{"meta":{"page":1,"per_page":50,"max_per_page":100,"total":10,"total_is_capped":false,"direct_labels_cover":0,"predictions_cover":10,"direct_label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline (scores rank; they never assert a category)","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12","author_layer_release":"2026-06-26"},"query_hash":"cd399dbc6883","filters":{"venue":"Green Energy and Resources"}},"results":[{"id":"W4360874586","doi":"10.1016/j.gerr.2023.100012","title":"Synergistic utilization of blast furnace slag with other industrial solid wastes in cement and concrete industry: Synergistic mechanisms, applications, and challenges","year":2023,"lang":"en","type":"article","venue":"Green Energy and Resources","topic":"Concrete and Cement Materials Research","field":"Engineering","cited_by":104,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Fundamental Research Funds for the Central Universities; China Academy of Space Technology; National Natural Science Foundation of China","keywords":"Ground granulated blast-furnace slag; Cementitious; Cement; Slag (welding); Gypsum; Waste management; Raw material; Materials science; Engineering; Metallurgy; Chemistry","authors":[{"name":"Qingsen Zeng","is_ca":false},{"name":"Xiaoming Liu","is_ca":false},{"name":"Zengqi Zhang","is_ca":false},{"name":"Chao Wei","is_ca":false},{"name":"Chunbao Xu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.0434089513677165,"gpt":0.239562840015514,"spread":0.1961538886477975,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001872595,0.0001343089,0.0002009221,0.0001773811,0.00005656278,0.00002991743,0.00006521338,0.0001620499,0.00002331638],"category_scores_gemma":[0.000009881664,0.0001118848,0.000007849526,0.0001892858,0.0001055503,0.00004536524,0.00007264857,0.00009428581,5.508354e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009818466,"about_ca_system_score_gemma":0.000009886465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003289375,"about_ca_topic_score_gemma":0.0002405509,"domain_scores_codex":[0.9992143,0.00005226746,0.0001903443,0.0001923956,0.0001553406,0.0001953705],"domain_scores_gemma":[0.9996787,0.00008175406,0.00004204103,0.0001090045,0.00001981092,0.0000687164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001249786,0.00006335843,0.0079253,0.004725012,0.001243353,0.0001069612,0.009169629,0.01960207,0.4460378,0.08684537,0.0004787462,0.4225526],"study_design_scores_gemma":[0.01954608,0.003910808,0.01686543,0.005122024,0.0007022531,0.00007373275,0.03226607,0.2550183,0.338749,0.006606887,0.3165812,0.004558234],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9952869,0.00318148,0.000660062,0.0001679601,0.00002443947,0.0001542388,0.00002559793,0.00006415569,0.0004351723],"genre_scores_gemma":[0.9944827,0.005149245,0.0000242186,0.00001011073,0.00007688798,0.00006921795,0.00001504733,0.00002189415,0.0001506678],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4179944,"threshold_uncertainty_score":0.456253,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4408079123","doi":"10.1016/j.gerr.2025.100118","title":"Solar dryers: A review of mechanism, methods and critical analysis of transport models applicable in solar drying of product","year":2025,"lang":"en","type":"review","venue":"Green Energy and Resources","topic":"Food Drying and Modeling","field":"Agricultural and Biological Sciences","cited_by":16,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"McGill University","funders":"","keywords":"Mechanism (biology); Process engineering; Environmental science; Engineering; Physics","authors":[{"name":"Onyinyechi Adanma Nnamchi","is_ca":false},{"name":"Cyprian N. Tom","is_ca":false},{"name":"Godwin Akpan","is_ca":false},{"name":"Ibeh Mathew","is_ca":false},{"name":"Linus –Chibuezeh Adindu","is_ca":false},{"name":"Leonard Akuwueke","is_ca":false},{"name":"Stephen Ndubuisi Nnamchi","is_ca":false},{"name":"Augustine Edet Ben","is_ca":true},{"name":"Macmanus Chinenye