{"id":"W4412107607","doi":"10.1016/j.aej.2025.06.056","title":"Machine learning-based estimation of seismic structural damage via an accessible web application","year":2025,"lang":"en","type":"article","venue":"Alexandria Engineering Journal","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Academia Oamenilor de Știință din România; Universitatea Politehnica din București; National University of Science and Technology","keywords":"Estimation; Computer science; Artificial intelligence; Machine learning; Engineering; Systems engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002094404,0.0002062844,0.000238952,0.0003940355,0.0001060122,0.00005647335,0.0002816902,0.00009954519,0.00001949367],"category_scores_gemma":[0.00004396109,0.0002071838,0.00005792173,0.0003643562,0.00001802017,0.0003275112,0.00002215324,0.0005666668,0.000001175944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001735634,"about_ca_system_score_gemma":0.00005040349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000025543,"about_ca_topic_score_gemma":0.0000026868,"domain_scores_codex":[0.9989598,0.0000284631,0.0004381555,0.0001398098,0.0001812869,0.0002524327],"domain_scores_gemma":[0.9994236,0.00006315888,0.0001017828,0.0002288601,0.0000766275,0.0001059659],"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":[0.00001165494,0.000003913238,0.002369145,0.0002179045,0.00001918004,0.000001717465,0.00002560391,0.9656863,0.009294183,0.00009184045,0.00002944363,0.02224907],"study_design_scores_gemma":[0.0003709544,0.00004564967,0.02414554,0.0001354466,0.00002321185,0.00002350748,0.000003124485,0.9581946,0.01619999,0.0003132469,0.0003832519,0.0001615189],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.574357,0.0002575104,0.4240338,0.00002952529,0.0005861268,0.0001324538,0.000004302762,0.0005458307,0.00005339074],"genre_scores_gemma":[0.9660625,0.00003448175,0.03366035,0.00001037857,0.0001373157,0.00001622508,0.00002436185,0.0000399937,0.00001436188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3917055,"threshold_uncertainty_score":0.844871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006360651543654631,"score_gpt":0.2698809707375341,"score_spread":0.2635203191938795,"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."}}