{"id":"W4401842621","doi":"10.1029/2023jd040514","title":"Forecasting Daily Fire Radiative Energy Using Data Driven Methods and Machine Learning Techniques","year":2024,"lang":"en","type":"article","venue":"Journal of Geophysical Research Atmospheres","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nuclear Safety and Security Commission; National Aeronautics and Space Administration","keywords":"Random forest; Environmental science; Meteorology; Variance (accounting); Radiative transfer; Atmospheric sciences; Statistics; Computer science; Geography; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001538828,0.0007004261,0.000490769,0.000864175,0.0002567302,0.0007427991,0.0006110269,0.0006644318,0.0005254215],"category_scores_gemma":[0.003725586,0.0004696397,0.0008173255,0.0006666271,0.0002037131,0.0006327323,0.0002524952,0.0009827324,0.0001500898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001066149,"about_ca_system_score_gemma":0.0009040276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02636956,"about_ca_topic_score_gemma":0.02452946,"domain_scores_codex":[0.9997191,0.0001151715,0.00002349301,0.00006428206,0.00005588279,0.00002205639],"domain_scores_gemma":[0.9980015,0.001456298,0.0001523758,0.00009727735,0.000255626,0.00003682811],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002416698,0.00005959776,0.004928519,0.00001608211,0.00004935949,0.00001308684,0.00000868134,0.9813959,0.0004296561,0.0002831758,0.0002130507,0.01257874],"study_design_scores_gemma":[0.000001395534,0.000003717628,0.0005043357,0.000001346048,0.000001420792,9.861149e-7,0.000001489709,0.9991646,0.0001304022,0.0001550758,0.00003325665,0.000002102959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7501091,0.0004034142,0.2440592,0.0005180589,0.0001073564,0.0001160636,0.001367876,0.0008870806,0.00243183],"genre_scores_gemma":[0.9622068,0.00008464471,0.03634473,0.00003534518,0.00002396744,0.000061571,0.0007918354,0.00001777942,0.0004334003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02636956,"threshold_uncertainty_score":0.05243218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08008670427820837,"score_gpt":0.3865273709236215,"score_spread":0.3064406666454132,"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."}}