{"id":"W4399130561","doi":"10.2166/hydro.2024.275","title":"Accelerating regional-scale groundwater flow simulations with a hybrid deep neural network model incorporating mixed input types: A case study of the northeast Qatar aquifer","year":2024,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Reservoir Engineering and Simulation Methods","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Sultan Qaboos University; Khalifa University of Science, Technology and Research; Hamad Bin Khalifa University","keywords":"Aquifer; Groundwater flow; Groundwater; Scale (ratio); Groundwater model; Hydrology (agriculture); Environmental science; Artificial neural network; Geology; Flow (mathematics); Geotechnical engineering; Geography; Computer science; Cartography; Mathematics; Machine learning","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":[0.0003831679,0.0005678253,0.0004078658,0.0003376282,0.0004086186,0.0005870025,0.0006538788,0.0009752928,0.001463299],"category_scores_gemma":[0.0007377843,0.0002699621,0.0005108526,0.0003505693,0.0004971014,0.0004063811,0.0004935156,0.0005726439,0.0001139088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001397365,"about_ca_system_score_gemma":0.001203947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07998721,"about_ca_topic_score_gemma":0.0605953,"domain_scores_codex":[0.9999151,0.000025549,0.000004556789,0.0000214917,0.00001245898,0.00002080652],"domain_scores_gemma":[0.999733,0.0001493793,0.00001924977,0.00001804748,0.00005827871,0.00002199895],"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":[0.00002555909,0.00003004995,0.00154536,0.00001125361,0.000008783825,0.00006149166,0.00001378938,0.9953063,0.0005343531,0.0002447593,0.0001866665,0.002031611],"study_design_scores_gemma":[0.00000417082,0.000008017875,0.0002662362,0.000001427565,0.00000188008,0.000002748244,0.000009592572,0.9993423,0.0002253676,0.00008010442,0.00005636744,0.000001808127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9622281,0.0001356299,0.03119066,0.0005254736,0.00004847614,0.0000459018,0.0005917145,0.0005164514,0.004717488],"genre_scores_gemma":[0.9891821,0.0000326482,0.009540221,0.00004741414,0.000006103999,0.0000192029,0.0002364983,0.00002020375,0.0009155982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07998721,"threshold_uncertainty_score":0.1590433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02530332618753747,"score_gpt":0.2524863562469032,"score_spread":0.2271830300593657,"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."}}