{"id":"W4408438947","doi":"10.5194/egusphere-egu25-4965","title":"Tracing the life cycle carbon footprint of global staple crops: an integrated approach combining machine learning and life cycle assessment","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Tracing; Carbon footprint; Life-cycle assessment; Computer science; Footprint; Agricultural engineering; Engineering; Geography; Economics; Biology; Production (economics); Greenhouse gas","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007277064,0.0006414448,0.0003564291,0.001351324,0.000193997,0.0007640247,0.0004323952,0.0006174875,0.0005575563],"category_scores_gemma":[0.001240732,0.0002322185,0.0006912592,0.001580104,0.000248878,0.001189737,0.0005074593,0.0004538171,0.00008767472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001228947,"about_ca_system_score_gemma":0.001028214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02226829,"about_ca_topic_score_gemma":0.02296968,"domain_scores_codex":[0.9998265,0.00006384258,0.000008390257,0.00004796327,0.00003465544,0.0000187507],"domain_scores_gemma":[0.9994998,0.000240886,0.00009804573,0.00004914052,0.00008614006,0.00002601271],"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":[0.00002869136,0.00004902206,0.01931227,0.00004880728,0.00008858988,0.00003671789,0.00002313905,0.9477869,0.001074031,0.00163279,0.0002319806,0.02968708],"study_design_scores_gemma":[0.000001922846,0.00001320096,0.003130342,0.000005703755,0.00001190498,0.000005596812,0.00001771812,0.9945949,0.0003496342,0.001520772,0.0003420935,0.0000062228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7770876,0.001379912,0.2118974,0.0007184452,0.00003997095,0.00009675972,0.00210085,0.0003742452,0.006304802],"genre_scores_gemma":[0.9563844,0.0003883957,0.04179437,0.00005219963,0.00001451911,0.0000467628,0.0007886668,0.0000273401,0.0005033793],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.02226829,"threshold_uncertainty_score":0.04427737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01188096912671498,"score_gpt":0.2806302844036404,"score_spread":0.2687493152769254,"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."}}