{"id":"W2769774644","doi":"10.3390/rs9111193","title":"Developing a Random Forest Algorithm for MODIS Global Burned Area Classification","year":2017,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidad de Alcalá; European Space Agency","keywords":"Random forest; Remote sensing; Environmental science; Boreal; Taiga; Bidirectional reflectance distribution function; Pixel; Vegetation (pathology); Temperate rainforest; Algorithm; Computer science; Forestry; Geography; Reflectivity; Artificial intelligence; Ecosystem; Ecology; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002105264,0.001095954,0.001148293,0.001966521,0.000450119,0.0006500309,0.00131732,0.0007125893,0.001326402],"category_scores_gemma":[0.002880613,0.0004666983,0.001290951,0.00146065,0.0002578151,0.001214888,0.0005487347,0.0008105109,0.00139705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004778673,"about_ca_system_score_gemma":0.0008381615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007183534,"about_ca_topic_score_gemma":0.007220479,"domain_scores_codex":[0.9990181,0.0002054462,0.00006894528,0.0002952101,0.0003021481,0.0001102236],"domain_scores_gemma":[0.9990869,0.0003135446,0.00008439857,0.00008088497,0.0004094097,0.00002479487],"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":[0.0001095989,0.00009236292,0.003571085,0.0001100889,0.0001201393,0.00009747154,0.00006211725,0.2924471,0.00659578,0.002469173,0.003965004,0.69036],"study_design_scores_gemma":[0.00001375324,0.00003531989,0.0008789971,0.00001682116,0.0000155927,0.00006994766,0.00001515954,0.9923079,0.0024139,0.00212205,0.00209482,0.00001565761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006632598,0.0001639879,0.9913012,0.00002141274,0.00001982424,0.00006649836,0.0001226082,0.001246869,0.0004249034],"genre_scores_gemma":[0.06735265,0.0001864093,0.929872,0.00004768627,0.00003656833,0.0002056181,0.00108429,0.0002470151,0.0009677334],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007183534,"threshold_uncertainty_score":0.01428342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03457994931679056,"score_gpt":0.2777452618132995,"score_spread":0.2431653124965089,"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."}}