{"meta":{"query_hash":"89bbdac4a4d8","filters":{"venue":"Smart Grids and Sustainable Energy"},"cohort_total":5,"direct_labels_cover":0,"predictions_cover":5,"exported":5,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/89bbdac4a4d8","api":"https://metacan.xera.ac/api/v1/cohort?venue=Smart+Grids+and+Sustainable+Energy"},"results":[{"id":"W4367053038","doi":"10.1007/s40866-023-00166-1","title":"Online Power Transfer Regulation Between Transmission and Active Distribution Systems for High-Voltage Support","year":2023,"lang":"en","type":"article","venue":"Smart Grids and Sustainable Energy","topic":"Optimal Power Flow Distribution","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":"AC power; Interconnection; Maximum power transfer theorem; Computer science; Transmission (telecommunications); Electric power system; Transmission system; Power (physics); Voltage regulation; Power transmission; Control theory (sociology); Voltage; Engineering; Control (management); Electrical engineering; Telecommunications","score_opus":0.00592776986298106,"score_gpt":0.2051302493738905,"score_spread":0.19920247951090944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367053038","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05018923,0.00031223454,0.93022436,0.0004938864,0.0002773833,0.000093224546,0.000067759654,0.0018365233,0.016505335],"genre_scores_gemma":[0.9901536,0.000045474986,0.007986023,0.00007265267,0.000043406686,0.000031151747,0.00002844954,0.000053498483,0.0015857161],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992005,0.00027757973,0.000033141805,0.00016991621,0.00021680056,0.00010210049],"domain_scores_gemma":[0.9986518,0.00071206054,0.00014869183,0.00015995403,0.00028210698,0.000045401976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009344109,0.00050558976,0.00053014734,0.00033016593,0.0005332078,0.0014639294,0.0011151402,0.000602479,0.0065404302],"category_scores_gemma":[0.002819726,0.00030886295,0.00028805088,0.00035475817,0.00054987764,0.0018569159,0.00076199614,0.0014385285,0.0008103383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097565795,0.0007740724,0.0013578256,0.0002223345,0.0000749306,0.00017850842,0.00020263508,0.6729798,0.026082164,0.03314335,0.0100261485,0.25398266],"study_design_scores_gemma":[0.000025859468,0.000078331694,0.00027683814,0.000014558317,0.000012920668,0.00002828584,0.000023200913,0.9857978,0.004941986,0.007268948,0.0015234275,0.000007892283],"about_ca_topic_score_codex":0.001469951,"about_ca_topic_score_gemma":0.0021294197,"teacher_disagreement_score":0.0065404302,"about_ca_system_score_codex":0.00066905544,"about_ca_system_score_gemma":0.0006702167,"threshold_uncertainty_score":0.021879852},"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":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3146329,0.00082447345,0.67363834,0.000502461,0.00027570056,0.000042883126,0.00037089558,0.0012346014,0.008477655],"genre_scores_gemma":[0.9844097,0.0001428075,0.01333196,0.00004369053,0.000037449656,0.00002332703,0.00017374758,0.000021185831,0.0018161462],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983656,0.00002407125,0.000009794524,0.000049454346,0.00005640855,0.000023740202],"domain_scores_gemma":[0.99976104,0.000094564595,0.000027824644,0.000023069684,0.0000822489,0.000011360837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003491862,0.0004947963,0.00060297473,0.00038695324,0.00032580682,0.0006914473,0.00073515764,0.00066491036,0.0007927336],"category_scores_gemma":[0.0008242257,0.0003031748,0.00043638682,0.0005305545,0.0002227264,0.00096406706,0.0003748199,0.000823097,0.00020982027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005893897,0.00004867451,0.0010186414,0.0000165861,0.000029880674,0.000034048728,0.000014603508,0.96929985,0.0011994168,0.00050325174,0.0004466247,0.027329432],"study_design_scores_gemma":[0.0000010815178,0.0000028311306,0.00011403415,4.831719e-7,0.0000020883253,0.0000015775447,6.978037e-7,0.9996885,0.00008351482,0.00008048709,0.000023748255,0.0000010707561],"about_ca_topic_score_codex":0.019286117,"about_ca_topic_score_gemma":0.021148026,"teacher_disagreement_score":0.019286117,"about_ca_system_score_codex":0.0005094985,"about_ca_system_score_gemma":0.00048464717,"threshold_uncertainty_score":0.03834772},"labels":[],"label_agreement":null},{"id":"W4397034408","doi":"10.1007/s40866-024-00206-4","title":"The Potential of Vehicle-to-Home Integration for Residential Prosumers: A Case Study","year":2024,"lang":"en","type":"article","venue":"Smart Grids and Sustainable Energy","topic":"Electric Vehicles and