{"id":"W3047042976","doi":"10.1071/wf19201","title":"Generating annual estimates of forest fire disturbance in Canada: the National Burned Area Composite","year":2020,"lang":"en","type":"article","venue":"International Journal of Wildland Fire","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"Canadian Forest Service; Natural Resources Canada; Canadian Space Agency; U.S. Forest Service","keywords":"Polygon (computer graphics); Environmental science; Fire regime; Remote sensing; Forest inventory; Physical geography; Geography; Disturbance (geology); Cartography; Meteorology; Forest management; Forestry; Ecosystem; Computer science; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0009067605,0.0004020103,0.0002246555,0.004906947,0.001006171,0.0008797407,0.0006400049,0.0001791128,0.00146407],"category_scores_gemma":[0.002571316,0.000237004,0.0003710332,0.005093897,0.0001738766,0.0004038186,0.0004736229,0.0003949866,0.0003336616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01490258,"about_ca_system_score_gemma":0.01389264,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9902085,"about_ca_topic_score_gemma":0.994917,"domain_scores_codex":[0.9995198,0.0000263854,0.00003774121,0.00007340268,0.0002690185,0.00007355706],"domain_scores_gemma":[0.9973718,0.0001318649,0.0002633635,0.00007995433,0.001967454,0.0001855462],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001137756,0.00006209433,0.8906131,0.0001620372,0.0002395693,0.00007970873,0.0005839471,0.01067551,0.0005867315,0.001052135,0.01884754,0.07698399],"study_design_scores_gemma":[0.000007007816,0.00001038184,0.9781439,0.0000587836,0.0000356767,0.00003476445,0.0004049457,0.009179679,0.0003978749,0.0001962773,0.0115089,0.0000217869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7895792,0.00291537,0.01603727,0.0003892108,0.00006172267,0.0003043432,0.1675446,0.0007234113,0.02244491],"genre_scores_gemma":[0.8897464,0.00162201,0.02171852,0.0001028397,0.0000182669,0.0001947838,0.08063659,0.0001004747,0.005860176],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01490258,"threshold_uncertainty_score":0.1081263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009095534021153843,"score_gpt":0.2147359075173246,"score_spread":0.2056403734961708,"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."}}