417037889","doi":"10.1016/b978-0-443-33771-0.00023-x","title":"Artificial intelligence in renewable energy technologies and sustainable transition","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Renewable energy; Energy engineering; Energy management; Smart grid; Wind power; Energy consumption; Efficient energy use; Distributed generation; Grid; Energy security","score_opus":0.009484203142155765,"score_gpt":0.20123100475168026,"score_spread":0.1917468016095245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417037889","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000013156946,0.0038255611,0.0032826157,0.000025755699,0.00016147594,0.00010739118,0.0000073063566,0.00040540323,0.99217135],"genre_scores_gemma":[0.021522246,0.0010665379,0.00059163483,0.000031081814,0.00007360108,0.00003974994,0.00002036311,0.00005719314,0.9765976],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989397,0.0000071415725,0.00034606917,0.00026408734,0.00009464644,0.00034836505],"domain_scores_gemma":[0.9996464,0.00004499182,0.000035429992,0.00021464778,0.000030655203,0.000027852118],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00013220427,0.00029047966,0.00032552402,0.00039915726,0.000064817956,0.000044978508,0.00013640642,0.00041116073,0.00001958674],"category_scores_gemma":[0.000011445679,0.00031641574,0.000058780148,0.00003982629,0.00008130296,0.000051850406,0.00006732619,0.00031211655,0.0000020756115],"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":[0.000004710296,0.0000016223798,2.0778324e-7,0.00022030072,0.000019814453,0.000052922824,0.00011928965,0.0030621241,0.000018800256,0.09181385,0.00004462772,0.90464175],"study_design_scores_gemma":[0.00003302575,0.000028874203,1.4209719e-7,0.0009511444,0.00003159107,0.000008965795,0.00023588687,0.0052634994,0.0031540317,0.28529257,0.70456296,0.00043732478],"about_ca_topic_score_codex":0.000020488034,"about_ca_topic_score_gemma":0.0006589542,"teacher_disagreement_score":0.9042044,"about_ca_system_score_codex":0.00009472397,"about_ca_system_score_gemma":0.000037685273,"threshold_uncertainty_score":0.9999288},"labels":[],"label_agreement":null},{"id":"W4417170216","doi":"10.1109/tsg.2025.3642111","title":"Multivariate Power Load Forecasting Model Considering Meteorological Feature-Load Dynamic Forward Lag and Turning Points","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Smart Grid","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":1,"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":"National Natural Science Foundation of China","keywords":"Lag; Focus (optics); Key (lock); Power (physics); Control theory (sociology); Point (geometry); Electric power system; Thermal inertia; Inertia","score_opus":0.017807911752723494,"score_gpt":0.23960467993678772,"score_spread":0.2217967681840642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417170216","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":[0.20264226,0.0015300375,0.7791123,0.00062728534,0.0067832223,0.00053607236,0.00022576177,0.0006136562,0.007929425],"genre_scores_gemma":[0.9764016,0.00035041067,0.020794246,0.0003770196,0.00010130521,0.00011915074,0.0000078581525,0.00013811291,0.0017102595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9956181,0.00017231664,0.0010170932,0.0011975536,0.0006137296,0.0013812203],"domain_scores_gemma":[0.9975881,0.00094646297,0.00019219054,0.00061802793,0.00025945748,0.00039579227],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00089779624,0.0010809259,0.0010570249,0.00050522556,0.0011147562,0.00031655803,0.0003332111,0.00082213554,0.00015754098],"category_scores_gemma":[0.0001521726,0.0011234077,0.00049765303,0.0007351809,0.00024974238,0.000560129,0.000023198792,0.0022412674,0.000032322518],"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":[0.00035174622,0.00015197672,0.00006064711,0.00030788407,0.0007628639,0.00008211162,0.0016694572,0.9651379,0.010589117,0.00016133206,0.000247177,0.020477766],"study_design_scores_gemma":[0.0022793196,0.00021038098,0.00011735732,0.0012815439,0.0004569548,0.00013543904,0.00020286668,0.9808994,0.010601804,0.00048440037,0.0023220489,0.0010085162],"about_ca_topic_score_codex":0.00013930007,"about_ca_topic_score_gemma":0.00044047894,"teacher_disagreement_score":0.77375937,"about_ca_system_score_codex":0.0008288252,"about_ca_system_score_gemma":0.0003492402,"threshold_uncertainty_score":0.9991216},"labels":[],"label_agreement":null},{"id":"W6888806555","doi":"10.24350/cirm.v.19738403","title":"The Dirichlet-to-Neumann map, the boundary Laplacian and Hörmander's rediscovered manuscript","year":2021,"lang":"en","type":"other","venue":"Centre International de Rencontres Mathématiques","topic":"Energy Load and Power Forecasting","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 Montréal","funders":"","keywords":"Boundary (topology); Laplace operator; Eigenvalues and eigenvectors; Calculus (dental); Joint (building); Square (algebra)","score_opus":0.005866957266894936,"score_gpt":0.21348721638117704,"score_spread":0.2076202591142821,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888806555","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009060045,0.025988575,0.00083209586,0.005015992,0.00456594,0.00032033084,0.00023382006,0.000496361,0.9616409],"genre_scores_gemma":[0.029946463,0.011647262,0.001121266,0.0010161347,0.0027578874,0.000060910203,0.00047095458,0.00059326773,0.95238584],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99880457,0.00007044714,0.0002803909,0.00023824483,0.0002761032,0.00033022198],"domain_scores_gemma":[0.99927235,0.00019189231,0.00009681595,0.00029287112,0.000047914535,0.000098176184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015789105,0.00030128882,0.00022850136,0.00010130929,0.00014323313,0.00052843924,0.00047093022,0.00016218684,0.0006339556],"category_scores_gemma":[0.00008298234,0.00019996353,0.000091603106,0.00005254046,0.00008319825,0.000059546805,0.00014618093,0.00029503784,0.000028569093],"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":[0.000008139675,0.000010279243,0.00023979915,0.00011342773,0.00036681804,0.000046502297,0.0010210953,0.00014211074,0.000054874363,0.0049726,0.9873991,0.0056252764],"study_design_scores_gemma":[0.00013427081,0.000006333859,0.00040573912,0.0009246832,0.000023586312,0.000032453092,0.00018852229,0.0025658375,0.00012668161,0.0013203928,0.9940366,0.00023488546],"about_ca_topic_score_codex":0.00015962755,"about_ca_topic_score_gemma":0.004593153,"teacher_disagreement_score":0.029040458,"about_ca_system_score_codex":0.00010415448,"about_ca_system_score_gemma":0.000041235886,"threshold_uncertainty_score":0.8154276},"labels":[],"label_agreement":null},{"id":"W6888956327","doi":"10.25316/ir-9522","title":"Nanaimo Free Press [Saturday, July 9, 1898]","year":2019,"lang":"en","type":"other","venue":"VIUSpace (Vancouver Island University Library)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"","score_opus":0.005622555932975578,"score_gpt":0.15925099250962194,"score_spread":0.15362843657664635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6888956327","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000014365689,0.001332838,0.00044242977,0.000042663858,0.0033382268,0.00023037668,0.0005875298,0.0017647622,0.9922468],"genre_scores_gemma":[0.00047932327,0.0012568639,0.0009157117,0.00004698145,0.00053973653,7.223502e-7,0.000026475002,0.00063676183,0.99609745],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99862236,0.00004166146,0.0001365052,0.0004405909,0.0002523724,0.00050648913],"domain_scores_gemma":[0.99882096,0.000053211854,0.00010669711,0.0008342996,0.0000105668805,0.00017428218],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000019844943,0.0005313294,0.00049437286,0.00037275229,0.000073263414,0.000069743925,0.0008689934,0.000596184,0.000874306],"category_scores_gemma":[0.000005656913,0.0005624645,0.00019421824,0.0002908103,0.00005882589,0.00050797156,0.0003292474,0.00053936534,0.00023851568],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000018549483,0.000015411792,0.000009505636,0.00025514135,0.00019714775,0.0001116655,0.00014620273,0.0014597169,0.00000561106,0.00039459538,0.9970864,0.00030007368],"study_design_scores_gemma":[0.00087104825,0.000030869156,0.0000043785044,0.00038288656,0.00007599274,0.0000018774898,0.000059060403,0.00054901437,0.0001469384,0.000027202384,0.99714243,0.0007082999],"about_ca_topic_score_codex":0.00036622884,"about_ca_topic_score_gemma":0.07322014,"teacher_disagreement_score":0.07285391,"about_ca_system_score_codex":0.00005475607,"about_ca_system_score_gemma":0.000060560764,"threshold_uncertainty_score":0.99968266},"labels":[],"label_agreement":null},{"id":"W6891933802","doi":"10.48550/arxiv.1907.07836","title":"Multi-year Long-term Load Forecast for Area Distribution Feeders based on Selective Sequence Learning","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Energy Load and Power Forecasting","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":"Sequence (biology); Window (computing); Artificial neural network; Sequence learning; Distribution (mathematics)","score_opus":0.06894315995300365,"score_gpt":0.1946492127351088,"score_spread":0.12570605278210514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6891933802","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":[0.52852553,0.000021138247,0.46772236,0.000004877491,0.0006078203,0.00042989943,0.00020469273,0.00041042716,0.0020732642],"genre_scores_gemma":[0.9979165,0.00004459977,0.00033242226,0.0000147395685,0.00007997233,0.0000055033024,0.00085449946,0.000073785624,0.0006779767],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984342,0.00005298459,0.00018601728,0.0007274239,0.000099939294,0.00049947674],"domain_scores_gemma":[0.9989348,0.00019549803,0.00014265711,0.00039982426,0.0001949109,0.00013230082],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00017564037,0.00044248346,0.00036118945,0.00014600302,0.00017709457,0.00006784294,0.000363171,0.0004139293,0.000018897852],"category_scores_gemma":[0.000090070076,0.00054180395,0.00027815526,0.0002807598,0.00006217282,0.00014431354,0.00012796508,0.0008534331,0.000034155346],"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":[0.00010980439,0.00003489108,0.020894269,0.00028157333,0.00009709401,0.00003482597,0.00008875517,0.9775183,0.0001745198,0.00039658137,0.00009457442,0.00027479662],"study_design_scores_gemma":[0.0010810654,0.000118890566,0.0023854717,0.0005082822,0.00009825181,0.0000014253902,0.00006845942,0.994142,0.0007454395,0.00008606995,0.00016716485,0.00059747073],"about_ca_topic_score_codex":0.00004754805,"about_ca_topic_score_gemma":0.000075767646,"teacher_disagreement_score":0.46939096,"about_ca_system_score_codex":0.0012194888,"about_ca_system_score_gemma":0.00015586165,"threshold_uncertainty_score":0.99970335},"labels":[],"label_agreement":null},{"id":"W6893614767","doi":"10.5281/zenodo.235165","title":"jhpoelen/eol-globi-data v0.8.6","year":2017,"lang":"en","type":"other","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Energy Load and Power Forecasting","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 New Brunswick","funders":"","keywords":"Context (archaeology); Production (economics); Term (time); Identification (biology); Relation (database)","score_opus":0.04926350925216444,"score_gpt":0.24354408512095263,"score_spread":0.1942805758687882,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6893614767","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000019026647,0.00085447973,0.0019311863,0.00005356425,0.00056673255,0.00020309575,0.0014356473,0.0035840904,0.9913522],"genre_scores_gemma":[0.011241549,0.0024429657,0.0010327393,0.00008016945,0.0029139535,6.693736e-8,0.024808662,0.03645401,0.9210259],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984694,0.0000854321,0.0001918596,0.00049217715,0.00030423515,0.00045689626],"domain_scores_gemma":[0.9978163,0.000011185696,0.000119159646,0.0017759149,0.000095885036,0.00018154777],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00033672556,0.000286512,0.00025550183,0.00028535226,0.0010041345,0.0007738897,0.0030061628,0.00021475462,0.028984912],"category_scores_gemma":[0.00027048725,0.00031656818,0.00005184438,0.00016486326,0.00012750519,0.00019299706,0.001824432,0.0004362193,0.013267201],"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":[0.0000032566209,0.0000140320035,2.5436034e-7,0.00013276568,0.000082146245,0.000019430763,0.000071463626,0.00009216188,0.00015352949,0.00039137766,0.89444125,0.104598306],"study_design_scores_gemma":[0.00022444055,0.000031808107,0.0000068683885,0.00024663613,0.000022775022,0.000056239638,0.000014240959,0.0015627983,0.000054029428,0.000018684827,0.9974256,0.0003358616],"about_ca_topic_score_codex":0.000046290003,"about_ca_topic_score_gemma":0.0000043639407,"teacher_disagreement_score":0.10426245,"about_ca_system_score_codex":0.00008723763,"about_ca_system_score_gemma":0.000003215797,"threshold_uncertainty_score":0.99992865},"labels":[],"label_agreement":null},{"id":"W6924716048","doi":"10.15468/dl.vvoh9j","title":"Occurrence