{"id":"W2031229943","doi":"10.1139/l10-128","title":"Statistical vehicle classification methods derived from girder strains in bridges","year":2011,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Girder; Bridge (graph theory); Artificial neural network; Span (engineering); Computer science; Strain gauge; Filter (signal processing); Engineering; Structural engineering; Artificial intelligence; Data mining; Pattern recognition (psychology); Computer vision","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0007623876,0.0004525339,0.0003568888,0.00195987,0.0001848443,0.000507587,0.0004532026,0.0004090957,0.0008002255],"category_scores_gemma":[0.003417992,0.0001884448,0.0002849569,0.001121891,0.0002007514,0.0006583218,0.0003004723,0.000369444,0.0003428737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000355502,"about_ca_system_score_gemma":0.0003210862,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003239376,"about_ca_topic_score_gemma":0.004373067,"domain_scores_codex":[0.9996736,0.00006535168,0.00002581163,0.00006661267,0.0001269057,0.00004170702],"domain_scores_gemma":[0.9986572,0.0007262804,0.00020497,0.00007056379,0.0003052858,0.00003571272],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002789734,0.0002252746,0.06812678,0.00009522601,0.0001075983,0.00009818258,0.0001466985,0.3095951,0.01633075,0.002032295,0.001294176,0.6016688],"study_design_scores_gemma":[0.000006958672,0.00005304038,0.03426762,0.000008134038,0.00001103467,0.00004002805,0.00005392381,0.9608314,0.002644236,0.001594088,0.0004760818,0.00001339187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7321858,0.0004068828,0.2635819,0.0001669631,0.00005961382,0.00006876733,0.0003556156,0.000634617,0.002539773],"genre_scores_gemma":[0.9610149,0.0001574022,0.03660854,0.00002287068,0.00005903095,0.00005692912,0.0008494431,0.00003086964,0.001199984],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003239376,"threshold_uncertainty_score":0.006440997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02372521959401657,"score_gpt":0.2391032217263404,"score_spread":0.2153780021323238,"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."}}