{"id":"W4408723695","doi":"10.1021/acs.est.4c12667","title":"Emission Factor Recommendation for Life Cycle Assessments with Generative AI","year":2025,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Factor (programming language); Generative grammar; Life-cycle assessment; Environmental science; Computer science; Artificial intelligence; Economics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000336643,0.0002654493,0.0002182086,0.0002454469,0.0008248229,0.00006177934,0.0005779379,0.0001600694,0.001879397],"category_scores_gemma":[0.00007979971,0.000217681,0.00005160885,0.0009463061,0.002455452,0.0007846403,0.000627643,0.0002377872,0.00007478411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001909914,"about_ca_system_score_gemma":0.00006688764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002920052,"about_ca_topic_score_gemma":0.00001468193,"domain_scores_codex":[0.997823,0.00003611896,0.0002798323,0.0008385651,0.0003651356,0.0006573416],"domain_scores_gemma":[0.9991999,0.00003515233,0.0001158028,0.0004496372,0.000004458597,0.0001950488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000132372,0.0006802506,0.5775084,0.00001138537,0.00002554867,0.000002976376,0.0002225426,0.0008966677,0.2920088,0.000443081,0.0009599411,0.127108],"study_design_scores_gemma":[0.002512768,0.001605176,0.5583122,0.00003047812,0.0000577547,0.00001375846,0.003267611,0.01127574,0.3500892,0.009911182,0.06193366,0.0009905061],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9673006,0.00001767193,0.02286613,0.004402827,0.000121211,0.000950689,0.00003293927,0.0001093747,0.004198571],"genre_scores_gemma":[0.9919259,0.00001641658,0.005145518,0.001271331,0.0000114399,0.0001764189,0.00002286938,0.00001529655,0.001414796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1261175,"threshold_uncertainty_score":0.999033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00711622386209644,"score_gpt":0.296181625688844,"score_spread":0.2890654018267476,"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."}}