{"id":"W2998673165","doi":"10.1155/2019/4352396","title":"A Spatiotemporal Deformation Modelling Method Based on Geographically and Temporally Weighted Regression","year":2019,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Dam Engineering and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Fundamental Research Funds for the Central Universities; Central South University; National Natural Science Foundation of China","keywords":"Deformation (meteorology); Flexibility (engineering); Transformation (genetics); Computer science; Inverse; Deformation monitoring; Lag; Regression; Algorithm; Mathematics; Data mining; Mathematical optimization; Statistics; Geology; Geometry","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":[0.0008788451,0.0009125564,0.0008123926,0.001262294,0.0004345729,0.0008692652,0.001521048,0.0007870492,0.002046156],"category_scores_gemma":[0.001845915,0.0004915199,0.001741035,0.00184014,0.0003128099,0.001928279,0.0008990883,0.0008662072,0.0007467213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005548455,"about_ca_system_score_gemma":0.001241813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01931691,"about_ca_topic_score_gemma":0.009788548,"domain_scores_codex":[0.9992297,0.0001446543,0.00006951658,0.0002835426,0.0002195718,0.00005302739],"domain_scores_gemma":[0.9996081,0.00009317797,0.00008185269,0.00004602721,0.0001508266,0.00001995604],"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":[0.00004108401,0.00003396412,0.002515554,0.000112823,0.0001041289,0.00013808,0.0001069867,0.8235251,0.009149222,0.01411928,0.001522744,0.148631],"study_design_scores_gemma":[0.000002075294,0.0000090836,0.000273361,0.000003559035,0.00001270811,0.00002988709,0.000008733614,0.9970075,0.000679863,0.00101718,0.0009448635,0.00001122575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004936553,0.00008790467,0.9937688,0.0000584407,0.0000272483,0.00001853159,0.00008764793,0.0003245809,0.0006902096],"genre_scores_gemma":[0.5044144,0.001255857,0.482706,0.0001193663,0.0001428656,0.0002852555,0.001101748,0.0005288589,0.0094456],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01931691,"threshold_uncertainty_score":0.03840894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007705022845872378,"score_gpt":0.2029021126153675,"score_spread":0.1951970897694951,"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."}}