{"id":"W3089801253","doi":"10.1088/1748-9326/abbc3b","title":"Modeling cloud-to-ground lightning probability in Alaskan tundra through the integration of Weather Research and Forecast (WRF) model and machine learning method","year":2020,"lang":"en","type":"article","venue":"Environmental Research Letters","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Aeronautics and Space Administration","keywords":"Weather Research and Forecasting Model; Tundra; Environmental science; Meteorology; Lightning (connector); Climatology; Atmospheric sciences; Numerical weather prediction; Arctic; Geography; Geology","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":[0.0005137745,0.0004529876,0.000352725,0.0005097727,0.000386151,0.0005053407,0.000767201,0.0006740466,0.0005714213],"category_scores_gemma":[0.001280023,0.0002767673,0.0005310192,0.0003548487,0.0002585479,0.0004634587,0.000321671,0.0004753207,0.0000570722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009764318,"about_ca_system_score_gemma":0.00109776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1308108,"about_ca_topic_score_gemma":0.0491443,"domain_scores_codex":[0.9998488,0.00003184671,0.00001538321,0.00004962579,0.00002260062,0.00003167067],"domain_scores_gemma":[0.9995824,0.00018428,0.00008042315,0.00002422914,0.00008731416,0.00004137173],"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":[0.00001113061,0.00001892767,0.01080781,0.000005247153,0.00001616272,0.00003276832,0.000008630648,0.9865658,0.0002221045,0.0001990414,0.00004823352,0.002064186],"study_design_scores_gemma":[0.000001172954,0.000002725544,0.0006981326,5.299437e-7,0.000002180585,0.000002468726,0.000002343727,0.9991991,0.00004111135,0.00003415093,0.00001496437,0.000001128378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9662549,0.0001503832,0.03175601,0.0001577713,0.00002945899,0.00001711886,0.0002347235,0.0001745891,0.001224951],"genre_scores_gemma":[0.9962876,0.00004094577,0.003225311,0.00001310556,0.00000971396,0.00001158434,0.0001340488,0.00000510384,0.0002725251],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1308108,"threshold_uncertainty_score":0.2600988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1148626577750293,"score_gpt":0.3398676384664631,"score_spread":0.2250049806914338,"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."}}