{"id":"W2507962883","doi":"10.1007/978-3-319-29794-1_3","title":"Determining Greenhouse Gas Emissions and Removals Associated with Land-Use and Land-Cover Change","year":2016,"lang":"en","type":"book-chapter","venue":"","topic":"Land Use and Ecosystem Services","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Greenhouse gas; Land use; Context (archaeology); Land cover; Agriculture; Environmental science; Land use, land-use change and forestry; Climate change; Agricultural land; Natural resource economics; Environmental resource management; Geography; Engineering; Economics; Civil 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":[0.002115207,0.0009654233,0.0007538993,0.002882417,0.000456345,0.001399014,0.00103235,0.0007022555,0.001766477],"category_scores_gemma":[0.00487732,0.0003488441,0.001268415,0.003376229,0.0004077243,0.001521675,0.0007810065,0.0005852638,0.0009293433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009849379,"about_ca_system_score_gemma":0.0007084112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007775782,"about_ca_topic_score_gemma":0.01732896,"domain_scores_codex":[0.9982998,0.0002807447,0.0001377684,0.000404109,0.0008035302,0.00007405429],"domain_scores_gemma":[0.9984787,0.0007654192,0.0003386276,0.0001524204,0.0002430417,0.00002173825],"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":[0.000155051,0.0001637226,0.2483427,0.003928839,0.0007621293,0.000345833,0.0007691219,0.04567493,0.05109027,0.0114085,0.009834274,0.6275246],"study_design_scores_gemma":[0.00003363793,0.0004201504,0.4725972,0.001677305,0.0006881118,0.00132567,0.001423738,0.08179683,0.1700884,0.02482932,0.2447105,0.0004091355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2019882,0.04040452,0.6801104,0.001268891,0.0005673412,0.001601456,0.03641013,0.00177395,0.03587527],"genre_scores_gemma":[0.3590345,0.05396035,0.551351,0.0006597714,0.0003239131,0.002505599,0.02316791,0.0006781689,0.008318841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007775782,"threshold_uncertainty_score":0.01546103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03081597953717826,"score_gpt":0.2109341042949668,"score_spread":0.1801181247577885,"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."}}