{"id":"W4404892667","doi":"10.1080/07011784.2024.2430776","title":"Large-scale flood modelling based on LiDAR data: a case study in the Southwest Miramichi watershed, New Brunswick, Canada","year":2024,"lang":"en","type":"article","venue":"Canadian Water Resources Journal / Revue canadienne des ressources hydriques","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère de l’Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs; Natural Resources Canada; Université du Québec à Rimouski; Ministère des Ressources naturelles et des Forêts; Concordia University","funders":"Natural Resources Canada","keywords":"Watershed; Flood myth; Scale (ratio); Lidar; Hydrology (agriculture); Environmental science; Geography; Remote sensing; Cartography; Geology; Archaeology; Computer science; Geotechnical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.00042712,0.0005949461,0.000331533,0.0006586651,0.001531967,0.001292505,0.001327776,0.0006291773,0.0009798346],"category_scores_gemma":[0.001357798,0.0003040814,0.0004368126,0.002252619,0.0007802321,0.000472882,0.0005004115,0.0005480089,0.0001154939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02310396,"about_ca_system_score_gemma":0.01496515,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9892196,"about_ca_topic_score_gemma":0.9931577,"domain_scores_codex":[0.9996904,0.00005752246,0.00001628097,0.00005811311,0.00008399702,0.0000936164],"domain_scores_gemma":[0.9993309,0.0002222025,0.00005307835,0.0000469726,0.0002744769,0.00007230989],"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":[0.0003293851,0.0006900211,0.3715838,0.0002894824,0.0002277293,0.00544146,0.002681775,0.5493268,0.005691338,0.002987884,0.006295057,0.05445522],"study_design_scores_gemma":[0.00009179278,0.00009364324,0.2902855,0.00006877578,0.0001072215,0.0002050943,0.006967548,0.6949704,0.002360986,0.0005100754,0.004224162,0.000114799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947049,0.00009158722,0.0012529,0.0002519219,0.000005772112,0.00008555996,0.0009036597,0.00008862785,0.002615003],"genre_scores_gemma":[0.9945772,0.0001387775,0.003226284,0.00003546835,0.000002687313,0.00003231084,0.0006774755,0.00001920278,0.0012905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02310396,"threshold_uncertainty_score":0.1676317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02204132594588072,"score_gpt":0.2237739519079318,"score_spread":0.201732625962051,"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."}}