{"id":"W2332622993","doi":"10.1061/40972(311)67","title":"Real-Time Construction Monitoring with a Wireless Shape-Acceleration Array System","year":2008,"lang":"en","type":"article","venue":"GeoCongress 2008","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of New Brunswick","funders":"New York State Department of Transportation; National Science Foundation","keywords":"Inclinometer; Casing; Acceleration; Wireless sensor network; Sensor array; Wireless; Instrumentation (computer programming); Computer science; Engineering; Real-time computing; Telecommunications; Mechanical engineering; Geology","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":[0.0002076064,0.0002291274,0.0002534684,0.0005005632,0.0001187994,0.0002821772,0.0003607795,0.0002981932,0.0008415911],"category_scores_gemma":[0.0004917001,0.000123464,0.0001116791,0.0004328381,0.0001023327,0.0004407997,0.0003584994,0.000192993,0.0004252237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001545531,"about_ca_system_score_gemma":0.0001677372,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005796258,"about_ca_topic_score_gemma":0.0009643311,"domain_scores_codex":[0.9997123,0.00005205332,0.000008888092,0.00006201188,0.00014423,0.00002045403],"domain_scores_gemma":[0.9997714,0.00004640889,0.00004696118,0.00002798492,0.00008636075,0.00002084369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006987464,0.0002776446,0.04922858,0.0001129141,0.00007324675,0.0002379456,0.0002623815,0.02684546,0.2853838,0.001189114,0.003634686,0.6320555],"study_design_scores_gemma":[0.0001852826,0.002121659,0.1061541,0.00003084779,0.0001590882,0.001124742,0.0002110194,0.7402875,0.1314109,0.0012663,0.01695509,0.00009350776],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5292667,0.0002672136,0.4585498,0.0003174068,0.0001303102,0.0001032997,0.0004083074,0.003481639,0.007475382],"genre_scores_gemma":[0.9115229,0.0001111152,0.08562467,0.00006190836,0.00006571703,0.00007298498,0.0002444886,0.00002406053,0.002272193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008415911,"threshold_uncertainty_score":0.002815366,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01650015831228065,"score_gpt":0.2378021320524837,"score_spread":0.2213019737402031,"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."}}