{"id":"W4406747467","doi":"10.1016/j.apenergy.2025.125373","title":"Quantifying spatio-temporal carbon intensity within a city using large-scale smart meter data: Unveiling the impact of behind-the-meter generation","year":2025,"lang":"en","type":"article","venue":"Applied Energy","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Science and Technology Agency; Council for Science, Technology and Innovation; Swine Innovation Porc","keywords":"Metre; Intensity (physics); Scale (ratio); Environmental science; Smart meter; Carbon fibers; Engineering; Computer science; Electrical engineering; Geography; Cartography; Smart grid; Physics; Optics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006959027,0.0002681881,0.0003210434,0.0001501402,0.0001780084,0.00007687721,0.0004960608,0.0001008962,0.00001270675],"category_scores_gemma":[0.00001805469,0.000185946,0.0001051974,0.0003716179,0.0000631595,0.0001303406,0.0004175747,0.0002068654,8.606225e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001726246,"about_ca_system_score_gemma":0.00004393285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005081375,"about_ca_topic_score_gemma":0.00661024,"domain_scores_codex":[0.9985492,0.00005632063,0.0004662365,0.0003595593,0.0002358662,0.000332875],"domain_scores_gemma":[0.9986076,0.00006865427,0.0001145698,0.001111177,0.00005841745,0.00003960809],"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":[0.00003033411,0.00003273649,0.004138371,0.00002475525,0.0004076326,0.00000149296,0.0003918576,0.9726758,0.01940565,0.001280583,0.001277438,0.0003333508],"study_design_scores_gemma":[0.0002768504,0.00001105204,0.00151342,0.00002034754,0.000117126,0.000001375687,0.0001318563,0.9585035,0.0378698,0.0001175309,0.001245934,0.0001912261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8806623,0.0001214844,0.1168088,0.00006836673,0.0008145905,0.0001530914,0.00001379751,0.000128012,0.001229507],"genre_scores_gemma":[0.9968264,0.000014203,0.002418785,0.0001779657,0.0002476895,0.0000288067,0.000199892,0.0000395994,0.00004672924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.116164,"threshold_uncertainty_score":0.7681553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05359290881789199,"score_gpt":0.276956416408029,"score_spread":0.223363507590137,"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."}}