{"id":"W2645423467","doi":"","title":"Is it possible to reduce greenhouse gas emissions without reducing production? An assessment of 26 technical options","year":2015,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Agriculture Sustainability and Environmental Impact","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Greenhouse gas; Production (economics); Environmental science; Agriculture; Biomass (ecology); Environmental economics; Marginal abatement cost; Climate change; Biogas; Natural resource economics; Agricultural engineering; Environmental engineering; Economics; Waste management; Engineering; Ecology","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":[0.005412686,0.001865948,0.0006507436,0.005415554,0.0006630882,0.003017927,0.0009008105,0.001430563,0.002230481],"category_scores_gemma":[0.004424627,0.0003277661,0.002258887,0.002906388,0.0009518611,0.001628631,0.0009939114,0.0004156941,0.0002928891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00312059,"about_ca_system_score_gemma":0.001578884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002073006,"about_ca_topic_score_gemma":0.003392215,"domain_scores_codex":[0.9956584,0.001567782,0.0002306988,0.0002770503,0.001962718,0.0003033618],"domain_scores_gemma":[0.9959934,0.002459065,0.000686888,0.0001194324,0.0005120564,0.0002291347],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003500226,0.002240113,0.1035754,0.006727709,0.001582184,0.001340797,0.0007070668,0.2248645,0.01727616,0.02729848,0.001213731,0.6096737],"study_design_scores_gemma":[0.001162294,0.05872718,0.5834696,0.005059971,0.004293058,0.002014045,0.008685503,0.1748448,0.03922881,0.03034123,0.09162046,0.0005530731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.945831,0.01169706,0.006119309,0.0004504041,0.00002215887,0.0004657441,0.0003767061,0.00005021583,0.03498748],"genre_scores_gemma":[0.98438,0.005295011,0.008097396,0.00005785292,0.00001341113,0.0002128847,0.0002847129,0.000008272861,0.001650474],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005415554,"threshold_uncertainty_score":0.02862537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02565224999122585,"score_gpt":0.3116048953982814,"score_spread":0.2859526454070556,"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."}}