Download","year":2018,"lang":"en","type":"dataset","venue":"Global Biodiversity Information Facility","topic":"Energy Load and Power Forecasting","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":"Download; Matching (statistics); Range (aeronautics); Confidentiality; Real world data","score_opus":0.01174718471485418,"score_gpt":0.1945832453594582,"score_spread":0.18283606064460403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6924716048","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056784315,0.000029356806,0.000010420662,0.0000083878585,0.0014917256,0.00010236139,0.996857,0.00029451642,0.0006383949],"genre_scores_gemma":[0.000030523333,0.00007602109,0.0000026157963,0.00010542,0.000005257751,0.0000018306475,0.9997783,2.036224e-8,5.99818e-8],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99896467,0.000017821836,0.0003075799,0.00013197707,0.00028263353,0.00029532073],"domain_scores_gemma":[0.9992852,0.000011289247,0.00009134994,0.0003597864,0.0001190134,0.0001333129],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00013108016,0.00027665912,0.0002162114,0.00007300527,0.0001649261,0.000109752335,0.0003844628,0.00033596516,0.0013318427],"category_scores_gemma":[0.00004556985,0.00029590767,0.00010584728,0.00023320834,0.00011131403,0.0006923825,0.0001403893,0.00023690707,0.11331747],"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":[0.000007833849,0.000005802945,0.00014499237,0.00018802671,0.000030081017,0.0000011484659,0.00002868785,0.00025878372,4.2737486e-8,3.593539e-8,0.99839705,0.0009374981],"study_design_scores_gemma":[0.00014944498,0.00001919897,0.000012300109,0.0000057350894,0.000027449949,0.000004488961,0.000019387737,0.000004340984,0.000009190842,4.2100424e-7,0.9994519,0.00029616404],"about_ca_topic_score_codex":0.00012063698,"about_ca_topic_score_gemma":0.000017686221,"teacher_disagreement_score":0.111985624,"about_ca_system_score_codex":0.00021555588,"about_ca_system_score_gemma":0.00003930691,"threshold_uncertainty_score":0.9999493},"labels":[],"label_agreement":null},{"id":"W6949863246","doi":"10.5281/zenodo.5905694","title":"USING XGBOOST MODEL WITH FEATURE SELECTION TECHNIQUES FOR WIND SPEED FORECASTING","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Energy Load and Power Forecasting","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":"Simon Fraser University","funders":"","keywords":"Feature selection; Wind speed; Feature (linguistics); Wind power; Boosting (machine learning); Selection (genetic algorithm); Feature extraction","score_opus":0.05002640933998671,"score_gpt":0.2320247811708171,"score_spread":0.1819983718308304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6949863246","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":[0.26311463,0.0002270655,0.54670614,0.00036103252,0.000487754,0.00241418,0.0009442153,0.009210205,0.17653479],"genre_scores_gemma":[0.9787659,0.0000072317794,0.017110543,0.000056006982,0.00022558583,1.2368105e-7,0.00086197216,0.002049917,0.0009227181],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99895144,0.00006268406,0.00014658594,0.0002525201,0.00023851923,0.00034828222],"domain_scores_gemma":[0.9994918,0.000016110507,0.00005644466,0.00015811529,0.00019730648,0.00008021606],"candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00035789434,0.00014824128,0.00012236333,0.0002039063,0.0025294153,0.0002698694,0.0003448178,0.000045305344,0.0006188388],"category_scores_gemma":[0.000065606924,0.00016072267,0.00004026453,0.00050735986,0.000035355308,0.00021138725,0.00029400922,0.00032906918,0.00001802229],"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":[0.000094530675,0.000035015775,0.000004950166,0.000105328465,0.00005324613,0.00000585357,0.0008266807,0.9087831,0.030569468,0.00077708776,0.028642397,0.030102367],"study_design_scores_gemma":[0.00023629144,0.0001792253,0.0000034578788,0.000026453934,0.000015222857,0.00023785222,0.00014071401,0.67107844,0.0031323193,0.00007896086,0.32469133,0.0001797483],"about_ca_topic_score_codex":0.000005148875,"about_ca_topic_score_gemma":3.9463785e-7,"teacher_disagreement_score":0.7156513,"about_ca_system_score_codex":0.00025717414,"about_ca_system_score_gemma":0.000004432876,"threshold_uncertainty_score":0.99876916},"labels":[],"label_agreement":null},{"id":"W6979306437","doi":"","title":"Wasserstein Distributionally Robust Shallow Convex Neural Networks","year":2024,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Energy Load and Power Forecasting","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":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Hydro-Québec","keywords":"Artificial neural network; Convex optimization; Context (archaeology); Stability (learning theory); Benchmark (surveying); Regular polygon; Nonlinear system; Scalability","score_opus":0.03467961227404202,"score_gpt":0.14878216806204403,"score_spread":0.11410255578800202,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6979306437","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":[0.6335497,0.00075682905,0.35138202,0.000042526728,0.0013994626,0.00006896825,0.000030325791,0.0011192434,0.011650927],"genre_scores_gemma":[0.9985262,0.00007374092,0.00003810863,0.000021444643,0.00017849957,2.9839325e-7,0.00006267188,0.00002700477,0.0010720018],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992921,0.00001752471,0.0001009808,0.00025896585,0.000037943628,0.00029253177],"domain_scores_gemma":[0.9996238,0.00007416869,0.000011463915,0.00015697013,0.000023864124,0.00010973476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007047632,0.00015799109,0.00012050546,0.0000738062,0.00007891854,0.00006400478,0.00018324373,0.0000977468,0.00015293383],"category_scores_gemma":[0.000006202509,0.00017721113,0.000105275445,0.00045849246,0.000043224714,0.00025902822,0.00004419974,0.00024504252,0.000054009648],"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":[0.000005598971,0.000005574371,0.0013483876,0.000029414481,0.00005540654,0.00031471302,0.000019722911,0.94782573,0.00004974773,0.049274266,0.0006855022,0.00038596216],"study_design_scores_gemma":[0.00012605193,0.00001554569,0.0003597924,0.000048745726,0.000032648273,0.000010591787,0.000033600525,0.99571747,0.000042127067,0.00033655373,0.0030726823,0.00020422332],"about_ca_topic_score_codex":0.00001425279,"about_ca_topic_score_gemma":0.000037406113,"teacher_disagreement_score":0.36497656,"about_ca_system_score_codex":0.000104755534,"about_ca_system_score_gemma":0.000014294652,"threshold_uncertainty_score":0.722646},"labels":[],"label_agreement":null},{"id":"W6980406719","doi":"","title":"Can AI be used in the fight for sustainable electricity? Electricity Canada says yes","year":2024,"lang":"en","type":"other","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Electricity; Government (linguistics); Electricity system; Mains electricity; Sustainability","score_opus":0.006860429235153057,"score_gpt":0.20242319256294689,"score_spread":0.19556276332779382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6980406719","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00008804193,0.002974134,0.00067200355,0.0007817248,0.00037430297,0.00065375917,0.00007472969,0.00044096616,0.99394035],"genre_scores_gemma":[0.047303308,0.00007564578,0.00008105646,0.0010633928,0.00043781253,0.0002297998,0.0000882024,0.0005421091,0.9501787],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99861526,0.000022718692,0.00020050771,0.00024608054,0.00021653021,0.0006988759],"domain_scores_gemma":[0.99953026,0.00013544776,0.000028170458,0.00023036617,0.000024633844,0.000051107312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016456042,0.00031566163,0.000298662,0.0002839343,0.000042038973,0.00009021779,0.00030637643,0.0002108637,0.0003106729],"category_scores_gemma":[0.000046495,0.00023154312,0.00007337021,0.00064443203,0.000008379509,0.000029325729,0.000018668377,0.00044030143,0.000003585792],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000001460638,0.0000054020493,0.000016359878,0.0004069856,0.000058554237,0.000058959893,0.00007820161,0.0008440486,0.000010211117,0.010144945,0.988151,0.00022388255],"study_design_scores_gemma":[0.0001261023,0.000022373597,0.0000022031895,0.000069249414,0.000030448527,0.0000046353343,0.00009427527,0.0056317416,0.0004955709,0.0005731896,0.9926305,0.00031974952],"about_ca_topic_score_codex":0.64653444,"about_ca_topic_score_gemma":0.95705974,"teacher_disagreement_score":0.31052533,"about_ca_system_score_codex":0.0003835241,"about_ca_system_score_gemma":0.00036839055,"threshold_uncertainty_score":0.94420546},"labels":[],"label_agreement":null},{"id":"W6989293517","doi":"","title":"Analyse de l'impact des variables météorologiques sur la prévision de la demande énergétique au Québec","year":2023,"lang":"fr","type":"other","venue":"Archipelago (University of Quebec in Montreal)","topic":"Energy Load and Power Forecasting","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":"Statistical analysis; Factorial analysis; Ceriodaphnia dubia","score_opus":0.007402536056448434,"score_gpt":0.21403127258070456,"score_spread":0.20662873652425612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6989293517","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":[0.91371244,0.0009460999,0.013936672,0.00007475103,0.00008214636,0.00014634083,0.000071963645,0.00033318266,0.070696376],"genre_scores_gemma":[0.94579685,0.0027562631,0.010003192,0.000010796104,0.00012542597,0.0000016840817,0.00003714541,0.0002825841,0.04098603],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99721205,0.0010312815,0.0003063278,0.00043225088,0.00019018896,0.00082791486],"domain_scores_gemma":[0.9964607,0.002621274,0.000220586,0.0003709647,0.00004644315,0.00028005076],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011953819,0.00050558225,0.0007747868,0.0007183006,0.00018407319,0.00003899312,0.00056480966,0.00088125025,0.0004952426],"category_scores_gemma":[0.00036812504,0.0005924505,0.00038052705,0.00049950316,0.00056356477,0.00026126628,0.00020323404,0.0008111317,0.000029801935],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017874586,0.0002035106,0.2390956,0.0005591626,0.0007071445,0.0012277432,0.05537508,0.37264377,0.00060152216,0.0029126732,0.0008854952,0.32560956],"study_design_scores_gemma":[0.0011620822,0.00015701403,0.90436536,0.002449128,0.0003350977,0.00006368594,0.004647623,0.07183303,0.00012501753,0.011688752,0.0023898466,0.0007833713],"about_ca_topic_score_codex":0.9935827,"about_ca_topic_score_gemma":0.9994156,"teacher_disagreement_score":0.66526973,"about_ca_system_score_codex":0.0017995784,"about_ca_system_score_gemma":0.0013427378,"threshold_uncertainty_score":0.9996527},"labels":[],"label_agreement":null},{"id":"W6990670108","doi":"","title":"Dynamic Bayesian smooth transition autoregressive (DBSTAR) models for non-stationary nonlinear time series","year":2014,"lang":"en","type":"dissertation","venue":"Open Research Online (The Open University)","topic":"Energy Load and Power Forecasting","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":"Autoregressive model; STAR model; Bayesian probability; Series (stratigraphy); Parametric statistics; Nonlinear system; Time series; Parametric model; Nonlinear autoregressive exogenous model; Convergence (economics)","score_opus":0.02868742872314285,"score_gpt":0.3042889015516666,"score_spread":0.27560147282852376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990670108","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050891396,0.00044290963,0.05317996,0.0035353235,0.0019650413,0.01887825,0.008346699,0.0006015693,0.86215883],"genre_scores_gemma":[0.05154691,0.0016456177,0.10237738,0.00013340806,0.0007359788,0.0002540161,0.11134796,0.000845557,0.7311132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99766266,0.0002386288,0.00033656278,0.0006031601,0.00050206936,0.0006569088],"domain_scores_gemma":[0.99813133,0.00035201688,0.00013340413,0.0005848002,0.0006031992,0.00019524722],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008462991,0.00041638294,0.0005507477,0.0005541056,0.000983855,0.00060939684,0.0032213244,0.0003614612,0.00023665474],"category_scores_gemma":[0.00003711749,0.00039338195,0.00015992773,0.0006803214,0.00012010162,0.0015864751,0.0003516145,0.0008953211,0.00004162165],"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":[0.007552292,0.0008873581,0.00000775892,0.0028894742,0.0019749848,0.0004898669,0.014426973,0.80787605,0.0036427795,0.029137654,0.030860659,0.10025417],"study_design_scores_gemma":[0.0012389212,0.00025793645,0.000036664766,0.0008574386,0.00008840903,0.000003855979,0.003255249,0.80996585,0.00024840047,0.0015697083,0.1818895,0.0005880436],"about_ca_topic_score_codex":0.00051154906,"about_ca_topic_score_gemma":0.004922857,"teacher_disagreement_score":0.15102884,"about_ca_system_score_codex":0.0003357764,"about_ca_system_score_gemma":0.00054896215,"threshold_uncertainty_score":0.9998518},"labels":[],"label_agreement":null},{"id":"W6990780006","doi":"","title":"Electricity Consumption Forecasting in