{"meta":{"query_hash":"b5fe38051880","filters":{"venue":"Energy RESEARCH LETTERS"},"cohort_total":2,"direct_labels_cover":0,"predictions_cover":2,"exported":2,"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/b5fe38051880","api":"https://metacan.xera.ac/api/v1/cohort?venue=Energy+RESEARCH+LETTERS"},"results":[{"id":"W4220912670","doi":"10.46557/001c.32617","title":"An Empirical Analysis of the Impact of COVID-19 on the Power Sector of India","year":2022,"lang":"en","type":"article","venue":"Energy RESEARCH LETTERS","topic":"COVID-19 impact on air quality","field":"Environmental Science","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":"Coronavirus disease 2019 (COVID-19); Market liquidity; Energy sector; Pandemic; Economics; Business; Clearing; Monetary economics; Emerging markets; Transmission (telecommunications); Distribution (mathematics); Development economics; International economics; Natural resource economics; Macroeconomics; Finance; Telecommunications","score_opus":0.09697218590425292,"score_gpt":0.440467127059459,"score_spread":0.34349494115520607,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220912670","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":[0.99542063,0.00018990967,0.0001545142,0.00045001606,0.000006822689,0.000008784248,0.0009460485,0.000011846218,0.002811374],"genre_scores_gemma":[0.99841976,0.00014296094,0.00003363699,0.000035193083,0.000009714597,0.000003191139,0.0008814854,0.0000026555063,0.00047127585],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993388,0.00015451852,0.000039511735,0.00007105117,0.000113877664,0.00028215247],"domain_scores_gemma":[0.98991454,0.004637817,0.003272553,0.00039359348,0.0009525616,0.00082899374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008021168,0.00021882278,0.00022800198,0.0009332324,0.00043259046,0.0016135727,0.00054153835,0.00042000908,0.002922922],"category_scores_gemma":[0.0050530722,0.00016477202,0.000463432,0.0022990173,0.00072201673,0.00076488184,0.0008478024,0.0014617293,0.00044796575],"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":[0.00011660593,0.00010042726,0.9866666,0.000037999056,0.00008800963,0.00046192732,0.0003195982,0.0053643845,0.00024486482,0.0008798961,0.0016611004,0.004058668],"study_design_scores_gemma":[0.000007038647,0.00008083386,0.9874878,0.0000191356,0.000058585876,0.0001757371,0.0019373603,0.008221929,0.00032633042,0.00021088915,0.0014607732,0.000013495984],"about_ca_topic_score_codex":0.08131541,"about_ca_topic_score_gemma":0.061520148,"teacher_disagreement_score":0.08131541,"about_ca_system_score_codex":0.0013541017,"about_ca_system_score_gemma":0.0009239963,"threshold_uncertainty_score":0.16168416},"labels":[],"label_agreement":null},{"id":"W4399150881","doi":"10.46557/001c.94677","title":"Causal Relatıonshıp Between Energy Taxes and Green Innovatıon in G-7 Countrıes: New Evıdence Based on Fourıer Functıons","year":2024,"lang":"en","type":"article","venue":"Energy RESEARCH LETTERS","topic":"Energy, Environment, Economic Growth","field":"Economics, Econometrics and Finance","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":"Causality (physics); Energy (signal processing); Boosting (machine learning); Sustainable energy; Economics; Econometrics; Demographic economics; Statistics; Engineering; Physics; Computer science; Mathematics; Renewable energy; Artificial intelligence","score_opus":0.0670368417599167,"score_gpt":0.2693424125251481,"score_spread":0.20230557076523142,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399150881","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":[0.98943055,0.0008958536,0.0046536457,0.0013191184,0.000073949595,0.000024068231,0.0010026824,0.00006539436,0.0025346803],"genre_scores_gemma":[0.99667835,0.00023503779,0.0004449018,0.000083187944,0.0000314272,0.000016221673,0.0009775661,0.000010055586,0.0015231582],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99765766,0.0009889863,0.00015723414,0.00045393495,0.00024437517,0.0004977208],"domain_scores_gemma":[0.9701704,0.019830335,0.0058041527,0.002096426,0.0011846637,0.00091412774],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004992255,0.00058751303,0.0009919221,0.0020884254,0.00060124823,0.001742841,0.00088073633,0.0013525892,0.008848509],"category_scores_gemma":[0.0140613,0.00026060204,0.0021346342,0.0019088127,0.0015290654,0.0017399581,0.0016136088,0.0024268448,0.00082104193],"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":[0.0004955424,0.00019618428,0.9693229,0.0000724903,0.0010028864,0.0004874619,0.0004141691,0.0056428052,0.0003063576,0.0061315517,0.0011012893,0.014826304],"study_design_scores_gemma":[0.00009725019,0.00041253574,0.92862666,0.00010906285,0.001423215,0.00028860042,0.003231082,0.042087987,0.0015129746,0.015788132,0.006314169,0.000108354354],"about_ca_topic_score_codex":0.027431592,"about_ca_topic_score_gemma":0.018982194,"teacher_disagreement_score":0.027431592,"about_ca_system_score_codex":0.0011539789,"about_ca_system_score_gemma":0.00097128557,"threshold_uncertainty_score":0.054543853},"labels":[],"label_agreement":null}]}