{"id":"W2333469039","doi":"10.2514/6.2010-8317","title":"Dynamic Mapping of Forest Fire Fronts Using Multiple Unmanned Aerial Vehicles","year":2010,"lang":"en","type":"article","venue":"AIAA Guidance, Navigation, and Control Conference","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Remote sensing; Aeronautics; Environmental science; Aerospace engineering; Engineering; Geology","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":[0.0002352529,0.0006293926,0.0004838034,0.0006744042,0.0004514081,0.0005490234,0.0006384167,0.0003346526,0.0007456185],"category_scores_gemma":[0.000738664,0.0003810036,0.0004578548,0.000327838,0.0002739982,0.0007783189,0.0006661119,0.0006062149,0.0002127863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005180743,"about_ca_system_score_gemma":0.0005077101,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007278747,"about_ca_topic_score_gemma":0.008104272,"domain_scores_codex":[0.9998443,0.00001635663,0.000006002229,0.00004401179,0.00006134383,0.0000278978],"domain_scores_gemma":[0.9998252,0.00004034176,0.00004217905,0.00002825633,0.00004276849,0.00002122499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002114128,0.00007080506,0.004115048,0.00005252571,0.00007078603,0.0002212717,0.0002506262,0.6155244,0.02423839,0.003793307,0.0007025714,0.3507489],"study_design_scores_gemma":[0.000007637919,0.00002802376,0.001168237,0.000004695857,0.000006765427,0.00004563592,0.0000386604,0.992456,0.003962657,0.001481249,0.0007921189,0.000008326603],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1074293,0.0001663493,0.8904057,0.00006161844,0.0000325213,0.00003156372,0.00004567688,0.0005942604,0.001232964],"genre_scores_gemma":[0.7907279,0.0001152159,0.2071173,0.00002053041,0.00002096887,0.00003694823,0.0001166188,0.00004920728,0.001795454],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007278747,"threshold_uncertainty_score":0.01447272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007362407037770482,"score_gpt":0.2140429955147622,"score_spread":0.2066805884769917,"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."}}