Algeria using ARIMA and Long Short-Term Memory Neural Network","year":2023,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Energy Load and Power Forecasting","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":"Medicine Hat College","funders":"","keywords":"Autoregressive integrated moving average; Electricity; Artificial neural network; Time series; Consumption (sociology); Box–Jenkins; Mains electricity; Grid; Energy consumption","score_opus":0.2633964608150226,"score_gpt":0.47263385857339524,"score_spread":0.20923739775837263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990780006","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":[0.9877485,0.009477583,0.00068636733,0.000006513391,0.0008821981,0.00024276559,0.000007909397,0.00016063577,0.0007875035],"genre_scores_gemma":[0.99566734,0.003602972,0.00023856781,0.000027917238,0.00032889718,0.000014195392,0.000017146114,0.000080562786,0.000022378852],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9977212,0.00013379009,0.0007910135,0.00034381627,0.00032630845,0.0006838656],"domain_scores_gemma":[0.9989422,0.00036187415,0.0002198048,0.00022068949,0.00006770333,0.00018773752],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011523119,0.00033358746,0.0006206359,0.0007667808,0.00023480065,0.0007083338,0.00070320815,0.00013732025,0.00038553917],"category_scores_gemma":[0.0001151759,0.00035398538,0.000097942924,0.0014474541,0.00006837899,0.0015876074,0.00043849094,0.0005297838,0.0000028928773],"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":[0.000037059246,0.00001475246,0.7053138,0.00014734022,0.0000653169,0.00012683003,0.00011753801,0.22557782,0.041275725,0.000008362313,0.00053088303,0.026784593],"study_design_scores_gemma":[0.00028676182,0.0000050471485,0.6759605,0.000673535,0.000042277392,0.000075674885,0.000015233911,0.31647992,0.0055814795,0.00042598287,0.000041661435,0.00041197438],"about_ca_topic_score_codex":0.00022322443,"about_ca_topic_score_gemma":0.00021365238,"teacher_disagreement_score":0.0909021,"about_ca_system_score_codex":0.00011766393,"about_ca_system_score_gemma":0.000036348447,"threshold_uncertainty_score":0.9998912},"labels":[],"label_agreement":null},{"id":"W6990995871","doi":"","title":"Ensemble of artifical neural networks for seasonal forecasting of wind speed in eastern Canada","year":2024,"lang":"en","type":"dissertation","venue":"Skemman","topic":"Energy Load and Power Forecasting","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":"Wind speed; Artificial neural network; Precipitation; Pointwise; Mean squared error; Wind power; Forecast skill; Climate change","score_opus":0.02101254582950182,"score_gpt":0.22777043696350582,"score_spread":0.206757891134004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6990995871","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":[0.99247295,0.0007652091,0.000097622215,0.00000517372,0.002612635,0.00016244914,0.000050170227,0.00002869827,0.0038050862],"genre_scores_gemma":[0.9977932,0.0000037489394,0.00008151418,0.000003547328,0.00026544454,0.0000042542406,0.00060251704,0.00007181453,0.0011739612],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885744,0.000010533003,0.00047307287,0.00018491792,0.00017697047,0.000297053],"domain_scores_gemma":[0.99957544,0.000112729766,0.00009227619,0.000113644724,0.000049784892,0.000056111385],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010250764,0.00021605277,0.00036693428,0.00010349071,0.000017341223,0.000013858968,0.000120583645,0.00017414593,0.00001279856],"category_scores_gemma":[0.000030267078,0.00023082661,0.00010589265,0.00017528427,0.000010885611,0.000036342248,0.000011198504,0.00029071714,3.151167e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000073177915,0.000012193472,0.0012129493,0.002319246,0.00009197863,0.000032430158,0.0006262071,0.97674286,0.0008957059,0.00023562428,0.0008380662,0.01691956],"study_design_scores_gemma":[0.00016576378,0.000030617244,0.0005354542,0.00081104913,0.000040275052,0.0000036810295,0.000209367,0.9949748,0.0025036095,0.00008750545,0.00043652297,0.00020135255],"about_ca_topic_score_codex":0.017839033,"about_ca_topic_score_gemma":0.47888935,"teacher_disagreement_score":0.4610503,"about_ca_system_score_codex":0.000066054694,"about_ca_system_score_gemma":0.000111773785,"threshold_uncertainty_score":0.9887013},"labels":[],"label_agreement":null},{"id":"W6996661847","doi":"","title":"Short term electricity price forecasting using neural network","year":2013,"lang":"en","type":"article","venue":"Universiti Utara Malaysia Institutional Repository (Universiti Utara Malaysia)","topic":"Energy Load and Power Forecasting","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":"Universiti Teknikal Malaysia Melaka","keywords":"Electricity price forecasting; Electricity price; Artificial neural network; Term (time); Electricity; Profit (economics); Electricity market; Demand forecasting","score_opus":0.01607769620198988,"score_gpt":0.1848646485672437,"score_spread":0.16878695236525382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6996661847","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":[0.9629674,0.00026221233,0.0071054003,0.000057911227,0.0013693272,0.00044952033,0.000024292343,0.0007482523,0.027015688],"genre_scores_gemma":[0.99483657,0.000027213024,0.0034285022,0.000078405006,0.0009221858,0.000007747847,0.0001078252,0.00008420335,0.00050733803],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99662954,0.0001094909,0.00052208314,0.0007621622,0.0006418907,0.0013348333],"domain_scores_gemma":[0.9983492,0.00015260866,0.00016831697,0.0005100653,0.00029811764,0.0005217241],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001700721,0.0007116619,0.0005735377,0.0005614197,0.0011171445,0.000241781,0.00074473873,0.00037971532,0.00026916538],"category_scores_gemma":[0.000017158301,0.0008911182,0.00038535052,0.0012396602,0.0003536109,0.0029116413,0.00028857467,0.0008054229,0.00007716852],"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":[0.0000465739,0.000049152637,0.0059899003,0.00008084873,0.00023663892,0.0013418769,0.00021181544,0.98036575,0.00862819,0.0015437086,0.0003478956,0.0011576387],"study_design_scores_gemma":[0.00057233253,0.00008819961,0.0004361271,0.00021205378,0.00018446411,0.0015950924,0.00034579993,0.99374235,0.00081501325,0.00012079219,0.0009364434,0.0009513298],"about_ca_topic_score_codex":0.00018324096,"about_ca_topic_score_gemma":3.301051e-7,"teacher_disagreement_score":0.031869184,"about_ca_system_score_codex":0.0015602156,"about_ca_system_score_gemma":0.0001921452,"threshold_uncertainty_score":0.99935395},"labels":[],"label_agreement":null},{"id":"W6997329663","doi":"","title":"webroot SUPPORT phone Number 180.O7.5O.6584 usa, webroot customer Support Phone Number, webroot Antivirus contact Phone Number","year":2016,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Energy Load and Power Forecasting","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":"Phone; Mobile phone; Service (business); Telephone banking; Mobile phone tracking","score_opus":0.011713544119669782,"score_gpt":0.24852666965409526,"score_spread":0.23681312553442546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6997329663","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009202876,0.000015504047,0.0015911773,0.00007392955,0.0062608523,0.0015689216,0.00079948484,0.0022792292,0.978208],"genre_scores_gemma":[0.11740729,0.0013311662,0.0015822443,0.00024270974,0.0019706504,0.00059156376,0.00047812113,0.002443946,0.8739523],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9867877,0.0006792688,0.0025851964,0.0049632057,0.0018726786,0.0031119601],"domain_scores_gemma":[0.989455,0.0006706109,0.0011948673,0.0070272773,0.00031178698,0.0013404638],"candidate_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.0031814545,0.0028907594,0.0030947474,0.0006294033,0.0003912106,0.00043366873,0.0030899222,0.0024509802,0.97011846],"category_scores_gemma":[0.00052341126,0.003041049,0.0012914124,0.00087548385,0.00055734586,0.0008014044,0.0021515738,0.0026837445,0.9865883],"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":[0.0002154476,0.000617684,0.047397085,0.00090741244,0.0018440753,0.00045409982,0.0005832034,0.0002466662,0.008363956,0.00046682978,0.92992836,0.008975165],"study_design_scores_gemma":[0.0033842507,0.0000028360214,0.0020105205,0.0012008359,0.00039569635,0.0007368866,0.00005447134,0.00016097906,0.019824956,0.00025157438,0.9685244,0.0034525774],"about_ca_topic_score_codex":0.0024754708,"about_ca_topic_score_gemma":0.0017617919,"teacher_disagreement_score":0.10820442,"about_ca_system_score_codex":0.0010055258,"about_ca_system_score_gemma":0.0006047452,"threshold_uncertainty_score":0.9996171},"labels":[],"label_agreement":null},{"id":"W6997733344","doi":"","title":"Формування в розумово відсталих учнів умінь читацької діяльності у процесі позакласного читання","year":2016,"lang":"uk","type":"article","venue":"ENPUIR (National Pedagogical Dragomanov University)","topic":"Energy Load and Power Forecasting","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":"Series (stratigraphy); Subject (documents); Volume (thermodynamics)","score_opus":0.0656920806842098,"score_gpt":0.25512746334659886,"score_spread":0.18943538266238907,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6997733344","genre_codex":"other","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":[0.18781112,0.001144444,0.028883584,0.0043268036,0.0038608802,0.00066380244,0.0008595528,0.0015615993,0.7708882],"genre_scores_gemma":[0.88186276,0.0006093424,0.0011157782,0.00031994106,0.00182135,0.000004448466,0.00011852597,0.00015697676,0.11399089],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9943721,0.0002990935,0.00084544823,0.0013681261,0.0015863932,0.0015287931],"domain_scores_gemma":[0.99640614,0.0012040937,0.00030823948,0.0006561641,0.0005886692,0.00083672313],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00051448634,0.0010450113,0.00084660697,0.0011028063,0.00072154263,0.00018216233,0.0013258281,0.00083884026,0.0057547092],"category_scores_gemma":[0.00048401247,0.000955578,0.00067783846,0.0015484458,0.00053741777,0.0012901339,0.0005957238,0.0008504033,0.002063697],"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":[0.00044400003,0.00063520134,0.003240933,0.00027329504,0.0007616435,0.0015911391,0.0013568274,0.0111617185,0.011678961,0.9292154,0.014260112,0.025380803],"study_design_scores_gemma":[0.0037073009,0.00034363984,0.0073278495,0.00062250555,0.00020244562,0.00010532304,0.00039076764,0.0025199223,0.0016207388,0.005334195,0.9758193,0.0020060476],"about_ca_topic_score_codex":0.000091177186,"about_ca_topic_score_gemma":0.00016688573,"teacher_disagreement_score":0.9615592,"about_ca_system_score_codex":0.001208927,"about_ca_system_score_gemma":0.0006523814,"threshold_uncertainty_score":0.99928945},"labels":[],"label_agreement":null},{"id":"W7014637515","doi":"","title":"Prévision à court terme du besoin électrique québécois","year":2023,"lang":"fr","type":"other","venue":"Archipelago (Université du Québec à Montréal)","topic":"Energy Load and Power Forecasting","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":"Limiting; Statistical analysis; Sampling error; Context (archaeology)","score_opus":0.004501146344327939,"score_gpt":0.15582651826391883,"score_spread":0.1513253719195909,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7014637515","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":[0.6384077,0.027541058,0.015454747,0.011933447,0.005416429,0.0014084044,0.0003551609,0.005972824,0.29351023],"genre_scores_gemma":[0.7707736,0.014146635,0.0024653599,0.0002913483,0.0028569337,0.00003478654,0.00014549981,0.0030707428,0.2062151],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99660265,0.00018443872,0.00055803085,0.00094334374,0.0005034519,0.0012080717],"domain_scores_gemma":[0.9974463,0.00073627435,0.00029528822,0.0008744165,0.0000909648,0.0005567608],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00029312642,0.0010748393,0.000984012,0.00080477534,0.000594986,0.00009240636,0.00095162826,0.00074310956,0.0039144405],"category_scores_gemma":[0.00015927116,0.0012362851,0.0006145148,0.0007322916,0.00024342659,0.00032797715,0.0004569789,0.0011523727,0.0029138722],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027796178,0.00040869488,0.0066615897,0.00097211485,0.0022501238,0.005415573,0.2811975,0.05075081,0.0015172611,0.02206187,0.10557449,0.522912],"study_design_scores_gemma":[0.0014723245,0.00029044828,0.0067524635,0.0014954668,0.00038601144,0.0002256503,0.0057722274,0.05302133,0.00012468867,0.00039214376,0.9283624,0.0017047852],"about_ca_topic_score_codex":0.83203846,"about_ca_topic_score_gemma":0.9756083,"teacher_disagreement_score":0.82278794,"about_ca_system_score_codex":0.0015979813,"about_ca_system_score_gemma":0.00067761715,"threshold_uncertainty_score":0.9990087},"labels":[],"label_agreement":null},{"id":"W7017727861","doi":"","title":"BluWave-Ai Releases Electricity System AI Load Predictor for