{"id":"W2966042986","doi":"10.1109/jsen.2015.2450236","title":"Rényi Entropy Filter for Anomaly Detection With Eddy Current Remote Field Sensors","year":2015,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; FedDev Ontario","keywords":"Anomaly detection; Entropy (arrow of time); Thresholding; Artificial intelligence; Computer science; Pattern recognition (psychology); Mobile robot; Filter (signal processing); Raw data; Data mining; Remote sensing; Computer vision; Robot; Physics; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003061793,0.0002469578,0.0002315152,0.0001916558,0.0001071698,0.00009723434,0.0001500154,0.0001005605,0.00001475942],"category_scores_gemma":[0.000184216,0.000213075,0.00009689153,0.0001502863,0.00003615355,0.0002093812,0.00001110871,0.0005634982,0.00001459264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002271319,"about_ca_system_score_gemma":0.00003959454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001528734,"about_ca_topic_score_gemma":0.000009472353,"domain_scores_codex":[0.9987805,0.00005663336,0.000304518,0.0001884684,0.0002695246,0.0004003745],"domain_scores_gemma":[0.9990139,0.0001372546,0.0001157673,0.00021922,0.0002784647,0.0002354223],"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":[0.002489625,0.0002496893,0.008999708,0.0006300894,0.0009585078,0.0007308687,0.003812835,0.07220497,0.6425517,0.0004640352,0.09739002,0.169518],"study_design_scores_gemma":[0.003795863,0.002614703,0.001359672,0.0006783248,0.0002373698,0.007643197,0.0002466512,0.08989995,0.8355993,0.04320036,0.01307789,0.001646699],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.753179,0.00008931344,0.2440997,0.00005741006,0.001330503,0.0002257792,0.000005550109,0.000471974,0.0005407686],"genre_scores_gemma":[0.7398744,0.00003132915,0.2589585,0.00002380046,0.000997375,0.0000051481,0.000001146401,0.00007497497,0.00003328616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1930476,"threshold_uncertainty_score":0.8688946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02970163842148171,"score_gpt":0.2667234984478529,"score_spread":0.2370218600263712,"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."}}