{"id":"W2965595054","doi":"10.24963/ijcai.2019/636","title":"FireCast: Leveraging Deep Learning to Predict Wildfire Spread","year":2019,"lang":"en","type":"article","venue":"","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Random forest; Geographic information system; Climate change; Machine learning; Meteorology; Artificial intelligence; Remote sensing; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009064505,0.00133857,0.0006069926,0.001322239,0.0003412975,0.0007983939,0.001421812,0.001109873,0.001375179],"category_scores_gemma":[0.002457432,0.0003490669,0.0006219136,0.0006800288,0.0002630745,0.001682115,0.0009167765,0.001353105,0.0005539308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008312256,"about_ca_system_score_gemma":0.0007879602,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0204477,"about_ca_topic_score_gemma":0.03396929,"domain_scores_codex":[0.9998093,0.0000246137,0.00001217706,0.0000806272,0.00004331183,0.00003006682],"domain_scores_gemma":[0.9993411,0.0003298107,0.00006326769,0.0000795021,0.0001128825,0.00007344711],"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":[0.0008108501,0.001446028,0.03212294,0.0001537094,0.000455458,0.0002916325,0.00009774365,0.46011,0.005388012,0.001491444,0.02004575,0.4775864],"study_design_scores_gemma":[0.00001961094,0.00005679615,0.0009638231,0.000007606202,0.00001541268,0.0000204862,0.000008306399,0.9960161,0.001315384,0.001021077,0.0005473539,0.00000799343],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6533671,0.002624756,0.3057778,0.001017866,0.0006976529,0.0002849975,0.00668056,0.02342876,0.006120527],"genre_scores_gemma":[0.9044232,0.0004509768,0.07951219,0.0003524997,0.0001384424,0.00009286856,0.008921472,0.0002168551,0.005891488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0204477,"threshold_uncertainty_score":0.04065734,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004984461185114209,"score_gpt":0.191770765722553,"score_spread":0.1867863045374388,"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."}}