Ndukwu","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.04889110637366587,"gpt":0.3255608799946964,"spread":0.2766697736210305,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001247948,0.000245819,0.002236622,0.0001524012,0.00006290304,0.00000600165,0.0002299587,0.0002144274,0.00001165647],"category_scores_gemma":[0.00006791399,0.0001023129,0.000429188,0.001179203,0.000135276,0.00005030837,0.00008779125,0.0001425123,1.584692e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006025442,"about_ca_system_score_gemma":0.00002380468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003016441,"about_ca_topic_score_gemma":0.0004239861,"domain_scores_codex":[0.997915,0.0003607947,0.0008588575,0.0004782123,0.000185792,0.0002013507],"domain_scores_gemma":[0.9989436,0.000493318,0.000294719,0.0001241947,0.00008339945,0.00006082782],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008553044,0.00004789604,0.00001098381,0.04587208,0.0003313442,0.00000121632,0.00008066504,0.0000400131,0.0002115707,0.001140329,0.000001565897,0.9522538],"study_design_scores_gemma":[0.0004508418,0.000948697,0.0001226038,0.4362249,0.03577703,0.00002439919,0.000758645,0.009679222,0.002291302,0.009304605,0.5021634,0.002254418],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001053027,0.99775,0.0007313345,0.0000585666,0.00001226863,0.0001669637,0.00007599636,0.000009676779,0.0001421742],"genre_scores_gemma":[0.004502101,0.9936565,0.001627021,0.00003587386,0.00002436733,0.00003073288,0.0000550126,0.000002005049,0.00006634592],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9499994,"threshold_uncertainty_score":0.4559977,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4407849306","doi":"10.1016/j.gerr.2025.100119","title":"Enhanced prediction of heating value of municipal solid waste using hybrid neuro-fuzzy model and decision tree-based feature importance assessment","year":2025,"lang":"en","type":"article","venue":"Green Energy and Resources","topic":"Smart Systems and Machine Learning","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"George Brown College","funders":"","keywords":"Decision tree; Feature (linguistics); Municipal solid waste; Tree (set theory); Value (mathematics); Artificial intelligence; Neuro-fuzzy; Fuzzy logic; Computer science; Environmental science; Machine learning; Waste management; Mathematics; Engineering; Fuzzy control system","authors":[{"name":"Oluwatobi Adeleke","is_ca":false},{"name":"Obafemi O. Olatunji","is_ca":false},{"name":"Tien‐Chien Jen","is_ca":false},{"name":"Iretioluwa Olawuyi","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.009712534361237639,"gpt":0.2557120363261273,"spread":0.2459995019648896,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002731945,0.0001226018,0.0002525332,0.0001499929,0.0001774364,0.00003029417,0.0001882915,0.00005880111,3.006325e-7],"category_scores_gemma":[0.0000213339,0.0001010335,0.00004678208,0.0001917713,0.00005502275,0.000125028,0.0001793354,0.0001038427,7.216197e-9],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001216069,"about_ca_system_score_gemma":0.00004414664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001079131,"about_ca_topic_score_gemma":0.0001276828,"domain_scores_codex":[0.9989886,0.00008386573,0.0002936613,0.000294259,0.0001973099,0.0001423167],"domain_scores_gemma":[0.9992855,0.0001451239,0.0002174069,0.0002589622,0.00004970486,0.00004335798],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006737981,0.0000478637,0.01626491,0.0001695458,0.00005149024,0.000005468372,0.0008695448,0.8707411,0.04886261,0.007770519,0.00003591702,0.05511371],"study_design_scores_gemma":[0.0004571684,0.00008312742,0.006329982,0.0003233018,0.00001697148,0.000004390226,0.00006042591,0.9862733,0.005165498,0.001135373,0.00007906232,0.000071426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6408189,0.0004039686,0.3583327,0.00005513438,0.00005957769,0.00003648415,0.000003260674,0.00001727895,0.0002726943],"genre_scores_gemma":[0.9791403,0.00002153148,0.02061112,0.000104918,0.00003544179,0.000003420595,0.000002553524,0.000006051997,0.00007463071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3383214,"threshold_uncertainty_score":0.4120027,"prediction_status":"machine_predicted_unvalidated"},"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,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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","authors":[{"name":"Ali Omidkar","is_ca":true},{"name":"Razieh