{"id":"W4361277442","doi":"10.3390/rs15071821","title":"Deep Learning Approaches for Wildland Fires Remote Sensing: Classification, Detection, and Segmentation","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Fire Detection and Safety Systems","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Deep learning; Segmentation; Artificial intelligence; Market segmentation; Machine learning; Fire detection; Remote sensing; Geography; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000324166,0.0001702523,0.000181368,0.000194357,0.0003439013,0.00009343542,0.00002851863,0.0001270524,7.274216e-7],"category_scores_gemma":[0.0001179449,0.0001878774,0.00005648403,0.0003824982,0.00003049395,0.00009984063,0.00001274728,0.000160373,0.00002050644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000898525,"about_ca_system_score_gemma":0.000006874995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005661904,"about_ca_topic_score_gemma":0.0001101445,"domain_scores_codex":[0.9989888,0.000062548,0.0002851721,0.0002650944,0.0001382024,0.000260169],"domain_scores_gemma":[0.9995179,0.0001310006,0.00006660113,0.0001504371,0.00006299688,0.00007102915],"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":[0.00001339632,5.61323e-7,0.000007309869,0.0001331678,0.00003326506,0.000002434251,0.0009629137,0.05586927,0.03457839,0.000002156429,0.00005643206,0.9083407],"study_design_scores_gemma":[0.0003141169,0.00002263154,0.0007539329,0.00005837749,0.00002109641,0.00008989108,0.001276041,0.9873635,0.003508809,0.0001599831,0.006226475,0.0002051317],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2269594,0.0001406079,0.7703125,0.0001589226,0.0005438362,0.0003355773,9.805945e-7,0.001073011,0.0004751426],"genre_scores_gemma":[0.9870442,0.0001214439,0.0121522,0.00001911268,0.0002631943,5.081642e-8,0.00002955399,0.00006745879,0.0003028021],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9314942,"threshold_uncertainty_score":0.7661418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03648153055778519,"score_gpt":0.2340152294937734,"score_spread":0.1975336989359882,"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."}}