{"id":"W4283447688","doi":"10.1145/3530190.3534816","title":"Landscape Optimization for Prescribed Burns in Wildfire Mitigation Planning","year":2022,"lang":"en","type":"article","venue":"","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Software deployment; Computer science; Genetic algorithm; Domain (mathematical analysis); Set (abstract data type); Key (lock); Optimization problem; Operations research; Environmental resource management; Environmental science; Engineering; Machine learning; Computer security; Mathematics","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":[0.0009482147,0.0005523039,0.0005904726,0.0004375734,0.0003582993,0.0007218434,0.0004775613,0.0007738686,0.002557434],"category_scores_gemma":[0.001911262,0.0003140383,0.0004951707,0.0004215964,0.0006580038,0.000774023,0.0005672554,0.000762303,0.0001483645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009950313,"about_ca_system_score_gemma":0.001145942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006611558,"about_ca_topic_score_gemma":0.009082952,"domain_scores_codex":[0.9997151,0.0001459354,0.000007353191,0.00003381426,0.00004825319,0.00004945428],"domain_scores_gemma":[0.9995326,0.000309479,0.00004934496,0.00001764222,0.00005667472,0.00003423816],"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":[0.00001029545,0.000009880762,0.0001845315,0.000009929675,0.000005578645,0.00001302561,0.000007342142,0.9941669,0.0001600079,0.002899305,0.0001335555,0.002399653],"study_design_scores_gemma":[0.000004306307,0.00001386611,0.0001081147,0.000005832454,0.000002810673,0.000005671948,0.00001366949,0.9957433,0.0001191854,0.003650569,0.0003306027,0.00000204712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1113533,0.0008515485,0.8711317,0.000759546,0.00007262974,0.0001038973,0.0001896248,0.000199201,0.01533855],"genre_scores_gemma":[0.8953044,0.0003000975,0.09941254,0.0001621538,0.00002034439,0.0001010434,0.0001190535,0.00008963533,0.004490617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006611558,"threshold_uncertainty_score":0.01314616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007230912457211775,"score_gpt":0.2160632133675854,"score_spread":0.2088323009103736,"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."}}