{"id":"W2992909639","doi":"10.3390/fire2040060","title":"Measuring Initial Attack Suppression Effectiveness through Burn Probability","year":2019,"lang":"en","type":"article","venue":"Fire","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; Parks Canada","funders":"Canadian Forest Service; U.S. Forest Service; Parks Canada","keywords":"Crew; Containment (computer programming); National park; Environmental science; Conditional probability; Statistics; Environmental resource management; Computer science; Geography; Engineering; Mathematics; Aeronautics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005326968,0.0001662591,0.0002179642,0.000007764293,0.00009899001,0.00003012549,0.0002566596,0.0001078987,0.001943254],"category_scores_gemma":[0.00009567653,0.0001392391,0.00007268036,0.0001498313,0.00006438077,0.0004506939,0.0002472952,0.0001644854,0.005371138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002785139,"about_ca_system_score_gemma":0.000009217567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008041736,"about_ca_topic_score_gemma":0.00004394332,"domain_scores_codex":[0.998357,0.0003187041,0.0001717002,0.000458653,0.0003851531,0.0003087957],"domain_scores_gemma":[0.9991995,0.0001910866,0.0000676159,0.0004685528,0.000008408487,0.0000648395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002373354,0.0003450633,0.8962542,0.0008344832,0.00003055102,0.00003008203,0.001313233,0.003195221,0.07257133,0.000031798,0.003061765,0.0220949],"study_design_scores_gemma":[0.001555658,0.0003920026,0.8815288,0.000667941,0.00001855863,0.00003060819,0.00002689045,0.01163604,0.08376468,0.0007365972,0.01897867,0.0006635836],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9852188,0.00003212849,0.00006524766,0.00004953332,0.0007002964,0.0009530062,0.000007115679,0.0001045861,0.01286929],"genre_scores_gemma":[0.9994605,0.0000012228,0.0001684673,0.00004402125,0.00006662418,0.00005690017,0.000006842673,0.00002194044,0.0001735041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02143132,"threshold_uncertainty_score":0.9989691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03037215146316395,"score_gpt":0.2595230708233253,"score_spread":0.2291509193601614,"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."}}