{"id":"W3088759699","doi":"10.1007/s13349-020-00438-9","title":"Cable tension monitoring through feature-based video image processing","year":2020,"lang":"en","type":"article","venue":"Journal of Civil Structural Health Monitoring","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Scale-invariant feature transform; Feature (linguistics); Computer science; Detector; Computer vision; Feature extraction; Image processing; Engineering; Displacement (psychology); Artificial intelligence; Real-time computing; Image (mathematics); Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001161855,0.0003622498,0.0002489905,0.001164066,0.0001250688,0.0003440308,0.0003351977,0.0004372342,0.001265406],"category_scores_gemma":[0.0004574738,0.0001186275,0.000143975,0.0007891543,0.0001301097,0.0005140462,0.0002115573,0.0003029509,0.0003945549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001695581,"about_ca_system_score_gemma":0.0001784485,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001653163,"about_ca_topic_score_gemma":0.00260594,"domain_scores_codex":[0.9998732,0.0000111219,0.000004043855,0.00002744793,0.00006822171,0.0000160624],"domain_scores_gemma":[0.9997445,0.00006030681,0.00004880177,0.00001950018,0.0001135903,0.00001342913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003085854,0.00009851089,0.003079076,0.0001325338,0.00002580158,0.0001075978,0.00004845115,0.004757882,0.6137524,0.0003635048,0.001597562,0.375728],"study_design_scores_gemma":[0.00003638517,0.0003860663,0.04634605,0.00003821888,0.00009528192,0.000744993,0.0001290385,0.5705574,0.3763927,0.0006905815,0.0045277,0.00005554915],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3399706,0.000747176,0.6512777,0.0001779352,0.0001071232,0.00009513392,0.0009243112,0.001564218,0.005135907],"genre_scores_gemma":[0.8366783,0.000600689,0.1587161,0.00006628112,0.00009476548,0.00005355414,0.0005053621,0.0000928267,0.003192072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001653163,"threshold_uncertainty_score":0.004233181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03506741077315791,"score_gpt":0.3258209127870616,"score_spread":0.2907535020139037,"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."}}