Es’haghian","is_ca":true},{"name":"Hua Song","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02928163507789641,"gpt":0.3123080897538571,"spread":0.2830264546759607,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004776925,0.00009782408,0.0001604967,0.0001113546,0.00008740283,0.0000151483,0.00004861241,0.0000932216,0.00000937035],"category_scores_gemma":[0.00001926286,0.00008889133,0.00002895559,0.00003847933,0.00004554337,0.00004512985,0.00003755391,0.00006254436,3.806831e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001981983,"about_ca_system_score_gemma":0.000009819159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001885134,"about_ca_topic_score_gemma":0.0002235744,"domain_scores_codex":[0.9994797,0.00006321903,0.0001653034,0.0001287338,0.00004615479,0.0001169012],"domain_scores_gemma":[0.99971,0.0001423733,0.00003826185,0.0000657846,0.00001476205,0.00002880688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001096336,0.00001953664,0.0629693,0.0002901872,0.0003298247,0.0000016868,0.0007142469,0.342097,0.03532989,0.002095815,0.00001874073,0.5560242],"study_design_scores_gemma":[0.0009783139,0.00006934648,0.02107216,0.000278968,0.0001522822,0.00002563011,0.00004325952,0.951367,0.0164316,0.0007266171,0.008606567,0.0002482928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.975548,0.006641062,0.01568181,0.00001173786,0.0001052824,0.00004683937,0.000005553697,0.00004199142,0.001917763],"genre_scores_gemma":[0.9960076,0.0001293107,0.003657825,0.00001023296,0.00003886564,0.000003852562,0.000009671928,0.00001275959,0.0001298701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.60927,"threshold_uncertainty_score":0.3624883,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4404733220","doi":"10.1016/j.gerr.2024.100104","title":"Modification approach of Northern Wall to improve the performance of solar greenhouse dryers: A review","year":2024,"lang":"en","type":"review","venue":"Green Energy and Resources","topic":"Greenhouse Technology and Climate Control","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of New Brunswick","funders":"","keywords":"Solar greenhouse; Environmental science; Greenhouse; Process engineering; Materials science; Engineering physics; Engineering; Horticulture; Biology","authors":[{"name":"Macmanus Chinenye Ndukwu","is_ca":false},{"name":"Leonard Akuwueke","is_ca":false},{"name":"Godwin Akpan","is_ca":false},{"name":"M.F. Umunna","is_ca":false},{"name":"Godwin Usoh","is_ca":false},{"name":"Ekop Inemesit","is_ca":false},{"name":"Etim Promise","is_ca":false},{"name":"I. Okosa","is_ca":false},{"name":"Francis Orji","is_ca":false},{"name":"E.C. Ikechukwu-Edeh","is_ca":false},{"name":"Ifiok Edem Ekop","is_ca":false},{"name":"Merlin Simo‐Tagne","is_ca":false},{"name":"Lyes Bennamoun","is_ca":true},{"name":"Hongwei Wu","is_ca":false},{"name":"Fidelis I. Abam","is_ca":false}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02514916624729912,"gpt":0.2357290986293854,"spread":0.2105799323820863,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003927663,0.0002961106,0.0009837567,0.00003906328,0.0001260686,0.00001362681,0.0006463131,0.0003196776,0.00001246171],"category_scores_gemma":[0.00001744119,0.0000848393,0.0003074576,0.000498462,0.0001939737,0.00003531305,0.0001892552,0.0002244506,0.000008009475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001108508,"about_ca_system_score_gemma":0.00001313134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008432906,"about_ca_topic_score_gemma":0.0008580524,"domain_scores_codex":[0.9984996,0.0001352946,0.0005594445,0.0004025388,0.0001847279,0.0002183686],"domain_scores_gemma":[0.9991844,0.0001066947,0.0003740592,0.0002154606,0.00006460051,0.00005480212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008381973,0.00003280188,0.00002883261,0.01036476,0.0001134237,6.41899e-7,0.0000589455,5.110653e-7,0.00006330598,0.0001712113,0.00008767984,0.9890695],"study_design_scores_gemma":[0.00003896262,0.0002830856,0.0000795473,0.008490236,0.0008861958,0.00001433361,0.00005064165,0.00005925843,0.00001325557,0.00003451698,0.9898264,0.0002235541],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01154676,0.9872151,0.000001394135,0.0004879846,0.00002789986,0.0004238247,0.00006397741,0.00006048821,0.0001725782],"genre_scores_gemma":[0.007407293,0.9916549,0.00001063616,0.0001383326,0.0000848694,0.0001504151,0.00004520263,0.000004771361,0.0005036241],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9897387,"threshold_uncertainty_score":0.3459646,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4414053311","doi":"10.1016/j.gerr.2025.100141","title":"Exploring