{"id":"W2135800237","doi":"10.1088/1748-9326/8/3/034009","title":"REDD+ emissions estimation and reporting: dealing with uncertainty","year":2013,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Forest ecology and management","field":"Environmental Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Greenhouse gas; United Nations Framework Convention on Climate Change; Environmental science; Reducing emissions from deforestation and forest degradation; Uncertainty analysis; Comparability; Deforestation (computer science); Consistency (knowledge bases); Propagation of uncertainty; Climate change; Baseline (sea); Econometrics; Environmental resource management; Carbon stock; Computer science; Kyoto Protocol; Economics; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007787688,0.0001071092,0.00009811448,0.00004879527,0.0003955387,0.00004561012,0.0001309359,0.00003986043,0.004092758],"category_scores_gemma":[0.0001177478,0.00008425771,0.00001885146,0.00008535111,0.0007403388,0.0002637383,0.0003436258,0.000244425,0.0008995468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000231067,"about_ca_system_score_gemma":0.000002891595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003867509,"about_ca_topic_score_gemma":0.00002498407,"domain_scores_codex":[0.9984161,0.00008116609,0.000263057,0.0003578879,0.0004731522,0.000408648],"domain_scores_gemma":[0.9993712,0.00008563021,0.0001312929,0.0002379211,0.000001380801,0.0001725965],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001023842,0.0003403779,0.6192657,0.00003664955,0.00008432047,0.0002998088,0.001281435,0.08451314,0.1753344,0.001353241,0.04832036,0.0690681],"study_design_scores_gemma":[0.0005611765,0.0002659119,0.9583707,0.00003297729,0.0000138188,0.00004890707,0.0006811598,0.02617723,0.001921817,0.00387224,0.007719824,0.0003342715],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903405,0.00001550803,0.0009031162,0.005043806,0.000017464,0.0004844924,8.943635e-7,0.00002466945,0.003169544],"genre_scores_gemma":[0.994903,0.00002523867,0.003478194,0.0005928685,0.00001157563,0.0001098487,0.0000114295,0.00001332653,0.0008545549],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3391049,"threshold_uncertainty_score":0.9998783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02905622097267992,"score_gpt":0.296600179159728,"score_spread":0.2675439581870481,"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."}}