{"id":"W4411162860","doi":"10.1016/j.egyr.2025.05.062","title":"Generative AI applied for synthetic data in PMU","year":2025,"lang":"en","type":"article","venue":"Energy Reports","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Total; Agência Nacional do Petróleo, Gás Natural e Biocombustíveis; Fundação de Amparo à Pesquisa do Estado de São Paulo; Research Centre for Gas Innovation","keywords":"Computer science; Generative grammar; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001286317,0.0006732569,0.000483672,0.00039833,0.0002130949,0.0005923943,0.0008504263,0.0006645407,0.001204408],"category_scores_gemma":[0.005357753,0.0003629337,0.0006451784,0.0005497091,0.0007663172,0.0006564082,0.0008505678,0.001391367,0.0002401714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005907161,"about_ca_system_score_gemma":0.0004105328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004224216,"about_ca_topic_score_gemma":0.004153048,"domain_scores_codex":[0.9994839,0.0002124006,0.00002388215,0.0001319537,0.0001103039,0.00003748193],"domain_scores_gemma":[0.9976081,0.001725047,0.0001372861,0.0003061546,0.0001784979,0.0000449954],"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":[0.00003563574,0.00001580245,0.0008469915,0.0000342617,0.00002038922,0.00005600891,0.00003313488,0.978942,0.0009186429,0.00325562,0.0004951854,0.01534635],"study_design_scores_gemma":[0.000001510971,0.00000629755,0.0001252187,0.000002144819,0.000001323888,0.00001118108,0.000002952283,0.9979665,0.0004558623,0.001269634,0.0001551122,0.000002235764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09322836,0.0004334971,0.9014859,0.0003946497,0.00009588051,0.00006859653,0.0004109616,0.001342898,0.002539246],"genre_scores_gemma":[0.9252306,0.0001484178,0.07218867,0.0001390432,0.0000319899,0.00008114734,0.0007507977,0.00009758113,0.001331705],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004224216,"threshold_uncertainty_score":0.008399248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01561321894090367,"score_gpt":0.2638015007875121,"score_spread":0.2481882818466085,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}