Ontario, Canada","year":2023,"lang":"en","type":"other","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Electricity; Mains electricity; Electricity system; Production (economics); Measure (data warehouse)","score_opus":0.006617935471304346,"score_gpt":0.180583008425527,"score_spread":0.17396507295422264,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7017727861","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00025276584,0.0009624028,0.0048713908,0.000029159837,0.004891501,0.0005486028,0.0005003761,0.005951346,0.9819925],"genre_scores_gemma":[0.014751829,0.000035377052,0.00024478705,0.00013456488,0.0010638712,0.000120776196,0.00010829353,0.0015438846,0.9819966],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9986359,0.000008874107,0.0002742889,0.00028865857,0.00029256483,0.00049973937],"domain_scores_gemma":[0.999393,0.000091258284,0.000054829867,0.00027674934,0.000044928787,0.00013922145],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000053829506,0.00037008035,0.00042287528,0.0001179219,0.000047708763,0.000035308647,0.00019425275,0.00029144873,0.00070725026],"category_scores_gemma":[0.00003224454,0.00035073818,0.000105009065,0.00016668937,0.000008512666,0.000032214703,0.000020901189,0.00029654746,0.00002718447],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000039297265,0.000003398069,0.00018202192,0.0005379804,0.00019109742,0.000022074577,0.000015977164,0.001912048,0.0000050047433,0.0006851443,0.995916,0.0005253382],"study_design_scores_gemma":[0.00023759215,0.000026354928,0.000025831925,0.0006463951,0.000062266525,0.000007601711,0.000016910242,0.011500575,0.00016353189,0.0000049751416,0.9868749,0.00043308162],"about_ca_topic_score_codex":0.98188764,"about_ca_topic_score_gemma":0.9994734,"teacher_disagreement_score":0.017585738,"about_ca_system_score_codex":0.001803468,"about_ca_system_score_gemma":0.0009517799,"threshold_uncertainty_score":0.99989444},"labels":[],"label_agreement":null},{"id":"W7018699419","doi":"","title":"Dry Type Transformer Market Forecast - 2027 | North America 6.5%+ CAGR by Canada, United States","year":2024,"lang":"en","type":"other","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Economic forecasting; Transformer; Consensus forecast; Revenue","score_opus":0.004388086250363652,"score_gpt":0.1720011458748116,"score_spread":0.16761305962444795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7018699419","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00009956681,0.0045665093,0.000594056,0.000052095016,0.0016966892,0.0001248525,0.0018053242,0.0007758235,0.9902851],"genre_scores_gemma":[0.0004574152,0.0018312052,0.00035993673,0.00027240015,0.0002359037,0.000013409294,0.0032340349,0.00097500277,0.9926207],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987872,0.000013228194,0.00023942205,0.00027406498,0.0002185316,0.0004675183],"domain_scores_gemma":[0.9995362,0.000031756303,0.000030845415,0.0002186823,0.000020239466,0.00016227606],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00002740202,0.00044540956,0.0003391974,0.00023674933,0.000026153179,0.000043308766,0.00018364837,0.00017710062,0.014168],"category_scores_gemma":[0.000004038051,0.00039203098,0.00006351152,0.00060998154,0.000032508076,0.000028302411,0.000012929421,0.00035887767,0.00016610889],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000003693747,0.0000043240007,0.00004526954,0.00031918116,0.00026073263,0.000029375173,0.00008185989,0.0012908016,0.0000055403852,0.0000079932015,0.9939126,0.004038657],"study_design_scores_gemma":[0.00007950223,0.000020245405,0.0000036005574,0.00019005986,0.000055713404,0.0000024463432,0.00007793139,0.008866944,0.000026823214,0.000004551293,0.9901771,0.0004950639],"about_ca_topic_score_codex":0.639303,"about_ca_topic_score_gemma":0.9026509,"teacher_disagreement_score":0.26334786,"about_ca_system_score_codex":0.000114640185,"about_ca_system_score_gemma":0.000109078006,"threshold_uncertainty_score":0.99985313},"labels":[],"label_agreement":null},{"id":"W7023595177","doi":"","title":"Parental sleep patterns and variability at 6 months postpartum: Associations with family factors and depressive symptoms","year":2023,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Social Sciences and Humanities Research Council of Canada; McGill University","keywords":"Depressive symptoms; Depression (economics); Sleep patterns; Sleep (system call); Disease","score_opus":0.009486352190197037,"score_gpt":0.2068559143980804,"score_spread":0.19736956220788338,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7023595177","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":[0.98230743,0.0002105273,4.8141465e-7,0.0000018947785,0.0010015377,0.00038788602,0.0042989464,0.0006645777,0.0111267315],"genre_scores_gemma":[0.9948843,0.00027828536,0.000039859406,0.000018491784,0.00003861441,0.00010636562,0.0035955675,0.0002606626,0.00077782443],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99730706,0.00014756918,0.0005746633,0.0008332766,0.00048659183,0.0006508395],"domain_scores_gemma":[0.99834174,0.00051822653,0.0002590193,0.0003947125,0.00014128053,0.00034502073],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00027700502,0.0007919267,0.0006924328,0.00022287876,0.0008634326,0.00011986259,0.00022854765,0.0006393564,0.000029097448],"category_scores_gemma":[0.00024456464,0.0007515626,0.00013710454,0.0002910478,0.000054418193,0.00046797117,0.00012592894,0.00093113637,0.000015464053],"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":[0.00030767953,0.00028204362,0.8979107,0.0026970897,0.004180002,0.00028345408,0.00067825697,0.006553413,0.01961056,0.007946488,0.000022254098,0.059528038],"study_design_scores_gemma":[0.0010431525,0.00017334997,0.980697,0.0008412692,0.00057567854,0.00002088852,0.00054696127,0.000972104,0.011198704,0.0011178034,0.0010423751,0.0017707429],"about_ca_topic_score_codex":0.000725762,"about_ca_topic_score_gemma":0.0069797197,"teacher_disagreement_score":0.08278625,"about_ca_system_score_codex":0.0005466456,"about_ca_system_score_gemma":0.000014937373,"threshold_uncertainty_score":0.99949354},"labels":[],"label_agreement":null},{"id":"W7024536659","doi":"","title":"Significant technical changes in the 2005 NBC, NFC and NPC","year":2005,"lang":"en","type":"article","venue":"NPARC","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Identification (biology); Set (abstract data type); Key (lock); Smart card","score_opus":0.011763972173706902,"score_gpt":0.2083884210931232,"score_spread":0.1966244489194163,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7024536659","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":[0.58045286,0.0012930273,0.0006367757,0.006158142,0.00024441755,0.00024060007,0.0000105878335,0.00044164585,0.41052192],"genre_scores_gemma":[0.9978913,0.00017827217,0.0014267598,0.00015677493,0.00020622529,0.000015758882,0.0000015931089,0.000010995233,0.00011231235],"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995723,0.000012506493,0.00007852381,0.00007740615,0.000074096315,0.00018518727],"domain_scores_gemma":[0.9997998,0.000059457343,0.000007020411,0.000100946985,0.000003176809,0.000029620895],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001720234,0.00007070311,0.00007124241,0.000037497823,0.000024566372,0.000018253551,0.00009161823,0.00004580933,0.000090180736],"category_scores_gemma":[0.000011602028,0.00005083353,0.000012035471,0.00007135602,0.00002399156,0.000034400004,0.000014569916,0.0001374042,0.000011274712],"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":[0.0000219154,0.000113769616,0.002552046,0.0001140483,0.000033923574,0.0000717931,0.003919252,0.016761778,0.5055647,0.031867087,0.03767122,0.40130848],"study_design_scores_gemma":[0.00070411834,0.00008002344,0.007323895,0.00012977036,0.00001939467,0.000087248016,0.00027130192,0.047909033,0.026470173,0.0017360456,0.9147434,0.0005255751],"about_ca_topic_score_codex":0.0000034817563,"about_ca_topic_score_gemma":0.0003054293,"teacher_disagreement_score":0.8770722,"about_ca_system_score_codex":0.000014901799,"about_ca_system_score_gemma":0.0000030413892,"threshold_uncertainty_score":0.20729312},"labels":[],"label_agreement":null},{"id":"W7027102566","doi":"","title":"Canada Renewable Energy Power Market Forecast to Grow By 2026 as Per OG Analysis Latest Report","year":2019,"lang":"en","type":"other","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Renewable energy; Energy market; Power (physics); Market research; Market penetration; Energy (signal processing)","score_opus":0.0030386323310649284,"score_gpt":0.1713086668488296,"score_spread":0.16827003451776465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7027102566","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000044187324,0.0007573988,0.0016663782,0.00010662317,0.0012955609,0.000094983305,0.00023488689,0.00037354336,0.9954264],"genre_scores_gemma":[0.008431226,0.000078681194,0.000555116,0.00048610414,0.00022201265,0.000024176528,0.0005747684,0.00066452153,0.98896337],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99786824,0.000021212638,0.00044267642,0.00058257673,0.00046065557,0.00062461774],"domain_scores_gemma":[0.99863505,0.00005213529,0.00011866614,0.0008407243,0.000043856162,0.00030955754],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00013558363,0.0005586781,0.00072854967,0.00043377336,0.000040634615,0.00007331372,0.00035915026,0.0003681524,0.037488267],"category_scores_gemma":[0.000028190907,0.0005321763,0.00018027269,0.0006690058,0.000012640241,0.000051227547,0.000089337445,0.00017587592,0.00009690575],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000032571163,0.0000068620975,0.0003378516,0.00003296912,0.0010937502,0.00014760905,0.000008997633,0.021167872,0.000053061918,0.000039111175,0.9769701,0.00013856962],"study_design_scores_gemma":[0.00009052338,0.000020595293,0.000017869972,0.00007678476,0.00021133419,0.000043140393,0.000014675253,0.0023969351,0.00021584588,0.000003551035,0.996214,0.0006947957],"about_ca_topic_score_codex":0.9506008,"about_ca_topic_score_gemma":0.96601236,"teacher_disagreement_score":0.03739136,"about_ca_system_score_codex":0.00022601259,"about_ca_system_score_gemma":0.00023776593,"threshold_uncertainty_score":0.999713},"labels":[],"label_agreement":null},{"id":"W7039977765","doi":"","title":"Food (in)security","year":2016,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Energy Load and Power Forecasting","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":"Food security; Mythology; Bust; Food processing; Food supply","score_opus":0.004474058428900502,"score_gpt":0.16624421640485637,"score_spread":0.16177015797595587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039977765","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000069786714,0.0007271171,6.3199343e-7,0.000015417361,0.00047432276,0.00014099856,0.0001233921,0.00034218418,0.9981061],"genre_scores_gemma":[0.007094356,0.0003849512,0.00019597713,0.000033719538,0.00018817502,0.000009897721,0.00004678743,0.00014988634,0.9918963],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99896663,0.00001947166,0.00043238865,0.00010900327,0.0001950045,0.00027752077],"domain_scores_gemma":[0.9994851,0.000053747848,0.00013812889,0.00022416045,0.00002708319,0.000071827744],"candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000095624084,0.0002664503,0.00030778203,0.000094087714,0.00001561321,0.00001860154,0.00020456307,0.00033507068,0.3100298],"category_scores_gemma":[0.000026184516,0.00024640388,0.00009669308,0.0000050688445,0.000044613113,1.1864246e-7,0.000035433462,0.0002002687,0.008041765],"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":[0.000009556734,0.0000081891785,0.0000029534413,0.0004269735,0.000034141503,0.0000069853027,0.000094358766,0.0007125849,1.6101473e-7,0.00006386012,0.9968699,0.0017703184],"study_design_scores_gemma":[0.00033259118,0.000054654924,0.0000022527956,0.0005276369,0.000010402176,0.000016589029,0.000012186671,0.000057475256,0.000013190388,0.000022402874,0.9986687,0.00028196062],"about_ca_topic_score_codex":0.0005328543,"about_ca_topic_score_gemma":0.0022896398,"teacher_disagreement_score":0.30198804,"about_ca_system_score_codex":0.00004142701,"about_ca_system_score_gemma":0.000008643772,"threshold_uncertainty_score":0.9999988},"labels":[],"label_agreement":null},{"id":"W7044811525","doi":"","title":"An adjustment method for electricity sales forecasting result considering the effects of spring festival","year":2019,"lang":"en","type":"article","venue":"DOAJ (DOAJ: Directory of Open Access Journals)","topic":"Energy Load and Power Forecasting","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":"Electricity; Quarter (Canadian coin); Spring (device); Electricity generation; Electricity price; Mains