{"id":"W4386353213","doi":"10.1016/j.ecoinf.2023.102282","title":"FirePred: A hybrid multi-temporal convolutional neural network model for wildfire spread prediction","year":2023,"lang":"en","type":"article","venue":"Ecological Informatics","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre For Cold Ocean Resources Engineering; Memorial University of Newfoundland","funders":"","keywords":"Weighting; Computer science; Convolutional neural network; Set (abstract data type); Deep learning; Property (philosophy); Artificial intelligence; Machine learning; Data mining; Environmental science","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007125413,0.0001969406,0.0002465736,0.00003078014,0.0003119804,0.00005174497,0.0002807537,0.0001441256,0.000188395],"category_scores_gemma":[0.0002354477,0.0001645963,0.0001273453,0.0002568227,0.0001434165,0.0005082684,0.0002724341,0.0001803569,0.001161067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002279077,"about_ca_system_score_gemma":0.00001826085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002960893,"about_ca_topic_score_gemma":0.00006024468,"domain_scores_codex":[0.9982499,0.00004575134,0.0006106368,0.0001964283,0.0003110793,0.0005861898],"domain_scores_gemma":[0.9991104,0.0002863817,0.0002034218,0.0002310122,0.00001146327,0.0001573432],"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":[0.00004313897,0.0001160274,0.1099659,0.00004692898,0.00002065165,0.000005547254,0.0003644511,0.7820937,0.00003003195,0.0000710309,0.104883,0.002359617],"study_design_scores_gemma":[0.0005572995,0.0001808201,0.1195262,0.000009861561,0.00001051935,0.00001271395,0.00003229878,0.876559,0.000007308486,0.0005237763,0.002420029,0.0001601647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9551399,0.000006599224,0.04138957,0.0001834838,0.0006754707,0.001239061,0.0001774214,0.0005411661,0.0006473255],"genre_scores_gemma":[0.985049,0.000004798545,0.01289933,0.0004806002,0.0001278517,0.0003309824,0.0003148229,0.00001526027,0.0007773536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.102463,"threshold_uncertainty_score":0.9996166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02939327001294127,"score_gpt":0.2455481965398592,"score_spread":0.216154926526918,"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."}}