{"id":"W3096872115","doi":"10.1007/s13349-020-00446-9","title":"Early warning system for the detection of unexpected bridge displacements from radar satellite data","year":2020,"lang":"en","type":"article","venue":"Journal of Civil Structural Health Monitoring","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":77,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Interferometric synthetic aperture radar; Bridge (graph theory); Computer science; Synthetic aperture radar; Warning system; Satellite; Remote sensing; Visualization; Real-time computing; Geology; Artificial intelligence; Engineering; Telecommunications; Aerospace 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008886626,0.0004365062,0.0006168975,0.001342331,0.0003462097,0.0005173819,0.0005510752,0.000735152,0.003168621],"category_scores_gemma":[0.001890994,0.0002004499,0.0001415202,0.0003353628,0.0001182337,0.0005658061,0.0006969486,0.0006358348,0.001270092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001973346,"about_ca_system_score_gemma":0.0005658112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006334078,"about_ca_topic_score_gemma":0.0009757738,"domain_scores_codex":[0.999723,0.00005230134,0.00002478677,0.00005538623,0.0001063748,0.0000380794],"domain_scores_gemma":[0.9991217,0.0002147031,0.0001151642,0.00009232401,0.0003643533,0.00009170396],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003228445,0.0006433601,0.01614516,0.0003859845,0.0001193641,0.0006960031,0.0002739903,0.009587198,0.3319804,0.002812832,0.03463752,0.5994898],"study_design_scores_gemma":[0.0005249054,0.001840659,0.03473978,0.0001106764,0.0002688437,0.001000619,0.0001482036,0.813946,0.1237305,0.002111103,0.02142912,0.0001495861],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3967161,0.00170412,0.5637519,0.001112919,0.001021087,0.0006018452,0.002245941,0.02683262,0.006013458],"genre_scores_gemma":[0.8602949,0.0002888262,0.1303089,0.0004505735,0.0002033367,0.0002189319,0.001639335,0.00009595534,0.006499149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003168621,"threshold_uncertainty_score":0.01060009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04075475091516181,"score_gpt":0.2965010927255428,"score_spread":0.2557463418103809,"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."}}