{"id":"W4415762767","doi":"10.1186/s40562-025-00429-y","title":"Eco-hydrologic model for assessing the climate and hydrologic elasticity of vegetation in mountain wetlands","year":2025,"lang":"en","type":"article","venue":"Geoscience Letters","topic":"Hydrology and Watershed Management Studies","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Korea Environmental Industry and Technology Institute; Ministry of Education, India; Ministry of Environment","keywords":"Wetland; Marsh; Vegetation (pathology); Climate change; Hydrology (agriculture); Hydrological modelling; Elasticity (physics)","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":[0.0002730109,0.0003896964,0.0002310936,0.0004382598,0.0002708009,0.0003791982,0.0004756783,0.0004954484,0.001100796],"category_scores_gemma":[0.0006083437,0.0002184263,0.0005203563,0.0002590058,0.0001944733,0.0004009559,0.0003677893,0.000332306,0.0001082658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005759125,"about_ca_system_score_gemma":0.0006248141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01660045,"about_ca_topic_score_gemma":0.00928282,"domain_scores_codex":[0.9999114,0.00002700733,0.000004970466,0.00002750527,0.00001207009,0.00001698578],"domain_scores_gemma":[0.9998252,0.00007709591,0.00003125931,0.00001440343,0.00003334551,0.00001877541],"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":[0.00001884947,0.00003514094,0.007933162,0.00000610662,0.00001820608,0.00003366857,0.00001364629,0.9880696,0.001291663,0.0005897736,0.00008988532,0.001900387],"study_design_scores_gemma":[0.000002515837,0.000006461177,0.001381051,5.513338e-7,0.000002282229,0.000005050244,0.000005798515,0.9982558,0.00011326,0.0001697164,0.00005572266,0.000001826839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7955042,0.0000595128,0.1998203,0.0001280829,0.00001860932,0.00008260218,0.0006268513,0.0004667013,0.003293216],"genre_scores_gemma":[0.9929718,0.0000209693,0.005919855,0.0000114735,0.000003281718,0.00005963705,0.0001540132,0.00001710609,0.0008418164],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01660045,"threshold_uncertainty_score":0.03300768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01129540743388902,"score_gpt":0.2540628382840592,"score_spread":0.2427674308501702,"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."}}