{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002411833,0.001552663,0.0008800041,0.0006087332,0.0004274128,0.002026266,0.001443643,0.001459065,0.004040169],"category_scores_gemma":[0.01506131,0.0003655679,0.0005131261,0.0006607752,0.0007584857,0.003713996,0.001087447,0.002014604,0.001002157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001277833,"about_ca_system_score_gemma":0.001131582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01078511,"about_ca_topic_score_gemma":0.01195533,"domain_scores_codex":[0.9988011,0.0003085267,0.00008399717,0.0003095412,0.0003415119,0.0001554357],"domain_scores_gemma":[0.991884,0.005653962,0.0003085758,0.0008789888,0.001156628,0.0001177673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0008800753,0.0002915126,0.02288893,0.0009811267,0.0003287447,0.0004254492,0.0002655096,0.6108264,0.04233801,0.005044323,0.004984568,0.3107453],"study_design_scores_gemma":[0.00002005628,0.0001676261,0.004472842,0.0001351823,0.00007945434,0.0001077034,0.0001252854,0.9350287,0.05289649,0.003700602,0.003230506,0.00003558606],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6498249,0.006367154,0.3028591,0.004623813,0.0006152262,0.000208851,0.001330279,0.007357597,0.02681308],"genre_scores_gemma":[0.947221,0.0006901461,0.0476031,0.0004461799,0.00005383197,0.00007360168,0.0009106763,0.0005158953,0.002485538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01078511,"threshold_uncertainty_score":0.02144468,"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."}}