the application of artificial intelligence for bioelectrochemical systems: A review of recent research","year":2025,"lang":"en","type":"article","venue":"Green Energy and Resources","topic":"Microbial Fuel Cells and Bioremediation","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Western University","keywords":"Scalability; Standardization; Artificial neural network; Efficient energy use; Applications of artificial intelligence; Support vector machine; Biomimetics","authors":[{"name":"Ying Zheng","is_ca":true},{"name":"Amarjeet Bassi","is_ca":true},{"name":"Tianlong Liu","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08460331351041822,"gpt":0.3037799663582716,"spread":0.2191766528478534,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000648658,0.00003760699,0.00008395044,0.00002463864,0.00007186634,0.000004397103,0.0001228647,0.00002344947,0.00001425239],"category_scores_gemma":[0.00004632759,0.0000234468,0.00002073645,0.0002993803,0.0001675035,0.0000214972,0.00006675965,0.00003509365,5.805941e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001739304,"about_ca_system_score_gemma":0.000005178851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001486957,"about_ca_topic_score_gemma":0.0001682101,"domain_scores_codex":[0.999429,0.00005717966,0.0001937584,0.0001178273,0.0001065864,0.000095586],"domain_scores_gemma":[0.9996802,0.0001140449,0.00005701619,0.000104084,0.00002990744,0.00001473662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006722648,0.00004668023,0.0001891513,0.001667325,0.00001270952,5.358231e-8,0.0002982418,0.00005580547,0.3270988,0.008694425,0.0005190918,0.6613505],"study_design_scores_gemma":[0.0000472713,0.0001291597,0.0003528553,0.0009771159,0.00002936318,8.991867e-7,0.0003693697,0.002966033,0.4720153,0.003107357,0.5199094,0.00009582513],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9008425,0.042309,0.04298667,0.007877096,0.0002228695,0.001490496,0.00002049989,0.00002680863,0.004224091],"genre_scores_gemma":[0.963833,0.03563306,0.0001368174,0.00009805166,0.00005801883,0.0001081876,0.000007925298,0.000003167707,0.0001217488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6612547,"threshold_uncertainty_score":0.2247844,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4401851940","doi":"10.1016/j.gerr.2024.100089","title":"Exploring the landscape of machine learning-aided research in biofuels and biodiesel: A bibliometric analysis","year":2024,"lang":"en","type":"article","venue":"Green Energy and Resources","topic":"Biodiesel Production and Applications","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"Kara Technologies; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; U.S. Department of Energy","keywords":"Citation; Scopus; China; Bibliometrics; Biofuel; Citation analysis; Web of science; Library science; Political science; Regional science; Geography; Engineering; Computer science; MEDLINE","authors":[{"name":"Avinash Alagumalai","is_ca":true},{"name":"Hua Song","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.08236044892744225,"gpt":0.2781679625718834,"spread":0.1958075136444412,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.0004714895,0.0000710617,0.0001215248,0.01716295,0.0001141515,0.00006025935,0.00007951327,0.00003515966,0.00001455669],"category_scores_gemma":[0.00001599549,0.00004661382,0.00003081186,0.04848703,0.0001316522,0.00007185436,0.00005543079,0.000184785,0.000001496829],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005106908,"about_ca_system_score_gemma":0.000002769796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001331563,"about_ca_topic_score_gemma":0.0004039081,"domain_scores_codex":[0.9993637,0.00006164645,0.0001366037,0.0001660788,0.0001318737,0.0001401231],"domain_scores_gemma":[0.9996299,0.0001817032,0.000