{"id":"W4402263850","doi":"10.1109/imsa61967.2024.10652803","title":"Focus on Carbon Dioxide Footprint of AI/ML Model Training","year":2024,"lang":"en","type":"article","venue":"","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Ericsson (Canada)","funders":"","keywords":"Carbon footprint; Focus (optics); Carbon dioxide; Footprint; Computer science; Training (meteorology); Artificial intelligence; Chemistry; Meteorology; Greenhouse gas; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003594067,0.0001368727,0.0001690997,0.000183396,0.00003849374,0.000104726,0.0006551037,0.000057758,0.000007932672],"category_scores_gemma":[0.00005012182,0.0001177679,0.0000923909,0.0004439667,0.00004369022,0.0001881275,0.0001827353,0.0002022955,0.00004401592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006488332,"about_ca_system_score_gemma":0.0001601762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003143757,"about_ca_topic_score_gemma":0.0000991523,"domain_scores_codex":[0.9985867,0.00002985218,0.0002965744,0.000438283,0.0003268764,0.0003217342],"domain_scores_gemma":[0.9991412,0.0001410354,0.00002945988,0.0005424034,0.00006099838,0.00008496845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003506779,0.00002399159,0.000006850951,0.00001503221,0.00001117962,0.00002553301,0.002559379,0.02139282,0.0151988,0.8493378,0.00003457725,0.1113906],"study_design_scores_gemma":[0.000014843,0.0000757723,0.000005456858,0.00004016237,0.000002212675,0.000002767056,0.0001020578,0.5836633,0.2966911,0.1191794,0.0001337679,0.00008912505],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04213897,0.00007753949,0.834923,0.002790714,0.0003021396,0.0001224608,7.135058e-7,0.00037882,0.1192657],"genre_scores_gemma":[0.9829654,0.000007731715,0.01596007,0.0001976207,0.00004212255,0.00001354041,1.526136e-7,0.00001390367,0.0007994471],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9408264,"threshold_uncertainty_score":0.4802437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06144673773181063,"score_gpt":0.2987434715150127,"score_spread":0.237296733783202,"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."}}