{"id":"W2056570528","doi":"10.1109/rsete.2012.6260614","title":"Land-Use Multicritera Evaluation Involving Carbon Sequestration Benefits Based on GIS and RS","year":2012,"lang":"en","type":"article","venue":"","topic":"Soil and Land Suitability Analysis","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Analytic hierarchy process; Carbon sequestration; Land use; Status quo; Environmental resource management; Geographic information system; Multiple-criteria decision analysis; Environmental science; Computer science; Forestry; Remote sensing; Geography; Operations research; Engineering; Ecology; Civil 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":[0.0005780408,0.00007216869,0.00006796731,0.00002696077,0.00006717134,0.00004022991,0.0000317953,0.00004235573,0.0007053075],"category_scores_gemma":[0.0001102307,0.00005491973,0.00002244929,0.00009770282,0.0000299939,0.0003122532,0.00001842308,0.00004524761,0.00002318098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000825957,"about_ca_system_score_gemma":0.000003791979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003790589,"about_ca_topic_score_gemma":0.001353852,"domain_scores_codex":[0.9992347,0.00009044618,0.0001029529,0.0001507916,0.0002785252,0.0001425794],"domain_scores_gemma":[0.9996603,0.00009727043,0.00002260509,0.0001325465,0.000008803268,0.00007847813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000006695836,0.0000534291,0.9834091,0.00000243812,0.00000278444,9.508136e-8,0.0003003559,0.005780312,0.0005041587,0.00001992578,0.00001462518,0.009906045],"study_design_scores_gemma":[0.000148437,0.00001898877,0.6052557,0.000004081332,0.000024649,2.128604e-7,0.00003307447,0.3939706,0.0004167632,0.00002863084,0.00003743664,0.00006139564],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956857,0.00003995842,0.00002898457,0.000152293,0.00003596343,0.0001013422,0.000001592406,0.00002194463,0.003932237],"genre_scores_gemma":[0.9992375,0.00000711493,0.000353719,0.0002696094,0.00002697222,0.00001136588,0.00001862038,0.000003819703,0.00007127948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3881903,"threshold_uncertainty_score":0.7722622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03752919897625654,"score_gpt":0.2556023849198575,"score_spread":0.218073185943601,"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."}}