009871414,0.0001190636,0.00002370104,0.00003574598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004090575,0.00004827387,0.07302401,0.0006927185,0.001081468,0.00001261715,0.007056938,0.01217378,0.003545123,0.003312435,0.0005772809,0.8984345],"study_design_scores_gemma":[0.00033837,0.0001201098,0.2364949,0.0001198206,0.0001917969,0.00001740034,0.002264319,0.2036398,0.003845403,0.00137943,0.5512167,0.0003718207],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678147,0.03019432,0.00008879762,0.0009020391,0.0000305107,0.00003355537,0.000007681474,0.0000876796,0.000840667],"genre_scores_gemma":[0.9857998,0.0137353,0.00002548166,0.000005459663,0.0000595953,0.00003117596,0.000005250994,0.000008295001,0.0003296792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8980626,"threshold_uncertainty_score":0.9939767,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4417490253","doi":"10.1016/j.gerr.2025.100165","title":"A comprehensive review of hydrogen integrated hybrid renewable energy systems: Configurations, models, simulation and optimization with artificial intelligence","year":2025,"lang":"en","type":"article","venue":"Green Energy and Resources","topic":"Hybrid Renewable Energy Systems","field":"Energy","cited_by":4,"is_retracted":false,"has_abstract":true,"routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false},"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Fonds de recherche du Québec – Nature et technologies; Danish Agency for Science and Higher Education; Wuhan University of Technology; Wuhan University; China Scholarship Council; National Natural Science Foundation of China","keywords":"Renewable energy; Software; Energy carrier; Hybrid system; Optimization problem; Applications of artificial intelligence; Energy storage; Stochastic optimization","authors":[{"name":"Chenglong Li","is_ca":false},{"name":"Tianqi Yang","is_ca":false},{"name":"Wenchao Cai","is_ca":false},{"name":"Kodjo Agbossou","is_ca":true},{"name":"Pierre Bénard","is_ca":true},{"name":"Richard Chahine","is_ca":true},{"name":"Yi Zong","is_ca":false},{"name":"Yaze Li","is_ca":false},{"name":"Shenglin Su","is_ca":false},{"name":"Guodong Li","is_ca":false},{"name":"Xianglin Yan","is_ca":false},{"name":"Jin Li","is_ca":false},{"name":"Jinsheng Xiao","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.02082871282186493,"gpt":0.2369214860972772,"spread":0.2160927732754123,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001941945,0.0003161427,0.0006156072,0.000301187,0.000226289,0.00006457892,0.0001715957,0.0001274193,0.00002153197],"category_scores_gemma":[0.0000357269,0.0002498401,0.00005454854,0.0006000839,0.0002371371,0.0002162691,0.00007089042,0.00007218558,3.012844e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004451883,"about_ca_system_score_gemma":0.00009569064,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08579965,"about_ca_topic_score_gemma":0.002247616,"domain_scores_codex":[0.9978183,0.0003768629,0.0007827633,0.0004882267,0.0002809887,0.0002528888],"domain_scores_gemma":[0.9985278,0.0002266635,0.0003546356,0.0003625646,0.000425136,0.000103231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001022169,0.00003257162,0.00001342366,0.001170919,0.000178498,0.000008288431,0.00007516163,0.9694009,0.0001561174,0.01781725,0.0001094774,0.01093511],"study_design_scores_gemma":[0.0001678519,0.0000883172,0.000002351882,0.003991947,0.0001145359,0.00003013956,0.0004088476,0.9234389,0.002986508,0.001335778,0.06717253,0.0002622969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01003469,0.1482179,0.8304416,0.000359207,0.0002002727,0.000338148,0.0000506575,0.0002137706,0.01014371],"genre_scores_gemma":[0.9827142,0.01331406,0.0006550028,0.0003684378,0.00009430457,0.00007804514,0.0002433934,0.00003843585,0.002494094],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9726796,"threshold_uncertainty_score":0.9999954,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4410076337","doi":"10.1016/j.gerr.2025.100117","title":"Corrigendum to ‘Exploring the landscape of machine learning-aided research in biofuels and biodiesel: A bibliometric analysis’ [Green Energy Res. 2 (2024) 100089]","year":2025,"lang":"en","type":"erratum","venue":"Green