Infrastructure","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":"Business","score_opus":0.003096063236907057,"score_gpt":0.2059223933165665,"score_spread":0.20282633007965944,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397034408","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9798219,0.00012958697,0.007277498,0.0001624128,0.000020866184,0.000091829424,0.00023880061,0.000096311785,0.012160645],"genre_scores_gemma":[0.9969988,0.000039900657,0.0016122038,0.0000071315258,0.0000023141615,0.000016278846,0.000045164066,0.000005553746,0.0012727301],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9996512,0.00017950269,0.000009724965,0.000028920218,0.000052540534,0.000078104254],"domain_scores_gemma":[0.9992866,0.00042721137,0.000036680834,0.000066357214,0.00010601901,0.00007718492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060908287,0.00065610115,0.00044139533,0.000458548,0.00066642853,0.0009964478,0.0010150302,0.0012531762,0.0043006986],"category_scores_gemma":[0.00081245485,0.0002790196,0.0005640171,0.0006008384,0.0005858718,0.00083922636,0.0007668819,0.0007626488,0.0003532678],"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":[0.000426019,0.00048542517,0.007833099,0.00012265058,0.000059625385,0.0027671966,0.00017740755,0.9695358,0.002869162,0.005893076,0.0010914529,0.008739149],"study_design_scores_gemma":[0.00008045031,0.00054234196,0.003389767,0.000018963536,0.000049840146,0.0003166512,0.00081328844,0.9870263,0.003342536,0.0024232054,0.0019669647,0.000029771416],"about_ca_topic_score_codex":0.01028866,"about_ca_topic_score_gemma":0.012852042,"teacher_disagreement_score":0.01028866,"about_ca_system_score_codex":0.0009201374,"about_ca_system_score_gemma":0.00049075304,"threshold_uncertainty_score":0.020457566},"labels":[],"label_agreement":null},{"id":"W4397039367","doi":"10.1007/s40866-024-00205-5","title":"Distribution Grid Fault Classification and Localization using Convolutional Neural Networks","year":2024,"lang":"en","type":"article","venue":"Smart Grids and Sustainable Energy","topic":"Power Systems Fault Detection","field":"Engineering","cited_by":22,"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":"Convolutional neural network; Computer science; Fault (geology); Artificial intelligence; Grid; Pattern recognition (psychology); Distribution (mathematics); Artificial neural network; Geology; Mathematics; Seismology; Geodesy","score_opus":0.00685186422698429,"score_gpt":0.20791351711929235,"score_spread":0.20106165289230807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4397039367","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3264527,0.00191741,0.65760016,0.00097142,0.0003088148,0.00007726211,0.0010418507,0.004297558,0.0073329145],"genre_scores_gemma":[0.95924366,0.00033570614,0.033740956,0.000081598344,0.00006920474,0.000022039108,0.0009553493,0.000037193713,0.0055143223],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984956,0.000015723861,0.000006994383,0.000050236762,0.000028785062,0.000048605678],"domain_scores_gemma":[0.9996301,0.00013081708,0.000055609245,0.000050999548,0.00011081442,0.000021612019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003135205,0.0007188339,0.000452643,0.00078380277,0.00028930535,0.0007145021,0.00073826144,0.0006525574,0.0014939999],"category_scores_gemma":[0.000961879,0.0003357473,0.00045819144,0.00070505275,0.00024401516,0.00066943374,0.0004987571,0.0007830626,0.0006116576],"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":[0.00041853543,0.00024887515,0.008918871,0.0000793097,0.00011230966,0.00016791848,0.00005028737,0.40939054,0.009292892,0.0030973528,0.007679241,0.56054384],"study_design_scores_gemma":[0.0000027749295,0.000008984445,0.00062946527,0.0000035405783,0.000006756291,0.000007823574,0.000004811587,0.99697053,0.0014419643,0.0006840262,0.00023692263,0.0000023613086],"about_ca_topic_score_codex":0.028108027,"about_ca_topic_score_gemma":0.029701764,"teacher_disagreement_score":0.028108027,"about_ca_system_score_codex":0.00094719615,"about_ca_system_score_gemma":0.0006805602,"threshold_uncertainty_score":0.05588883},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5639484,0.0009137971,0.42561817,0.00064498314,0.0003653599,0.0000852123,0.00067646976,0.0011763992,0.006571125],"genre_scores_gemma":[0.9717287,0.00013395015,0.026410827,0.00003727253,0.000053208867,0.000034318662,0.00043638152,0.000028534076,0.0011368141],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997365,0.000078585224,0.000024059109,0.00006430121,0.000059854086,0.000036768808],"domain_scores_gemma":[0.99923944,0.0004443669,0.00007330865,0.000044019325,0.00018289841,0.000016101561],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064777356,0.00068020483,0.0007543507,0.00068210496,0.00025662594,0.0009911588,0.00043024856,0.00050816545,0.0011757353],"category_scores_gemma":[0.002433524,0.0004023174,0.0004277085,0.00070438656,0.00022601483,0.0010056213,0.00025911897,0.00078518357,0.00025824318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000103788254,0.00006758572,0.002295942,0.000018550852,0.00005881292,0.000013024986,0.0000088737,0.9634761,0.0005546484,0.0008209775,0.0007425682,0.03183914],"study_design_scores_gemma":[0.000002926297,0.0000044328376,0.00023441609,6.0066594e-7,0.0000032184757,7.8530036e-7,8.123887e-7,0.9993774,0.00013539521,0.00020897672,0.000029907496,0.0000011165585],"about_ca_topic_score_codex":0.011192666,"about_ca_topic_score_gemma":0.012007899,"teacher_disagreement_score":0.011192666,"about_ca_system_score_codex":0.0006878872,"about_ca_system_score_gemma":0.0006332491,"threshold_uncertainty_score":0.022255063},"labels":[],"label_agreement":null}]}