electricity","score_opus":0.13910072399913767,"score_gpt":0.472096123294584,"score_spread":0.3329953992954463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7044811525","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":[0.96425825,0.011369365,0.0208752,0.000013939638,0.0010618458,0.00084550394,0.00001675243,0.00009802382,0.001461124],"genre_scores_gemma":[0.98857677,0.0011451459,0.009772725,0.000053699303,0.0002482127,0.0000574612,0.0000057531643,0.000094104376,0.000046125046],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99765795,0.0002180851,0.0008395398,0.00034589673,0.00041191484,0.00052659365],"domain_scores_gemma":[0.99587095,0.0027866166,0.00052274135,0.00046424966,0.00019365337,0.00016182],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0016931851,0.000351217,0.00076312106,0.00040069324,0.00018887836,0.0003416472,0.0014262184,0.00010030863,0.00014335118],"category_scores_gemma":[0.0006695776,0.00028290975,0.00021474667,0.0005878432,0.000048962687,0.0011107143,0.00029018527,0.0004042291,0.0000018764389],"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":[0.00015712353,0.000087873865,0.0719831,0.0011985716,0.00040821102,0.000011835523,0.00043904845,0.105344824,0.7590862,0.00035593077,0.0009914646,0.059935823],"study_design_scores_gemma":[0.0013145098,0.00008373199,0.10419719,0.0021255729,0.00020625576,0.00003557826,0.00011202324,0.124215096,0.7625401,0.0019181216,0.0025530742,0.00069872156],"about_ca_topic_score_codex":0.00039652584,"about_ca_topic_score_gemma":0.000055691406,"teacher_disagreement_score":0.0592371,"about_ca_system_score_codex":0.00009723243,"about_ca_system_score_gemma":0.00006454494,"threshold_uncertainty_score":0.99996233},"labels":[],"label_agreement":null},{"id":"W7072065711","doi":"","title":"The use of piecewise linear models to predict hydroelectric load for Manitoba Hydro","year":2002,"lang":"en","type":"dissertation","venue":"Mspace (University of Manitoba)","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"Natural Resources Canada","funders":"Division of Mathematical Sciences; Manitoba Hydro; University of Manitoba","keywords":"Hydroelectricity; Piecewise linear function; Nonlinear system; Linear model; Set (abstract data type); Interval (graph theory); Piecewise; Noise (video); Prediction interval; Estimation theory","score_opus":0.028489253726630713,"score_gpt":0.18952985144881984,"score_spread":0.16104059772218912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7072065711","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":[0.97525924,0.0012749381,0.012470859,0.00008928948,0.0011313336,0.00092988583,0.00022900225,0.0002910063,0.008324469],"genre_scores_gemma":[0.9795504,0.0020718242,0.0069732177,0.000019272304,0.00031382864,0.000007879809,0.0004260575,0.00020332633,0.010434167],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987403,0.000026231268,0.00020581181,0.00026879754,0.00037431848,0.00038454361],"domain_scores_gemma":[0.9988582,0.00021834578,0.00018158318,0.00038962477,0.0002398985,0.000112350586],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00012331226,0.00030158125,0.00040928862,0.00025391846,0.00026323932,0.000025887291,0.0004914286,0.00028054198,0.0000036965994],"category_scores_gemma":[0.00003906919,0.0003397898,0.0002462384,0.00037777345,0.00003983298,0.00026032716,0.000041759336,0.00026571457,0.000012579051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049441436,0.00010819796,0.0003229643,0.0012454209,0.000678574,0.000025260564,0.0025431807,0.924757,0.0020177797,0.0006676003,0.041463807,0.025675789],"study_design_scores_gemma":[0.0007410781,0.00029908027,0.00072660757,0.00053446763,0.0003504507,0.0000034595625,0.004583245,0.930277,0.0018433179,0.00016275546,0.059873465,0.0006050612],"about_ca_topic_score_codex":0.0016711424,"about_ca_topic_score_gemma":0.23949517,"teacher_disagreement_score":0.23782402,"about_ca_system_score_codex":0.00019555482,"about_ca_system_score_gemma":0.000049756916,"threshold_uncertainty_score":0.9999054},"labels":[],"label_agreement":null},{"id":"W7073638978","doi":"","title":"Wind power forecasting using artificial neural networks with numerical prediction : a case study for mountainous Canada","year":2016,"lang":"en","type":"other","venue":"cIRcle (University of British Columbia)","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"BC Hydro","keywords":"Artificial neural network; Wind power; Wind power forecasting; Terrain; Numerical weather prediction; Wind speed; Turbine; Electricity generation; Predictive modelling; Weather forecasting","score_opus":0.010944022999574051,"score_gpt":0.16983155872844255,"score_spread":0.1588875357288685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7073638978","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":[0.96724355,0.00015681649,0.024270605,0.0000026022446,0.00089635554,0.0007418994,0.0007824853,0.00027668386,0.005629027],"genre_scores_gemma":[0.9960601,0.0000036277945,0.00040861793,0.0000047774843,0.000271798,0.0000011850283,0.000028135006,0.00023656744,0.0029852286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886316,0.00002792856,0.00015921013,0.0003491594,0.0002060896,0.0003944281],"domain_scores_gemma":[0.99941766,0.00005015986,0.00014235034,0.00018965865,0.000072644456,0.0001275198],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007470798,0.00012101894,0.0003850047,0.00006232603,0.00022042778,0.000063412,0.00014802332,0.00017940463,0.00012726727],"category_scores_gemma":[0.00000694105,0.00031912306,0.00007544558,0.00015666289,0.0000667998,0.00010432325,0.000043808235,0.00018447526,3.2927568e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009482792,0.0002959854,0.01350428,0.0005423187,0.0013413401,0.028117454,0.00067043654,0.4001893,0.0000142683,8.3098e-7,0.056985606,0.49824336],"study_design_scores_gemma":[0.0024987587,0.00057475915,0.010583735,0.0012798086,0.0004933885,0.0068551917,0.005272381,0.96697605,1.3469881e-7,0.0000050753933,0.0042935964,0.00116711],"about_ca_topic_score_codex":0.8914076,"about_ca_topic_score_gemma":0.9915214,"teacher_disagreement_score":0.56678677,"about_ca_system_score_codex":0.0002343538,"about_ca_system_score_gemma":0.00012722203,"threshold_uncertainty_score":0.9999261},"labels":[],"label_agreement":null},{"id":"W7084588259","doi":"10.1080/09515089.2025.2566960","title":"Moral rationalization as corruption in the corporate world","year":2025,"lang":"en","type":"article","venue":"Philosophical Psychology","topic":"Energy Load and Power Forecasting","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":"York University","funders":"","keywords":"Rationalization (economics); Language change; Motivated reasoning; Corporate governance; Moral disengagement","score_opus":0.06877804507068908,"score_gpt":0.31991612510214446,"score_spread":0.2511380800314554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7084588259","genre_codex":"other","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":[0.1725898,0.0006198083,0.01492812,0.048485637,0.0024416153,0.00029639955,0.000005854666,0.0003182124,0.7603146],"genre_scores_gemma":[0.99615806,0.000024535808,0.00008735962,0.0033546127,0.0001913876,0.00002587387,0.00003486055,0.000008404768,0.000114880866],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99938875,0.0000659641,0.00020267874,0.0001321969,0.00007631916,0.00013406944],"domain_scores_gemma":[0.999715,0.000076397366,0.000027074142,0.00014574114,0.00001801855,0.00001779923],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017050676,0.000086650754,0.00009383869,0.00016582668,0.000037957398,0.000011688397,0.0001355277,0.00007788465,0.00007386847],"category_scores_gemma":[0.000030218205,0.00007144199,0.000026492984,0.0005730825,0.000047461195,0.000054514803,0.0000094703955,0.00022546489,0.000056915454],"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":[0.000026100675,0.00004086952,0.0063738413,0.000010190126,0.0000100628495,0.000011900116,0.00007188828,0.01332092,0.00037272903,0.9756712,0.0021525803,0.001937749],"study_design_scores_gemma":[0.00037593083,0.000022630491,0.018469036,0.00002685854,0.0000062047457,0.000010920014,0.000003442053,0.013438404,0.000053777858,0.96181357,0.005686558,0.000092697876],"about_ca_topic_score_codex":0.0000042226757,"about_ca_topic_score_gemma":0.000043342767,"teacher_disagreement_score":0.8235683,"about_ca_system_score_codex":0.00001890388,"about_ca_system_score_gemma":0.000007564501,"threshold_uncertainty_score":0.29133198},"labels":[],"label_agreement":null},{"id":"W7090328029","doi":"10.1109/access.2025.3620719","title":"Electric Vehicle Load Forecasting in Rural Areas: A Systematic Review","year":2025,"lang":"en","type":"article","venue":"IEEE Access","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":2,"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; Canada First Research Excellence Fund","keywords":"Standardization; Documentation; Work (physics); Resilience (materials science); Psychological resilience; Rural area","score_opus":0.016857412740002506,"score_gpt":0.25812765191746995,"score_spread":0.24127023917746743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7090328029","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":[0.8113132,0.13790382,0.0024137294,0.00012104904,0.0016015717,0.0012914246,0.0000039149454,0.0007354533,0.044615813],"genre_scores_gemma":[0.9980924,0.001232833,0.00002437096,0.0002809501,0.0000476248,0.00012984134,0.0000020642917,0.00002164935,0.0001682537],"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","domain_scores_codex":[0.99888587,0.00004091324,0.00046351363,0.00013263685,0.00014787674,0.00032915984],"domain_scores_gemma":[0.9994938,0.00014067287,0.00005320592,0.00022816265,0.00004454662,0.00003965543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031173142,0.00016679721,0.00042509864,0.00016108446,0.000042508822,0.00007978137,0.0003700686,0.00005873898,0.000012501182],"category_scores_gemma":[0.0001900916,0.00015297625,0.00006869883,0.0010907303,0.0000067994933,0.00027977367,0.00003309546,0.00019165863,0.000014514379],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000020899484,0.00007627957,0.0078694625,0.9273602,0.00025956193,0.0001384977,0.0003630282,0.037532035,0.0029775095,0.0006165371,0.005394733,0.017391259],"study_design_scores_gemma":[0.00079425867,0.00003329973,0.0009722422,0.496434,0.00025905596,0.000045740824,0.000046251804,0.49105772,0.00871648,0.0006998097,0.0001939118,0.00074722245],"about_ca_topic_score_codex":0.000097753255,"about_ca_topic_score_gemma":0.00015572467,"teacher_disagreement_score":0.4535257,"about_ca_system_score_codex":0.00016444337,"about_ca_system_score_gemma":0.00003793793,"threshold_uncertainty_score":0.62381905},"labels":[],"label_agreement":null},{"id":"W7095706233","doi":"","title":"l w","year":2015,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Explanatory power; Competition (biology); Electricity market; Support vector machine; Electricity; Mains electricity; Variables; Kernel (algebra)","score_opus":0.02550397927771767,"score_gpt":0.204341902822302,"score_spread":0.17883792354458433,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095706233","genre_codex":"other","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":[0.0704348,0.00008608315,0.0043841447,0.000009086763,0.00027470983,0.0000040998257,1.0698577e-7,0.00032591965,0.92448103],"genre_scores_gemma":[0.99709386,0.0000011900298,0.0014009057,0.000021534988,0.000051347055,4.8714736e-7,5.444976e-7,0.0000043405485,0.0014257652],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.99989754,6.9033814e-7,0.000021212561,0.000014831237,0.000021861431,0.000043858912],"domain_scores_gemma":[0.99993,0.0000023809775,7.2814436e-7,0.000028828119,0.0000037695804,0.000034239623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000018380164,0.0000178148,0.00001621685,0.0000074857276,0.0000028083384,0.000003955934,0.000017650413,0.000008326079,0.00003929155],"category_scores_gemma":[0.000002867222,0.0000150250735,0.000004722144,0.000023868253,0.0000017005054,0.00002347837,0.0000028146915,0.000014374013,0.00010944099],"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":[0.0000028916452,0.000012415579,0.004768476,0.000022608781,0.000035882982,0.000021148024,0.001322644,0.42192772,0.0026788209,0.06957698,0.38977483,0.10985556],"study_design_scores_gemma":[0.00022778574,0.000016169026,0.00015758724,0.0000066725333,0.000002032109,0.000011616087,0.00009192583,0.14792575,0.014550696,0.0014462529,0.8353983,0.00016519129],"about_ca_topic_score_codex":0.000002955057,"about_ca_topic_score_gemma":0.0000033253473,"teacher_disagreement_score":0.9266591,"about_ca_system_score_codex":0.000004438351,"about_ca_system_score_gemma":0.0000015156933,"threshold_uncertainty_score":0.1406679},"labels":[],"label_agreement":null},{"id":"W7097541618","doi":"","title":"Layered Timeseries Analysis for Smart Grid Agents","year":2014,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Smart grid; Autoregressive integrated moving average; Electricity; Big data; Grid; Electricity market; Microgrid; Renewable energy; Metering mode; Time