{"id":"W4403468743","doi":"10.1007/s10064-024-03941-1","title":"Integrating analytical and machine learning approaches to simulate and predict dam foundation stress and river valley contraction in a large-scale reservoir","year":2024,"lang":"en","type":"article","venue":"Bulletin of Engineering Geology and the Environment","topic":"Dam Engineering and Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"China Scholarship Council; National Natural Science Foundation of China; University of Ottawa","keywords":"Nature Conservation; Foundation (evidence); Contraction (grammar); Scale (ratio); Geotechnical engineering; Geology; Hydrology (agriculture); Geography; Archaeology; Ecology; Cartography; Philosophy; Biology","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":[0.0006527891,0.0001338854,0.0001956052,0.0001063704,0.00004914052,0.00002183521,0.00003569002,0.00008400179,0.00001377943],"category_scores_gemma":[0.00006475049,0.000108681,0.00001543336,0.00004701789,0.0001147899,0.00002652934,0.00008493716,0.000369732,0.000002027618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002106112,"about_ca_system_score_gemma":0.000001476786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004061285,"about_ca_topic_score_gemma":0.000007784016,"domain_scores_codex":[0.9993183,0.00004683844,0.0001834065,0.0001974162,0.00007201553,0.0001820127],"domain_scores_gemma":[0.9994739,0.0003789616,0.00001105081,0.0000804844,0.00000274673,0.00005280982],"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":[0.00003538503,0.00001278285,0.005171696,0.0002356983,0.00006432575,0.000005300037,0.001626012,0.987797,0.00009400637,0.002839367,0.000006977387,0.002111485],"study_design_scores_gemma":[0.0005312106,0.0000499283,0.03276545,0.0001203474,0.00002481958,0.00001191032,0.0001346761,0.9592185,0.00003341141,0.00008989314,0.006915362,0.0001045065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9661775,0.003658182,0.02902886,0.000640853,0.00007278978,0.0001958487,0.00000492929,0.00007248569,0.0001485573],"genre_scores_gemma":[0.9976329,0.0009435794,0.001269852,0.000005161314,0.00002621943,0.00002113516,0.00000737874,0.00001821723,0.00007557333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03145538,"threshold_uncertainty_score":0.4431883,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01051410267896587,"score_gpt":0.1807599470975251,"score_spread":0.1702458444185592,"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."}}