{"id":"W4323310471","doi":"10.5121/csit.2023.130402","title":"LEON: Light Weight Edge Detection Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Convolutional neural network; Scratch; Artificial intelligence; Edge device; Edge detection; Feature extraction; Feature (linguistics); Artificial neural network; Separable space; Pattern recognition (psychology); Architecture; Image (mathematics); Image processing; Mathematics; Cloud computing","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":[0.0004053348,0.001148852,0.0006681192,0.0008229939,0.0002744782,0.0006357981,0.001659794,0.0008598624,0.004314258],"category_scores_gemma":[0.00110258,0.0004480213,0.0004436221,0.0005908143,0.0003525922,0.001476594,0.001374152,0.0009875218,0.001404648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005470153,"about_ca_system_score_gemma":0.0006069882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004191873,"about_ca_topic_score_gemma":0.009263881,"domain_scores_codex":[0.9997671,0.00002121691,0.00001016815,0.00007659428,0.00007822215,0.0000467035],"domain_scores_gemma":[0.9997978,0.00005324388,0.00002741947,0.00004178375,0.00006191081,0.00001797376],"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":[0.0006948311,0.0002265322,0.003288519,0.0002986526,0.0001526435,0.0002817604,0.00006349039,0.1418724,0.03444196,0.009232241,0.03057154,0.7788754],"study_design_scores_gemma":[0.00003782333,0.0001365627,0.0009967877,0.00002707926,0.00003531454,0.0001488217,0.00001676415,0.9683892,0.01790832,0.004470444,0.007810517,0.00002246417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1136156,0.002511739,0.8495248,0.0005290158,0.0003603985,0.0002406448,0.001421676,0.02054894,0.01124725],"genre_scores_gemma":[0.5224872,0.001217245,0.4441758,0.001083551,0.0001007673,0.0002638759,0.005763075,0.0006686208,0.02423993],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004314258,"threshold_uncertainty_score":0.01443261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01585596747780969,"score_gpt":0.249121444091388,"score_spread":0.2332654766135784,"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."}}