series","score_opus":0.01578979268624233,"score_gpt":0.2155838134752703,"score_spread":0.19979402078902797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7097541618","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":[0.3279514,0.0000769307,0.45248002,0.000076953496,0.0010302474,0.00011221362,0.000026171801,0.0008405077,0.21740556],"genre_scores_gemma":[0.99306923,0.000004375795,0.0042852988,0.000056075,0.00013897622,0.000013219921,0.0000468739,0.000015955937,0.0023699796],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995937,0.0000056645026,0.0001062804,0.00008536302,0.000051259532,0.00015776174],"domain_scores_gemma":[0.99976414,0.00004179683,0.000009815668,0.00012420137,0.000015891577,0.00004413798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000084102896,0.0000789875,0.00012781938,0.00007624074,0.00004010568,0.000023865849,0.00006968172,0.00003385923,0.00028003872],"category_scores_gemma":[0.000020036965,0.00006999008,0.00009894348,0.0001864881,0.0000068939166,0.00005878265,0.000010601937,0.000028149592,0.000027578802],"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":[0.00001664416,0.000024672861,0.038643476,0.00012302405,0.0018063831,9.4148635e-7,0.00054402434,0.88746786,0.0018444124,0.00916951,0.046360113,0.013998961],"study_design_scores_gemma":[0.00021849493,0.000026911917,0.005291901,0.0000061029064,0.0001251355,5.530566e-7,0.000025848021,0.77137107,0.0065842355,0.0002056486,0.21594107,0.00020303756],"about_ca_topic_score_codex":0.000024399207,"about_ca_topic_score_gemma":0.000099518395,"teacher_disagreement_score":0.66511786,"about_ca_system_score_codex":0.000007769728,"about_ca_system_score_gemma":0.0000016953813,"threshold_uncertainty_score":0.30662274},"labels":[],"label_agreement":null},{"id":"W7098156827","doi":"","title":"Terms of Reference SE-91 Renewable Integration","year":2012,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Renewable energy; Variable (mathematics); Variable renewable energy; Renewable resource; Production (economics); Electricity generation","score_opus":0.03029166609256172,"score_gpt":0.23146043360738652,"score_spread":0.2011687675148248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7098156827","genre_codex":"other","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":[0.25214854,0.00011041808,0.016060758,0.0000055073665,0.00031227182,0.000020752217,0.0000017840484,0.00015993937,0.73118],"genre_scores_gemma":[0.9953533,0.000016777585,0.002706939,0.000008755183,0.00006175384,0.0000019288404,0.000008115568,0.000007714023,0.0018347127],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999708,0.0000038933167,0.00009583138,0.000032186665,0.000044952754,0.00011514048],"domain_scores_gemma":[0.99985266,0.000014068326,0.00001133152,0.00008206507,0.0000085379315,0.000031352796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000059483384,0.00004934419,0.000058823032,0.000026422013,0.000010602885,0.000004561646,0.00004084772,0.00003378678,0.00017996262],"category_scores_gemma":[0.000007960999,0.000039099774,0.000013619955,0.000059743106,0.0000061535693,0.00014310215,0.0000076680635,0.00003955638,0.000016346306],"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":[0.000013310322,0.00008905304,0.029827785,0.00024039131,0.000077834266,0.0000010975576,0.004005143,0.08154046,0.6867012,0.06036102,0.017522424,0.11962032],"study_design_scores_gemma":[0.00015023477,0.000030808304,0.00273061,0.00009815209,0.000011014628,0.0000059584722,0.00012736203,0.030886507,0.9238479,0.0004605145,0.04142247,0.00022846927],"about_ca_topic_score_codex":0.00010413779,"about_ca_topic_score_gemma":0.00006481678,"teacher_disagreement_score":0.7432048,"about_ca_system_score_codex":0.000009509666,"about_ca_system_score_gemma":0.0000015321215,"threshold_uncertainty_score":0.19704644},"labels":[],"label_agreement":null},{"id":"W7100068919","doi":"","title":"ACCEPTED TO IEEE TRANSACTIONS ON POWER SYSTEMS 1 Application of Public-Domain Market Information to Forecast Ontario’s Wholesale Electricity Prices","year":2008,"lang":"en","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Univariate; Autoregressive integrated moving average; Volatility (finance); Electricity market; Electricity price forecasting; Electricity; Multivariate statistics; Time series","score_opus":0.012365503192549443,"score_gpt":0.19014762109593858,"score_spread":0.17778211790338913,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7100068919","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":[0.27647406,0.000005259359,0.66406065,0.000026867065,0.0001890577,0.000314627,0.00001377886,0.0001745262,0.058741175],"genre_scores_gemma":[0.9966403,0.000003482824,0.0026822677,0.00007664911,0.000014247597,0.00012152888,0.000014620453,0.000017627575,0.000429279],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989813,0.000015601048,0.0003795563,0.00012292173,0.00023180679,0.000268825],"domain_scores_gemma":[0.9994415,0.000044221757,0.000054660275,0.00021071777,0.00009566508,0.00015324046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018042768,0.00015598911,0.00018014421,0.00033970867,0.00009514527,0.000041332893,0.00015454591,0.00008801115,0.0001631697],"category_scores_gemma":[0.0000062261993,0.00015186868,0.000053064417,0.0006092319,0.000009179709,0.0005074361,0.0000032784822,0.00012868346,0.000060916194],"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":[0.00015591522,0.00022178903,0.0018240676,0.00022487981,0.0001919583,0.0000021596736,0.0128357485,0.89126235,0.010189815,0.0015055987,0.016256897,0.06532883],"study_design_scores_gemma":[0.0021181959,0.0013025955,0.032637436,0.0002794346,0.000055638582,0.00013852103,0.0015076412,0.32903677,0.09568847,0.000050817624,0.53501904,0.0021654295],"about_ca_topic_score_codex":0.001800718,"about_ca_topic_score_gemma":0.0038860484,"teacher_disagreement_score":0.7201662,"about_ca_system_score_codex":0.00023873275,"about_ca_system_score_gemma":0.000037737474,"threshold_uncertainty_score":0.6193025},"labels":[],"label_agreement":null},{"id":"W7104267222","doi":"10.23977/acss.2025.090317","title":"Power Load Forecasting Method Combining Informer Model and ACO Optimization Algorithm","year":2025,"lang":"","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"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":"Hyperparameter; Ant colony optimization algorithms; Scheduling (production processes); Stability (learning theory); Process (computing); Hyperparameter optimization; Electric power system; Power (physics)","score_opus":0.01499022927429781,"score_gpt":0.26072802269442263,"score_spread":0.24573779342012483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7104267222","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":[0.002741088,0.065370105,0.92268753,0.000019155434,0.0024373475,0.00033771055,0.000015635369,0.00008445603,0.0063069626],"genre_scores_gemma":[0.6168118,0.0045952727,0.37799236,0.00012220319,0.00020923474,0.00004366686,0.000011081829,0.000054217715,0.00016014393],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99729836,0.00013043176,0.00110933,0.0005808062,0.00027395776,0.0006070934],"domain_scores_gemma":[0.99864286,0.0006100026,0.00022903652,0.00022882067,0.00015373355,0.00013551653],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010637937,0.0005048115,0.0008001622,0.0003299006,0.00024356239,0.00042234876,0.00019062798,0.00025781945,0.000007049348],"category_scores_gemma":[0.000031765205,0.0005189991,0.00007029311,0.00049571175,0.00006767771,0.0011796458,0.00024013998,0.0003939046,8.349292e-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":[0.000009746854,0.000015030401,0.00030737184,0.0005475931,0.000061831226,0.000008869671,0.0012218474,0.7593809,0.000023849685,0.0013641289,0.000020253961,0.23703858],"study_design_scores_gemma":[0.0010408901,0.000104562845,0.000011358383,0.0035364719,0.00003180035,0.000046572626,0.00021604598,0.99235696,0.000044261913,0.000553651,0.0015613281,0.000496101],"about_ca_topic_score_codex":0.000045216253,"about_ca_topic_score_gemma":0.000007397332,"teacher_disagreement_score":0.6140707,"about_ca_system_score_codex":0.0001095145,"about_ca_system_score_gemma":0.00007518274,"threshold_uncertainty_score":0.9997262},"labels":[],"label_agreement":null},{"id":"W7106329570","doi":"10.1016/j.epsr.2025.112534","title":"Hybrid model for short-term wind power prediction by the SAO and a parallel architecture of LSTM and GRU","year":2025,"lang":"en","type":"article","venue":"Electric Power Systems Research","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Major Science and Technology Projects in Yunnan Province; Yunnan Provincial Department of Education","keywords":"Adaptability; Key (lock); Mean squared error; Grid; Power (physics); Wind power; Electric power system; Feature (linguistics)","score_opus":0.01873480700136997,"score_gpt":0.2705821990131071,"score_spread":0.2518473920117371,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7106329570","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":[0.79326284,0.034368575,0.16170228,0.00015328247,0.00048278345,0.0014021238,0.00013705163,0.00014529732,0.008345758],"genre_scores_gemma":[0.9975229,0.00027507966,0.00007133066,0.000007956355,0.000033847304,0.00010045097,0.0000115370685,0.00002962871,0.0019472633],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984849,0.00009659502,0.0003029681,0.0002679349,0.000332119,0.0005154571],"domain_scores_gemma":[0.99918246,0.000337837,0.000023237622,0.00025903343,0.00011498997,0.00008242302],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009397717,0.0001656733,0.00024507428,0.0003018792,0.00019614572,0.00008998911,0.00019653664,0.00010365562,0.0000024259384],"category_scores_gemma":[0.0000852398,0.00012455514,0.00004471415,0.00037612868,0.00007386646,0.00007322381,0.00005986949,0.00043656054,4.0052007e-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":[0.0011189838,0.00034756423,0.012438502,0.0057608495,0.0022242311,0.000033188182,0.0107534705,0.32556123,0.30198884,0.015885463,0.27601698,0.04787071],"study_design_scores_gemma":[0.0005446396,0.0002281513,0.00059564254,0.00025292076,0.000020901458,0.00005464469,0.00007852248,0.98569703,0.0024956022,0.0005812385,0.009269095,0.00018158973],"about_ca_topic_score_codex":0.00004288107,"about_ca_topic_score_gemma":0.000007641478,"teacher_disagreement_score":0.6601358,"about_ca_system_score_codex":0.00006040581,"about_ca_system_score_gemma":0.000059503713,"threshold_uncertainty_score":0.5079211},"labels":[],"label_agreement":null},{"id":"W7115564490","doi":"10.1016/j.suscom.2025.101284","title":"Explainable and counterfactual lasso regression for resilient micro gas turbine power prediction in smart grids","year":2025,"lang":"en","type":"article","venue":"Sustainable Computing Informatics and Systems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Lasso (programming language); Microgrid; Smart grid; Counterfactual thinking; Turbine; Power (physics); Electric power system; Grid; Energy (signal processing)","score_opus":0.005467601936135394,"score_gpt":0.20940605431126838,"score_spread":0.20393845237513297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115564490","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":[0.97239065,0.0017495657,0.014006401,0.00001760081,0.000763166,0.0004977924,0.000010674804,0.00013423791,0.0104299085],"genre_scores_gemma":[0.99740845,0.00005462862,0.0004987596,0.000019485537,0.000041694642,0.000020619484,0.000021944034,0.000015010931,0.0019194178],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987644,0.000019279109,0.00056888873,0.00011169889,0.000102067104,0.00043369652],"domain_scores_gemma":[0.99946696,0.00014545856,0.00007942544,0.00013414421,0.00011727976,0.000056761466],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006933098,0.00018107434,0.00026707494,0.00025478765,0.00023323798,0.00021155314,0.00007415496,0.00011306802,0.0000010534746],"category_scores_gemma":[0.000059097027,0.00016105454,0.000024216468,0.00019992371,0.000026676347,0.00023289342,0.00009151731,0.00014915396,4.3949825e-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":[0.00017970167,0.00007437361,0.03971464,0.02702758,0.00018417771,0.000031885564,0.03415691,0.8469595,0.0004493151,0.026082529,0.017649712,0.0074897115],"study_design_scores_gemma":[0.0009264654,0.00008313552,0.00056486204,0.0011297453,0.000010070088,0.000017767103,0.016196337,0.9309871,0.00022132613,0.00007715178,0.049614403,0.00017162049],"about_ca_topic_score_codex":0.0000911282,"about_ca_topic_score_gemma":0.0000055354035,"teacher_disagreement_score":0.084027655,"about_ca_system_score_codex":0.00015665477,"about_ca_system_score_gemma":0.000037126636,"threshold_uncertainty_score":0.65676135},"labels":[],"label_agreement":null},{"id":"W7115927221","doi":"10.34658/9788367934886.w8.4.719-743","title":"PROGNOZOWANIE SZEREGÓW CZASOWYCH ZA POMOCĄ GŁĘBOKO UCZONYCH SZTUCZNYCH SIECI NEURONOWYCH NA PRZYKŁADZIE RYNKU ENERGII ELEKTRYCZNEJ","year":2025,"lang":"","type":"article","venue":"Wydawnictwo Politechniki Łódzkiej","topic":"Energy Load and Power Forecasting","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":"Energy (signal processing); Transmission network; New