{"id":"W2155879844","doi":"10.1007/978-3-540-72108-6_26","title":"Increasing public and environmental safety through integrated monitoring and analysis of structural and ground deformations","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in geoinformation and cartography","topic":"Synthetic Aperture Radar (SAR) Applications and Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Deformation (meteorology); Structural engineering; Warning system; Acceleration; Deformation monitoring; Engineering; Computer science; Materials science; Physics; Classical mechanics; Aerospace engineering; Composite material","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":[0.0004490418,0.0005077261,0.0004321989,0.001310465,0.0001987711,0.001315944,0.0006118375,0.0006274066,0.003385374],"category_scores_gemma":[0.0008513173,0.0002145329,0.0003356769,0.001447363,0.0004493395,0.001955872,0.00135124,0.0005122186,0.001252539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002989877,"about_ca_system_score_gemma":0.0005181239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001452535,"about_ca_topic_score_gemma":0.003384404,"domain_scores_codex":[0.9995133,0.00004719034,0.00001151748,0.00008522352,0.0003000469,0.0000427337],"domain_scores_gemma":[0.9996654,0.00008457309,0.00006233829,0.00004358227,0.0001259667,0.00001820192],"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":[0.00009647569,0.0001475028,0.008327956,0.0001444529,0.00005749822,0.00003919049,0.0001695221,0.01770225,0.04454015,0.006567336,0.006104334,0.9161032],"study_design_scores_gemma":[0.0000821326,0.0008063183,0.117688,0.0004403236,0.0003891938,0.0007439524,0.001473291,0.4118113,0.2151021,0.1051368,0.1461501,0.0001764276],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1821464,0.004240396,0.7350646,0.002337375,0.0001949586,0.0001114181,0.001194629,0.003461645,0.07124875],"genre_scores_gemma":[0.7106415,0.004684145,0.2608038,0.0003581898,0.0001866992,0.00007389607,0.001281933,0.0002673646,0.02170248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003385374,"threshold_uncertainty_score":0.01132518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008568693602381841,"score_gpt":0.2118560180073665,"score_spread":0.2032873244049847,"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."}}