{"id":"W2329883071","doi":"10.1007/s00477-016-1244-4","title":"Adaptation strategies for mitigating agricultural GHG emissions under dual-level uncertainties with the consideration of global warming impacts","year":2016,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Greenhouse gas; Agriculture; Agricultural productivity; Environmental science; Life-cycle assessment; Irrigation; Agricultural engineering; Business; Climate change; Natural resource economics; Food security; Farm water; Global warming; Production (economics); Agricultural economics; Environmental resource management; Economics; Water conservation; Engineering; Geography; Agronomy","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":[0.001093909,0.000860077,0.0006519029,0.0005599417,0.0003662106,0.001168978,0.001375687,0.001208399,0.002952076],"category_scores_gemma":[0.003141731,0.0003430195,0.0007898547,0.0005973427,0.0006291152,0.001566183,0.00195031,0.0009645494,0.0001897104],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007492051,"about_ca_system_score_gemma":0.00105256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003588856,"about_ca_topic_score_gemma":0.00349572,"domain_scores_codex":[0.9995826,0.0001538216,0.00001894357,0.00009971107,0.00006135053,0.00008362097],"domain_scores_gemma":[0.9994032,0.0002989511,0.0001004428,0.0000489004,0.0001098255,0.0000386204],"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":[0.00006179185,0.00005451655,0.001657932,0.00006505076,0.00013751,0.0001484748,0.00008839751,0.9496739,0.003572456,0.02013016,0.0005950151,0.02381478],"study_design_scores_gemma":[0.00001261064,0.00004511245,0.001129455,0.0000146809,0.00005599241,0.00003633041,0.0001329009,0.9719586,0.0005867453,0.0250362,0.0009711416,0.00002016457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1962034,0.001157071,0.7836991,0.002229812,0.0002231344,0.0001363335,0.0001931008,0.0002276442,0.01593038],"genre_scores_gemma":[0.97747,0.0003382965,0.02013431,0.0001286339,0.00004247031,0.00007638403,0.00005626503,0.00002585368,0.0017277],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003588856,"threshold_uncertainty_score":0.009875655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03952808739707051,"score_gpt":0.3415527093651577,"score_spread":0.3020246219680872,"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."}}