{"meta":{"query_hash":"ea61d8800430","filters":{"venue":"International Energy Journal"},"cohort_total":3,"direct_labels_cover":0,"predictions_cover":3,"exported":3,"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/ea61d8800430","api":"https://metacan.xera.ac/api/v1/cohort?venue=International+Energy+Journal"},"results":[{"id":"W3035596020","doi":"","title":"Stochastic Model for Generating Synthetic Hourly Global Horizontal Solar Radiation Data Sets Based on Auto Regression Characterization","year":2020,"lang":"en","type":"article","venue":"International Energy Journal","topic":"Solar Radiation and Photovoltaics","field":"Computer Science","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 Manitoba","funders":"","keywords":"Monte Carlo method; Radiation; Stochastic modelling; Probability distribution; Data set; Autocorrelation; Meteorology; Environmental science; Computer science; Mathematics; Statistics; Geography; Physics","score_opus":0.04210618833291274,"score_gpt":0.2869506609439145,"score_spread":0.24484447261100178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3035596020","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":[0.08982075,0.000085617816,0.90717745,0.000087145265,0.000026118782,0.000067387795,0.00082935166,0.0009525364,0.0009536499],"genre_scores_gemma":[0.91462946,0.00017102638,0.08101492,0.00004079734,0.000026220967,0.00025097327,0.0024564571,0.00009232034,0.001317955],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996345,0.000101512065,0.000028334318,0.000112858914,0.00008992921,0.000032804437],"domain_scores_gemma":[0.99924135,0.00040133123,0.0001208381,0.00007118476,0.00014682808,0.000018512399],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007802188,0.00046128433,0.0004539207,0.0005393686,0.00017358336,0.00040183213,0.00081670185,0.00049014925,0.00072282704],"category_scores_gemma":[0.0018557037,0.00033123032,0.000738209,0.00070789835,0.00022218129,0.0004181644,0.00031017166,0.0006234011,0.00022855242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000015134212,0.000011538398,0.0007653254,0.0000148672725,0.000012540387,0.00002402143,0.000008358558,0.991991,0.00063027174,0.001457747,0.00019157921,0.0048777303],"study_design_scores_gemma":[0.0000011168703,0.000002977625,0.00014843531,6.139217e-7,0.0000010319899,0.0000033597319,0.0000010453304,0.9994506,0.00014052367,0.00019450832,0.00005429416,0.0000014575642],"about_ca_topic_score_codex":0.012215794,"about_ca_topic_score_gemma":0.009051598,"teacher_disagreement_score":0.012215794,"about_ca_system_score_codex":0.00046794428,"about_ca_system_score_gemma":0.00057867554,"threshold_uncertainty_score":0.02428937},"labels":[],"label_agreement":null},{"id":"W3093028472","doi":"","title":"Short-run and Long-run Gasoline Demand Elasticities: A Case Study of Australia","year":2020,"lang":"en","type":"article","venue":"International Energy Journal","topic":"Energy, Environment, and Transportation Policies","field":"Energy","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":"Economics; Gasoline; Short run; Stockpile; Instrumental variable; Price elasticity of demand; Distributed lag; Panel data; Econometrics; Income elasticity of demand; Error correction model; Quarter (Canadian coin); Monetary economics; Cointegration; Microeconomics; Engineering","score_opus":0.033620443053195184,"score_gpt":0.28651845630809186,"score_spread":0.2528980132548967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093028472","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":[0.9983333,0.000056150402,0.00047361068,0.00008043242,0.0000012484436,0.0000112365715,0.00011995797,0.0000035699077,0.00092049025],"genre_scores_gemma":[0.99780196,0.0001259622,0.00050623645,0.00002446261,0.0000028636737,0.000011098523,0.00027895047,0.00000338298,0.0012450899],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995085,0.00017400741,0.000032793043,0.00008457918,0.00008798836,0.00011225038],"domain_scores_gemma":[0.9985089,0.0006512778,0.00029439633,0.000115774696,0.00030894694,0.00012065443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090612704,0.00026951026,0.00032443088,0.00073900016,0.0005902174,0.0008778116,0.00048236028,0.00053911336,0.0011741829],"category_scores_gemma":[0.0025227005,0.0002696971,0.0007845586,0.0016664919,0.0004767501,0.0010132452,0.0009544038,0.0009370468,0.00016944435],"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":[0.00022725512,0.0007540556,0.9081287,0.00015562294,0.00039944815,0.004204345,0.004179585,0.053856537,0.0017674343,0.005922538,0.0010271834,0.019377358],"study_design_scores_gemma":[0.00002022129,0.00031137432,0.892031,0.000042592237,0.00015449642,0.00065079797,0.009176039,0.090149574,0.0010831216,0.0022663972,0.004044006,0.00007032312],"about_ca_topic_score_codex":0.18415514,"about_ca_topic_score_gemma":0.19407874,"teacher_disagreement_score":0.18415514,"about_ca_system_score_codex":0.0017131853,"about_ca_system_score_gemma":0.0011112478,"threshold_uncertainty_score":0.36616647},"labels":[],"label_agreement":null},{"id":"W7131778669","doi":"10.64289/iej.24.0103.5248721","title":"Evaluating the Impact of Time-of-Use Billing on Energy Costs in a University Building in Newfoundland, Canada","year":2024,"lang":"","type":"article","venue":"International Energy Journal","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Memorial University of Newfoundland","keywords":"Tariff; Energy consumption; Energy (signal processing); Consumption (sociology); Yield (engineering); Electric energy; Electric energy consumption; Efficient energy use","score_opus":0.01778609290125787,"score_gpt":0.2747672274049975,"score_spread":0.2569811345037396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7131778669","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":[0.99023294,0.000094579365,0.00077684876,0.00019823655,0.000009633754,0.000052546646,0.0014172172,0.00004645591,0.0071714814],"genre_scores_gemma":[0.9965905,0.00009056162,0.0008819475,0.000025526448,0.0000013307913,0.000011905071,0.00082035316,0.0000129846,0.0015648339],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9991035,0.00013481831,0.000022843074,0.000085482854,0.00019812511,0.00045526747],"domain_scores_gemma":[0.9984066,0.00057830825,0.000106648054,0.000076969496,0.00065732957,0.00017418213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082291785,0.00071461895,0.0004901829,0.00089674443,0.0014609082,0.0019293121,0.0017523518,0.0007321237,0.0025442324],"category_scores_gemma":[0.0024921587,0.00047799418,0.0010094503,0.0016923195,0.0010184019,0.00070209714,0.0006248677,0.00090049347,0.00016350835],"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":[0.00035773547,0.00031962324,0.08047856,0.00008049244,0.00011242989,0.0004633423,0.0001661099,0.8996606,0.0015440037,0.0032450354,0.0028030728,0.010768995],"study_design_scores_gemma":[0.00007470856,0.0002851083,0.1406392,0.00004032639,0.00014409107,0.00008777386,0.0019668327,0.8509235,0.002334794,0.00040637443,0.002990033,0.00010722245],"about_ca_topic_score_codex":0.9890183,"about_ca_topic_score_gemma":0.99220204,"teacher_disagreement_score":0.053950597,"about_ca_system_score_codex":0.053950597,"about_ca_system_score_gemma":0.017839061,"threshold_uncertainty_score":0.3914408},"labels":[],"label_agreement":null}]}