{"id":"W4396827656","doi":"10.2172/2348933","title":"Development of R&D GREET 2023 Rev1 to Estimate Greenhouse Gas Emissions of Sustainable Aviation Fuels for 40B Provision of the Inflation Reduction Act","year":2024,"lang":"en","type":"report","venue":"","topic":"Advanced Aircraft Design and Technologies","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cascades (Canada)","funders":"U.S. Department of Energy; Office of Energy Efficiency and Renewable Energy; University of Chicago; Argonne National Laboratory; Office of Energy Efficiency; Purdue University; U.S. Department of Agriculture","keywords":"Greenhouse gas; Aviation; Waste management; Environmental science; Natural resource economics; Sustainable development; Reduction (mathematics); Business; Environmental economics; Engineering; Economics; Political science; Law; Aerospace engineering","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":[0.0006621794,0.0002108528,0.0003357079,0.0001778691,0.0001062993,0.000009547975,0.000291758,0.000252298,0.0001047318],"category_scores_gemma":[0.0006872155,0.0001435349,0.0001202808,0.0006375373,0.0001125224,0.0001778143,0.0004322139,0.0001525912,0.00001023599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006341062,"about_ca_system_score_gemma":0.0003431944,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002267218,"about_ca_topic_score_gemma":0.0001658552,"domain_scores_codex":[0.9978937,0.00002069671,0.0007838624,0.0003773262,0.0006935182,0.0002309209],"domain_scores_gemma":[0.9986268,0.00004837624,0.0006532895,0.0004722496,0.0001582883,0.00004101731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001455679,0.0002658364,0.001832858,0.003766441,0.0001082877,0.000002530763,0.001577242,0.006129209,0.6464905,0.001714594,0.02556602,0.3124009],"study_design_scores_gemma":[0.0003414829,0.0003528606,0.008268086,0.001721919,0.0002101517,0.00001872244,0.001262198,0.0002618975,0.7760687,0.03304304,0.1778025,0.0006483893],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7126024,0.0004215033,0.236838,0.001562991,0.001277047,0.01258074,0.0001414644,0.0006281634,0.03394761],"genre_scores_gemma":[0.6990921,0.0002020033,0.2208893,0.000006344214,0.00004168496,0.0003921042,0.0001289822,0.00008312584,0.07916438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3117526,"threshold_uncertainty_score":0.5853185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03189249509617711,"score_gpt":0.321571752443184,"score_spread":0.2896792573470069,"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."}}