{"id":"W4295408928","doi":"10.3390/su141811413","title":"Computer-Vision-Based Statue Detection with Gaussian Smoothing Filter and EfficientDet","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Smoothing; Object detection; Filter (signal processing); Object (grammar); Enhanced Data Rates for GSM Evolution; Gaussian; Pattern recognition (psychology)","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":[0.00118488,0.0008093845,0.0008235077,0.001874003,0.0003550627,0.0009889398,0.001284402,0.001160346,0.001558661],"category_scores_gemma":[0.001965984,0.0004112273,0.0008351599,0.001232803,0.0004344965,0.001338253,0.0008670003,0.0006741889,0.001168253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008959732,"about_ca_system_score_gemma":0.001212082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0147829,"about_ca_topic_score_gemma":0.0200669,"domain_scores_codex":[0.999424,0.00005921503,0.00002855124,0.0002045427,0.0001855111,0.00009816974],"domain_scores_gemma":[0.9992185,0.0001808153,0.00007007833,0.0001622191,0.0003321827,0.00003622237],"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":[0.0007430161,0.000384008,0.01239527,0.000172202,0.0001931595,0.0002092514,0.0001226398,0.1242903,0.06313023,0.003629692,0.004910287,0.78982],"study_design_scores_gemma":[0.0000101972,0.0000663879,0.003893489,0.000005751783,0.00001808596,0.000120881,0.00002310046,0.9759089,0.01777931,0.0007319162,0.001420363,0.00002158049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.109746,0.0003532211,0.8812894,0.0001495,0.00008529229,0.0001228572,0.000303963,0.005503585,0.00244618],"genre_scores_gemma":[0.5604405,0.0002425585,0.4318927,0.0001482152,0.00002462632,0.00009367431,0.0009553012,0.0001982941,0.006004188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0147829,"threshold_uncertainty_score":0.02939373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006304635929988091,"score_gpt":0.2454849375365495,"score_spread":0.2391803016065614,"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."}}