Energy and Resources","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"University of Calgary","funders":"","keywords":"Biofuel; Biodiesel; Bioenergy; Computer science; Environmental science; Engineering; Chemistry; Waste management; Organic chemistry","authors":[{"name":"Avinash Alagumalai","is_ca":true},{"name":"Hua Song","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.06940533462003191,"gpt":0.2897588419758485,"spread":0.2203535073558166,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.00217873,0.0003826914,0.000734102,0.01989209,0.0007237458,0.0001492875,0.0006790492,0.0003698296,0.0001258828],"category_scores_gemma":[0.0002485213,0.0002729904,0.0001420066,0.06554654,0.0006682819,0.0001097445,0.001502528,0.0009669224,0.000003583066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008070007,"about_ca_system_score_gemma":0.00003771558,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1990309,"about_ca_topic_score_gemma":0.01951567,"domain_scores_codex":[0.9960452,0.0007686834,0.0006123327,0.0008885752,0.0009857198,0.000699452],"domain_scores_gemma":[0.9984177,0.0005422621,0.0002438073,0.0005226963,0.00005204897,0.0002214417],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003582065,0.0001399422,0.3160751,0.0004352403,0.0008777494,0.00008384959,0.00596159,0.002998447,0.00007261116,0.0001013191,0.0980738,0.5748221],"study_design_scores_gemma":[0.0002479283,0.0003083642,0.0985565,0.0003219633,0.0001670416,0.000004518996,0.0006310404,0.006436545,0.00005688569,0.0002508136,0.8926132,0.0004052618],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"other","genre_scores_codex":[0.8584876,0.05848533,0.0002912622,0.004819164,0.008025972,0.0005726457,0.00030232,0.0002479041,0.06876779],"genre_scores_gemma":[0.4130927,0.0114082,0.0001319897,0.0001658383,0.0008467182,0.0001172378,0.0001342542,0.00006234739,0.5740407],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7945393,"threshold_uncertainty_score":0.9999722,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null},{"id":"W4414111301","doi":"10.1016/j.gerr.2025.100144","title":"Editorial: AI-driven green revolution","year":2025,"lang":"en","type":"article","venue":"Green Energy and Resources","topic":"Smart Cities and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false},"ca_institutions":"Western University","funders":"","keywords":"","authors":[{"name":"Tianlong Liu","is_ca":true},{"name":"Ying Zheng","is_ca":true}],"retraction":null,"screen_n_in":null,"score":{"opus":0.00398430227559051,"gpt":0.1821209590433912,"spread":0.1781366567678007,"validation_status":"score_only:v0-immature-baseline"},"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003525424,0.0000968708,0.0001124983,0.0001107652,0.00009024492,0.00001956552,0.0001117191,0.0001669644,0.000007312816],"category_scores_gemma":[0.000007727586,0.00008381754,0.00002994866,0.0001440342,0.00007832618,0.00005404958,0.00006672093,0.00009047017,0.000001773195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001776487,"about_ca_system_score_gemma":0.000004280229,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001492643,"about_ca_topic_score_gemma":0.0004668609,"domain_scores_codex":[0.9995508,0.000009600086,0.0001006329,0.0001149125,0.00007441444,0.0001496933],"domain_scores_gemma":[0.9997814,0.00002690735,0.00001149745,0.000139495,0.0000184525,0.00002219821],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002423514,0.00001248321,0.004387531,0.0001467406,0.0001695871,0.000005414354,0.0004032232,0.002686215,0.0007225716,0.02251247,0.8681547,0.1007748],"study_design_scores_gemma":[0.0001341852,0.00001713118,0.001392037,0.00003145185,0.00001282661,0.000001162302,0.0001088294,0.002370324,0.0003969977,0.003972682,0.9914611,0.000101298],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6897449,0.03866592,0.01300322,0.01781237,0.1247401,0.0003636666,0.00007115294,0.00935105,0.1062476],"genre_scores_gemma":[0.9760943,0.001351138,0.0001778752,0.0003293787,0.01652041,0.0000273787,0.00001048605,0.00002431406,0.005464746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2863493,"threshold_uncertainty_score":0.341798,"prediction_status":"machine_predicted_unvalidated"},"labels":[],"label_agreement":null}]}