energy","score_opus":0.008932960990674262,"score_gpt":0.23671771578175485,"score_spread":0.2277847547910806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7115927221","genre_codex":"other","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":[0.15133747,0.04253072,0.021722486,0.005126743,0.023921119,0.005767674,0.0012607763,0.010127082,0.7382059],"genre_scores_gemma":[0.9485556,0.002674053,0.0057303486,0.0018063788,0.0028597445,0.0007956634,0.00046931882,0.0009928497,0.03611607],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","domain_scores_codex":[0.9824742,0.0006431348,0.004509911,0.0039373455,0.0019813844,0.0064540245],"domain_scores_gemma":[0.9906423,0.0010019119,0.0009976651,0.004913546,0.00081238564,0.0016321806],"candidate_categories":["metaepi_narrow","sts","scholarly_communication","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","research_integrity","insufficient_payload"],"category_scores_codex":[0.0017687144,0.0038492163,0.0033563902,0.0028269887,0.0016754074,0.0014534945,0.004178166,0.0029006165,0.0015935078],"category_scores_gemma":[0.0007100057,0.004415839,0.0018510771,0.006625431,0.0009164121,0.0013866986,0.0019567236,0.0046459856,0.00089038425],"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":[0.0009559888,0.0040214583,0.015246015,0.0070510614,0.0047883946,0.0013498721,0.002940095,0.018989373,0.05217973,0.5736228,0.08223083,0.23662433],"study_design_scores_gemma":[0.00546095,0.0011443069,0.003738123,0.0047904723,0.0014490496,0.0005055801,0.0005654146,0.028418867,0.046250004,0.0066221994,0.8945777,0.006477352],"about_ca_topic_score_codex":0.0010832611,"about_ca_topic_score_gemma":0.0002980896,"teacher_disagreement_score":0.8123469,"about_ca_system_score_codex":0.0016714414,"about_ca_system_score_gemma":0.0013971874,"threshold_uncertainty_score":0.9998875},"labels":[],"label_agreement":null},{"id":"W7116968577","doi":"10.18280/mmep.121101","title":"Enhancing LSTM-Based Models for Electricity Consumption Forecasting with Bayesian Hyperparameter Tuning","year":2025,"lang":"","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Hyperparameter; Consumption (sociology); Electricity; Energy consumption; Bayesian inference","score_opus":0.031610714008352754,"score_gpt":0.21354803232776373,"score_spread":0.18193731831941098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7116968577","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":[0.046755962,0.002063133,0.9487881,0.00004944003,0.00023724877,0.0008198231,0.000012986539,0.00052737846,0.0007459238],"genre_scores_gemma":[0.7012989,0.00008920806,0.29804406,0.00002660189,0.00007466988,0.00020278327,0.000013630787,0.0001374764,0.000112665824],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99662983,0.000030165162,0.0010597481,0.0007503851,0.00029023254,0.0012396079],"domain_scores_gemma":[0.9977396,0.0013422607,0.00013699233,0.00037979966,0.0001340047,0.00026730576],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008332115,0.000813901,0.0009426291,0.00049560796,0.00038254622,0.0003606396,0.00021970666,0.0004069888,0.0000098547225],"category_scores_gemma":[0.0001106206,0.0007893686,0.00019086568,0.0004910786,0.00007408426,0.0003354501,0.000056099605,0.00071493664,0.0000022207014],"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":[0.000050480874,0.00006266231,0.000019486642,0.011038845,0.00021621352,0.0000037078607,0.000647203,0.96806747,0.0013388501,0.016657712,0.0000036444062,0.0018937167],"study_design_scores_gemma":[0.0010829724,0.00017080603,5.633272e-7,0.009903116,0.0003283149,0.000024757532,0.00002923861,0.97129965,0.0037873313,0.012452464,0.00009222884,0.0008285664],"about_ca_topic_score_codex":0.000015689562,"about_ca_topic_score_gemma":0.000004448524,"teacher_disagreement_score":0.654543,"about_ca_system_score_codex":0.00016361111,"about_ca_system_score_gemma":0.00007665824,"threshold_uncertainty_score":0.99945575},"labels":[],"label_agreement":null},{"id":"W7122603626","doi":"10.32996/jcsts.2026.5.1.7","title":"AI-Enhanced Sustainable Energy Management and Policy Recommendations for the U.S. Power Sector","year":2025,"lang":"","type":"article","venue":"Frontiers in Computer Science and Artificial Intelligence","topic":"Energy Load and Power Forecasting","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":"St. Francis Xavier University","funders":"","keywords":"Sustainability; Demand forecasting; Context (archaeology); Renewable energy; Energy management; Smart grid; Electricity; Demand management; Energy policy; Electricity generation","score_opus":0.01641132009360267,"score_gpt":0.26645217665122645,"score_spread":0.25004085655762376,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7122603626","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":[0.0010023225,0.0024135334,0.98528206,0.0056865513,0.004236228,0.00036803688,0.00000589436,0.000038694747,0.0009667018],"genre_scores_gemma":[0.9531923,0.0025157335,0.04144432,0.0016651696,0.00027936316,0.000094399555,0.00000207813,0.00001803672,0.0007886097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978302,0.00003517092,0.0004987649,0.00062268664,0.00021021704,0.00080297707],"domain_scores_gemma":[0.9990729,0.00021683081,0.00006238562,0.00032779507,0.00020566281,0.00011441094],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001098482,0.00026271743,0.00025054542,0.0008285052,0.0012096387,0.0007572052,0.00058433507,0.00008497033,0.000007368144],"category_scores_gemma":[0.000083081664,0.00023568269,0.0000477521,0.0023119824,0.0007323029,0.0005383288,0.00044250168,0.00023494007,6.194885e-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":[0.000020576092,0.00003167879,0.000051401268,0.00009379773,0.000037936974,0.000002770066,0.001546835,0.023985205,0.000047701247,0.2712768,0.0036123944,0.6992929],"study_design_scores_gemma":[0.00006747667,0.00007743405,0.00009811714,0.000247214,0.000023040486,0.0000015610254,0.002363757,0.8569633,0.0071067302,0.08505788,0.047707,0.00028649892],"about_ca_topic_score_codex":0.00020077325,"about_ca_topic_score_gemma":0.00006271221,"teacher_disagreement_score":0.95219,"about_ca_system_score_codex":0.00023053467,"about_ca_system_score_gemma":0.0002092746,"threshold_uncertainty_score":0.9610861},"labels":[],"label_agreement":null},{"id":"W7124139685","doi":"10.1109/icacrs67045.2025.11324130","title":"Robust Load Forecasting using Modified Bacterial Foraging Optimization Algorithm with Deep Learning for Smart Grid Environment","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Horizon College and Seminary","funders":"","keywords":"Hyperparameter; Smart grid; Hyperparameter optimization; Grid; Feature selection; Deep learning; Selection (genetic algorithm); Time series; Electric power system; Feature (linguistics)","score_opus":0.024098112916480386,"score_gpt":0.20134329338018636,"score_spread":0.17724518046370596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7124139685","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"codex-gemma-dda1882f352a","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010215642,0.00058662536,0.9768474,0.000037686532,0.0025601976,0.00087621494,0.000019588171,0.0003227672,0.008533862],"genre_scores_gemma":[0.24071957,0.00016542911,0.7560648,0.000043926688,0.001161932,0.000114613,0.00025481824,0.00024355327,0.001231378],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9961889,0.00009115103,0.0010433982,0.00093311566,0.00043087592,0.0013125755],"domain_scores_gemma":[0.9985995,0.0003436068,0.00030786035,0.0003753948,0.0001630257,0.00021060457],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006982689,0.0008507193,0.00074621086,0.00035838396,0.0010608529,0.00048395392,0.0002883847,0.0003679009,0.00035030473],"category_scores_gemma":[0.0001142891,0.0008854149,0.00023818786,0.000512806,0.00009438788,0.00069700694,0.00020736367,0.0005765593,0.0000031570025],"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":[0.00015445345,0.000048841277,0.00016409936,0.0003628166,0.00033871792,0.000010371808,0.00045750552,0.9103494,0.0005410982,0.00018110983,0.000018896659,0.087372646],"study_design_scores_gemma":[0.0027948362,0.0002085691,0.000010317365,0.001049308,0.0004181096,0.000033271554,0.00052908773,0.9893669,0.0023933158,0.000031636704,0.0022182676,0.0009463878],"about_ca_topic_score_codex":0.0001835787,"about_ca_topic_score_gemma":0.00004178413,"teacher_disagreement_score":0.23050392,"about_ca_system_score_codex":0.000812736,"about_ca_system_score_gemma":0.00018567535,"threshold_uncertainty_score":0.99935967},"labels":[],"label_agreement":null},{"id":"W7127376130","doi":"10.1109/ccece64018.2025.11364462","title":"Reliability-Based Artificial Neural Network for Improving Voltage Stability in Smart Grids under Environmental Variability","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Smart grid; Robustness (evolution); Renewable energy; Artificial neural network; Stability (learning theory); Grid; Voltage; Environmental pollution","score_opus":0.012406009306071792,"score_gpt":0.2133676193099398,"score_spread":0.200961610003868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127376130","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":[0.6799016,0.00018358842,0.31197184,0.00019200334,0.0037733687,0.0013482973,0.00016642916,0.00024283862,0.0022200157],"genre_scores_gemma":[0.99166465,0.000004513241,0.006930588,0.00032246154,0.00047409284,0.00020034371,0.00013600643,0.00008102624,0.00018633016],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9944484,0.00036013566,0.0018560123,0.0014167047,0.00032110143,0.0015976147],"domain_scores_gemma":[0.9961694,0.0022317278,0.00014586962,0.0011750505,0.000046447978,0.00023149037],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0037855613,0.00078942365,0.0008716899,0.00018228176,0.00040614448,0.00017630243,0.0004416677,0.0006020986,0.000698941],"category_scores_gemma":[0.00044002823,0.00086522085,0.000490519,0.0007288568,0.000323009,0.00036951806,0.00023637549,0.0009667123,0.000008115161],"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":[0.00044650145,0.00058980385,0.08194604,0.0011655155,0.000052275416,0.0000031040483,0.00014455999,0.8776175,0.012547734,0.00624761,0.00008784244,0.019151527],"study_design_scores_gemma":[0.0012473054,0.00016746389,0.02545777,0.00011601464,0.000112066686,5.583346e-7,0.00020457845,0.9531245,0.008372686,0.009453271,0.00096575584,0.0007780147],"about_ca_topic_score_codex":0.00054219883,"about_ca_topic_score_gemma":0.0011322218,"teacher_disagreement_score":0.31176302,"about_ca_system_score_codex":0.0013365081,"about_ca_system_score_gemma":0.00028576964,"threshold_uncertainty_score":0.9993799},"labels":[],"label_agreement":null},{"id":"W7127400585","doi":"10.1109/ropec68163.2025.11354054","title":"Comparative Analysis of Fine-Tuning Time Series Foundation Models in Short-Term Load Prediction","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Time series; Series (stratigraphy); Electricity; Moment (physics); Operator (biology)","score_opus":0.02252457575370977,"score_gpt":0.2586122052886554,"score_spread":0.23608762953494564,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127400585","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":[0.6248589,0.00047875242,0.20184375,0.000033235356,0.0004182454,0.00019740897,0.00004273741,0.0001330963,0.17199387],"genre_scores_gemma":[0.9956286,0.00009692586,0.001624329,0.000005813756,0.00003354816,0.000014507913,0.00020052333,0.000011496403,0.0023842724],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99818146,0.00006897285,0.0008906919,0.00032512826,0.00024008253,0.0002936771],"domain_scores_gemma":[0.9993159,0.0001328613,0.000064145424,0.00026647077,0.00017276837,0.000047834495],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00031790268,0.0002820028,0.0007624083,0.0010215237,0.000086164335,0.00006768826,0.00014286302,0.00016733873,0.0004505328],"category_scores_gemma":[0.000020765034,0.00031446936,0.00018668141,0.0027471732,0.000065890395,0.0008493295,0.00007106116,0.00022484465,0.000006597875],"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":[0.00005396027,0.000049359245,0.01012071,0.00010680728,0.0016074636,0.000001574832,0.0034514456,0.96934277,0.006743413,0.005491572,0.00004174005,0.0029891895],"study_design_scores_gemma":[0.00024513545,0.000048161142,0.009983928,0.00038300548,0.00087338715,4.6887578e-7,0.00027436827,0.981093,0.00654769,0.00025811887,0.00009002982,0.00020269223],"about_ca_topic_score_codex":0.00019297788,"about_ca_topic_score_gemma":0.0015569185,"teacher_disagreement_score":0.37076968,"about_ca_system_score_codex":0.0002881897,"about_ca_system_score_gemma":0.000101968886,"threshold_uncertainty_score":0.99993074},"labels":[],"label_agreement":null},{"id":"W7130371697","doi":"10.1109/iconstem65670.2025.11374605","title":"Experimental Design of Wind Speed Analytics Prediction Methodology Using Modified Deep Learning Principle","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"SAIT Polytechnic","funders":"","keywords":"Wind speed; Autoregressive integrated moving average; Hyperparameter; Residual; Deep learning; Convolutional neural network; Outlier; Mean absolute percentage error; Renewable energy; Wind