{"id":"W2095003778","doi":"10.1061/(asce)be.1943-5592.0000608","title":"Energy Harvesting from Train-Induced Response in Bridges","year":2014,"lang":"en","type":"article","venue":"Journal of Bridge Engineering","topic":"Innovative Energy Harvesting Technologies","field":"Engineering","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trinity College","funders":"","keywords":"Energy harvesting; Train; Bridge (graph theory); Structural health monitoring; Energy (signal processing); Vibration; Work (physics); Engineering; Computer science; Structural engineering; Mechanical engineering; Acoustics","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":[0.000100138,0.0001015989,0.0001183964,0.00008900569,0.000096253,0.0001504407,0.0001808704,0.0001741731,0.0008869687],"category_scores_gemma":[0.0001294841,0.00006510771,0.0001454104,0.0001369568,0.0001996712,0.0002108451,0.0002063619,0.0001147342,0.0001265063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001453591,"about_ca_system_score_gemma":0.00007227218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003147433,"about_ca_topic_score_gemma":0.0004504375,"domain_scores_codex":[0.9999596,0.000005228725,0.000001183699,0.000008894667,0.00001777628,0.000007285596],"domain_scores_gemma":[0.999976,0.00001047871,0.000003914388,0.000003305251,0.000004425176,0.00000186267],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001260467,0.00008155037,0.004496142,0.00009354542,0.00002249664,0.0002797801,0.0002218579,0.1504186,0.8149955,0.00212138,0.0003816904,0.02676143],"study_design_scores_gemma":[0.0000169081,0.0003462044,0.02466452,0.00001632352,0.00001638894,0.0001925396,0.0001764499,0.8100253,0.1606551,0.002296206,0.001566991,0.00002690754],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9674906,0.00009707159,0.02888852,0.00004745677,0.000007464897,0.00001164612,0.00005020998,0.00004870971,0.003358251],"genre_scores_gemma":[0.9988417,0.00003716148,0.0005671976,0.000006207223,0.000001062749,0.000005840853,0.00001725737,0.000002096498,0.000521483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008869687,"threshold_uncertainty_score":0.002967238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01593367574714528,"score_gpt":0.2096239892162777,"score_spread":0.1936903134691325,"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."}}