{"id":"W4405761261","doi":"10.1016/j.measurement.2024.116567","title":"Displacement prediction for long-span bridges via limited remote sensing images: An adaptive ensemble regression method","year":2024,"lang":"en","type":"article","venue":"Measurement","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mitacs","funders":"Mitacs; European Commission; HORIZON EUROPE Framework Programme; European Space Agency","keywords":"Displacement (psychology); Span (engineering); Regression; Regression analysis; Computer science; Remote sensing; Structural engineering; Geology; Engineering; Statistics; Machine learning; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006586033,0.0006832122,0.0006401913,0.0004854005,0.0002425844,0.0004166186,0.001039161,0.0006940812,0.000931084],"category_scores_gemma":[0.001438063,0.000310884,0.0007801749,0.0005744611,0.0002029492,0.0008462194,0.0004798803,0.0009220431,0.0003182452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002404706,"about_ca_system_score_gemma":0.000464509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005174693,"about_ca_topic_score_gemma":0.0054646,"domain_scores_codex":[0.9997749,0.00004967515,0.00001246719,0.00007607829,0.00006491726,0.00002196247],"domain_scores_gemma":[0.999587,0.0001671876,0.00005282333,0.00005589043,0.0001204056,0.0000168381],"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":[0.0000523612,0.00007739236,0.002838615,0.00004243054,0.0001000241,0.00006943429,0.0000554683,0.7899275,0.009898606,0.002694609,0.001209811,0.1930338],"study_design_scores_gemma":[7.799329e-7,0.000005578787,0.0001561473,0.000001246934,0.000003558017,0.000004430562,0.000001655779,0.9991825,0.0003285219,0.0002189208,0.00009449329,0.000002071507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02684412,0.0001806823,0.9719947,0.00009080226,0.00003151923,0.00001271049,0.00003917132,0.0002937026,0.0005126141],"genre_scores_gemma":[0.688356,0.0005376632,0.3068648,0.00009360461,0.000116168,0.00009188829,0.0004049732,0.0001033893,0.003431431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005174693,"threshold_uncertainty_score":0.01028913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07232079999457293,"score_gpt":0.3359010437246631,"score_spread":0.2635802437300901,"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."}}