power","score_opus":0.1149419298701821,"score_gpt":0.3286089479399881,"score_spread":0.21366701806980604,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7130371697","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":[0.22644654,0.0013978692,0.7581077,0.000005625298,0.0016000111,0.00020677029,0.0000029370576,0.000133806,0.012098713],"genre_scores_gemma":[0.8923322,0.000060806848,0.10599953,0.000016133741,0.00013534984,9.937254e-7,0.000009319001,0.00004861012,0.0013970888],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99731857,0.0005001342,0.00097393076,0.00042722942,0.0002264457,0.00055371],"domain_scores_gemma":[0.9986819,0.00060677796,0.00020290757,0.00029809444,0.00010318811,0.00010710405],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0010589792,0.0003913909,0.000657007,0.0004889642,0.00021441928,0.0000483917,0.00021456793,0.00038204883,0.0003303015],"category_scores_gemma":[0.00027549212,0.00044217688,0.00017773217,0.0007367057,0.00011144816,0.00020900251,0.0001659786,0.00049468345,0.0000029892772],"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":[0.00007791572,0.000057075405,0.0006396446,0.00011255351,0.0003361975,0.0000043112236,0.0008423994,0.7355278,0.2584816,0.002910659,0.000009143209,0.0010006671],"study_design_scores_gemma":[0.00060879317,0.00013714618,0.000091809605,0.000145099,0.00017605562,0.000008672472,0.00073818414,0.7148667,0.28279707,0.000085465916,0.0001378304,0.00020720894],"about_ca_topic_score_codex":0.000113122296,"about_ca_topic_score_gemma":0.0000025501479,"teacher_disagreement_score":0.6658856,"about_ca_system_score_codex":0.00024464418,"about_ca_system_score_gemma":0.00010259659,"threshold_uncertainty_score":0.999803},"labels":[],"label_agreement":null},{"id":"W7133556799","doi":"10.1109/icpee65973.2025.11411039","title":"A Hybrid AI-ML Framework for Predictive Maintenance and Load Forecasting in Smart Grid Infrastructures","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Smart grid; Predictive maintenance; Context (archaeology); Demand forecasting; Key (lock)","score_opus":0.008535378544377617,"score_gpt":0.22984211640376573,"score_spread":0.22130673785938812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7133556799","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":[0.14878163,0.0041556032,0.81129956,0.00055710244,0.0049624057,0.0010519492,0.0002592254,0.0002829487,0.028649544],"genre_scores_gemma":[0.95533323,0.00028122516,0.042283833,0.00063982134,0.0005030171,0.00014454998,0.000020418058,0.00007749177,0.0007164401],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99692684,0.000045848163,0.00089112896,0.0007667326,0.00022970804,0.0011397221],"domain_scores_gemma":[0.9980286,0.0011157183,0.00012511003,0.00036304834,0.0001888199,0.00017870324],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005001377,0.00062651304,0.0006969173,0.0003542051,0.00024581974,0.00021023821,0.00028934208,0.00035707437,0.000095145595],"category_scores_gemma":[0.0015250033,0.00063058065,0.0001630785,0.0005414145,0.00016246104,0.00036154749,0.00023844246,0.0010010208,0.0000018906022],"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":[0.0014162487,0.0001545435,0.067663856,0.0048700315,0.0010988405,0.00015254835,0.0058516394,0.4450511,0.0004739407,0.21646214,0.023591794,0.2332133],"study_design_scores_gemma":[0.001825274,0.00022928994,0.006797322,0.005299716,0.00010597336,0.000047914396,0.0004079609,0.83803993,0.0029644794,0.13105172,0.012458317,0.00077211496],"about_ca_topic_score_codex":0.0002557101,"about_ca_topic_score_gemma":0.00033002315,"teacher_disagreement_score":0.8065516,"about_ca_system_score_codex":0.0003573903,"about_ca_system_score_gemma":0.00022497542,"threshold_uncertainty_score":0.99961454},"labels":[],"label_agreement":null},{"id":"W7139017617","doi":"10.1109/globecom59602.2025.11431919","title":"Probabilistic Electrical Load Forecasting via Prior-Guided Meta Diffusion Models","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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 Winnipeg; University of Manitoba; Hydro-Québec; York University; McGill University","funders":"","keywords":"Probabilistic forecasting; Electrical load; Probabilistic logic; Electric power system; Time series; Grid; Energy (signal processing); Electric utility; Electric power","score_opus":0.04184963957381766,"score_gpt":0.244868806391738,"score_spread":0.20301916681792034,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7139017617","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":[0.05088159,0.009682935,0.6052586,0.00028357565,0.0020988828,0.0010892709,0.0000116485135,0.0010422707,0.32965124],"genre_scores_gemma":[0.9749853,0.00028553506,0.012093996,0.00022887031,0.00027956485,0.0001138849,0.000016175474,0.00012947644,0.011867208],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9949168,0.00014952433,0.0016011001,0.0010640781,0.00070611626,0.0015623798],"domain_scores_gemma":[0.99757534,0.0007409407,0.00017742101,0.00081925135,0.00034602536,0.00034100155],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00084525696,0.0009989842,0.0013124582,0.00043057013,0.00054175936,0.00027359094,0.00063607487,0.0005682616,0.0006790829],"category_scores_gemma":[0.00058703247,0.0008976716,0.00073407585,0.001807923,0.00010187845,0.00050525833,0.00042851488,0.0009657577,0.000045723482],"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":[0.000072948096,0.00029542527,0.00009231515,0.0009536765,0.0024499658,0.00005433751,0.0005755019,0.81741375,0.0031999226,0.06846057,0.0021116755,0.104319945],"study_design_scores_gemma":[0.00093205523,0.00010059774,0.000018876208,0.00036431945,0.0020606443,0.000048808597,0.00001704691,0.9664525,0.0024776903,0.02293823,0.0037575078,0.0008317351],"about_ca_topic_score_codex":0.00045057124,"about_ca_topic_score_gemma":0.00016350561,"teacher_disagreement_score":0.9241037,"about_ca_system_score_codex":0.000795241,"about_ca_system_score_gemma":0.0004035329,"threshold_uncertainty_score":0.9993474},"labels":[],"label_agreement":null},{"id":"W7139949961","doi":"10.1109/isgtasia63446.2025.11431299","title":"A Hybrid Wind Power Forecasting System Integrating Dynamic Model Selection and Data Augmentation","year":2025,"lang":"","type":"article","venue":"","topic":"Energy Load and Power Forecasting","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":"McGill University","funders":"","keywords":"Selection (genetic algorithm); Wind power; Model selection; Power (physics); Feature selection; Data modeling","score_opus":0.019522504753462632,"score_gpt":0.2528162692923844,"score_spread":0.23329376453892176,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7139949961","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":[0.32950273,0.0009654282,0.6291177,0.000051764426,0.0011181892,0.00031651536,0.000077610464,0.00042610173,0.03842394],"genre_scores_gemma":[0.9767782,0.000053384123,0.021525444,0.000038832983,0.00006400694,0.000009317807,0.00018301331,0.000065762586,0.001282006],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974686,0.00006610584,0.0008440018,0.000819847,0.00022440686,0.00057703047],"domain_scores_gemma":[0.99892044,0.00018468391,0.00015997987,0.0005100485,0.000109574736,0.00011527953],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006659578,0.00048046044,0.00041666292,0.00036534446,0.00048144325,0.00040614622,0.00035381076,0.00016355333,0.00003290703],"category_scores_gemma":[0.00014008892,0.0005072919,0.00006039835,0.00047226332,0.000036500736,0.0010163379,0.000407402,0.0004940075,0.0000050855724],"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":[0.000042316293,0.000031651707,0.0005042532,0.0015285102,0.00033723228,0.00001113483,0.0009056094,0.9148857,0.004860068,0.011589671,0.0003984873,0.064905345],"study_design_scores_gemma":[0.00059523515,0.000044690336,0.000040022496,0.002199983,0.00017240523,0.000093890165,0.0013735265,0.99395657,0.0008008357,0.00019461637,0.000110689674,0.00041752635],"about_ca_topic_score_codex":0.00019999906,"about_ca_topic_score_gemma":0.00029716536,"teacher_disagreement_score":0.6472755,"about_ca_system_score_codex":0.00047855065,"about_ca_system_score_gemma":0.00013127552,"threshold_uncertainty_score":0.99973786},"labels":[],"label_agreement":null},{"id":"W744361766","doi":"10.1063/1.4915865","title":"Support vector machine for day ahead electricity price forecasting","year":2015,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"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":"Electricity price forecasting; Bidding; Support vector machine; Electricity market; Computer science; Volatility (finance); Electricity; Mean absolute percentage error; Minification; Artificial neural network; Econometrics; Machine learning; Mathematical optimization; Economics; Microeconomics; Engineering; Mathematics","score_opus":0.04408965965973612,"score_gpt":0.24082613750945056,"score_spread":0.19673647784971443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W744361766","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":[0.4617331,0.000613076,0.2612738,0.00048986997,0.0020040935,0.0012036477,0.00008401144,0.0026786043,0.2699198],"genre_scores_gemma":[0.99501246,0.000014682407,0.0038009435,0.00008284887,0.00032639026,0.000093255,0.000031386622,0.00006316031,0.0005748785],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985188,0.0000041465273,0.00032039388,0.00029955618,0.0002258272,0.0006312697],"domain_scores_gemma":[0.99909693,0.00008031808,0.00008175433,0.00009062855,0.00038904935,0.00026131765],"candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00050966156,0.00027763666,0.0002833211,0.000116691815,0.000101289086,0.00013661297,0.00028057204,0.00012780953,0.0000443949],"category_scores_gemma":[0.00041242564,0.00027333817,0.00006938147,0.00032228956,0.000030555155,0.00040852439,0.00004866582,0.00025229363,0.000022619733],"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":[0.0006505041,0.0005509459,0.17548645,0.004230813,0.0009444561,0.00006864993,0.052035794,0.0028022814,0.22268339,0.12186067,0.16149525,0.25719082],"study_design_scores_gemma":[0.0010495523,0.00048330947,0.00051765796,0.00013619692,0.000050392842,0.00007105819,0.00020729983,0.9224819,0.016654322,0.0029793878,0.054602724,0.0007662155],"about_ca_topic_score_codex":0.00002401176,"about_ca_topic_score_gemma":0.0000129282935,"teacher_disagreement_score":0.9196796,"about_ca_system_score_codex":0.0001156125,"about_ca_system_score_gemma":0.00009811453,"threshold_uncertainty_score":0.99997187},"labels":[],"label_agreement":null},{"id":"W9491067","doi":"10.4088/jcp.v59n0109","title":"Empirical and Artificial Neural Network Approach for Determination of Constant Drying Rate Phase of Medicinal and Aromatic Plants","year":2014,"lang":"en","type":"article","venue":"International Journal of Energy Engineering","topic":"Energy Load and Power Forecasting","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 New Brunswick","funders":"National Institute of Mental Health","keywords":"Payback period; Wind power; Electricity; Profit (economics); Environmental science; Duration (music); Renewable energy; Engineering; Wind speed; Environmental engineering; Environmental economics; Process engineering; Production (economics); Meteorology; Electrical engineering; Economics; Physics","score_opus":0.02274567586806989,"score_gpt":0.2650918145036788,"score_spread":0.24234613863560892,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W9491067","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":[0.5759766,0.00027342438,0.4231713,0.000011951478,0.00043004742,0.000016688673,0.000004378843,0.000009163297,0.00010640882],"genre_scores_gemma":[0.9860472,0.00004707726,0.013483809,0.00000972631,0.00038760382,0.0000018353044,0.000006722915,0.000014466958,0.0000015444657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991788,0.000015271848,0.00047177312,0.00006255717,0.00015934113,0.00011221347],"domain_scores_gemma":[0.99937755,0.00028857717,0.00016679321,0.000035860943,0.00007406913,0.00005713625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003223314,0.000100355275,0.0002304375,0.0001576999,0.000014339831,0.000014562957,0.00009018221,0.00004339557,0.0000013259831],"category_scores_gemma":[0.000107300264,0.00009241295,0.00003993234,0.00004383334,0.000029123963,0.00011035957,0.00001594495,0.00007858832,8.939832e-9],"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":[0.000116049574,0.00003636954,0.00046140124,0.00012507338,0.0001518157,0.0000091247375,0.00025937648,0.8871752,0.05441911,0.0022031593,0.000023618288,0.055019718],"study_design_scores_gemma":[0.00073367567,0.00014289537,0.0001968485,0.00022042824,0.000024762305,0.00014579434,0.000022594628,0.986128,0.011750696,0.00036964484,0.00018103953,0.00008363921],"about_ca_topic_score_codex":0.000002028343,"about_ca_topic_score_gemma":0.000001205691,"teacher_disagreement_score":0.41007057,"about_ca_system_score_codex":0.000016078935,"about_ca_system_score_gemma":0.000008890706,"threshold_uncertainty_score":0.37684909},"labels":[],"label_agreement":null}]}