{"id":"W4401894700","doi":"10.3390/f15091493","title":"Predicting Forest Fire Area Growth Rate Using an Ensemble Algorithm","year":2024,"lang":"en","type":"article","venue":"Forests","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Terrain; Random forest; Hyperparameter; Collinearity; Computer science; Algorithm; Environmental science; Statistics; Geography; Mathematics; Cartography; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004493323,0.0001917863,0.0001538755,0.00004158663,0.0001711147,0.0001601153,0.0002305505,0.00009241004,0.0002523882],"category_scores_gemma":[0.00006001867,0.0001708324,0.000064268,0.000322008,0.00006808341,0.0007167276,0.0001456406,0.0001632049,0.0006094258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00023447,"about_ca_system_score_gemma":0.00001872439,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003855553,"about_ca_topic_score_gemma":0.008469342,"domain_scores_codex":[0.9985339,0.00008585752,0.0002108484,0.000463914,0.000278746,0.0004267395],"domain_scores_gemma":[0.9993903,0.0001082999,0.00004756794,0.0002806583,0.000007312838,0.0001658345],"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":[0.000009708868,0.00008625819,0.9413869,0.0001336486,0.00003254601,0.0003961061,0.000723825,0.004199984,0.004838575,0.00009088164,0.00251723,0.04558436],"study_design_scores_gemma":[0.0001023615,0.0001053533,0.1443437,0.0001299448,0.00001625798,0.00007305861,0.00001499201,0.8526599,0.000816222,0.0007267247,0.0008252579,0.00018612],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915167,0.0001112605,0.005648017,0.0000409809,0.0008101363,0.0003109245,0.00002004209,0.000312478,0.001229479],"genre_scores_gemma":[0.9979585,0.00000291596,0.001365377,0.00003543851,0.0002377823,0.00002374955,0.00002018065,0.0000520998,0.0003039952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.84846,"threshold_uncertainty_score":0.7833139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348768201559407,"score_gpt":0.2338670824462029,"score_spread":0.2203794004306088,"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."}}