{"id":"W3121103211","doi":"10.5194/esd-12-635-2021","title":"Modelled land use and land cover change emissions – a spatio-temporal comparison of different approaches","year":2021,"lang":"en","type":"article","venue":"Earth System Dynamics","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Oak Ridge National Laboratory; Horizon 2020 Framework Programme; National Natural Science Foundation of China; Met Office; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; Agence Nationale de la Recherche; National Center for Atmospheric Research; National Science Foundation","keywords":"Environmental science; Forcing (mathematics); Bookkeeping; Land use, land-use change and forestry; Land cover; Sink (geography); Climate change; Carbon sink; Land use; Vegetation (pathology); Atmospheric sciences; Global change; Carbon flux; Greenhouse gas; Carbon cycle; Climatology; Ecosystem; Ecology; Biology; Accounting; Geography; Geology","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":[0.0008679215,0.0006109908,0.0004356212,0.0008691628,0.0002298913,0.0008059768,0.0009521206,0.0008825248,0.001519444],"category_scores_gemma":[0.001401783,0.000274496,0.0009773537,0.001134195,0.0002789722,0.0006367915,0.0003493938,0.0004381164,0.0002548669],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009316901,"about_ca_system_score_gemma":0.000369027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02620888,"about_ca_topic_score_gemma":0.02220028,"domain_scores_codex":[0.9997877,0.00006030172,0.00001256333,0.00008056497,0.00003056446,0.00002839282],"domain_scores_gemma":[0.9994798,0.0002354861,0.00006333419,0.00009307436,0.00009339117,0.00003481293],"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":[0.0003075463,0.0001874455,0.06435239,0.0001863762,0.0004743537,0.0001246026,0.00009564465,0.9174325,0.002958779,0.001104567,0.0006316275,0.01214412],"study_design_scores_gemma":[0.00006292708,0.00007897936,0.05640576,0.000041087,0.0001215058,0.000054138,0.0001159415,0.9385341,0.002408321,0.0007583241,0.001376022,0.00004280623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9865252,0.0004915395,0.006345741,0.0001235994,0.00002488604,0.00003171843,0.003290454,0.0002945899,0.002872376],"genre_scores_gemma":[0.9933156,0.0001744427,0.003103485,0.00002186032,0.000008913465,0.00003306452,0.003036839,0.00004120719,0.0002646124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02620888,"threshold_uncertainty_score":0.05211264,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04111187986867812,"score_gpt":0.2167260972539542,"score_spread":0.1756142173852761,"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."}}