{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001043697,0.00009428046,0.00008043188,0.00005940303,0.0002373387,0.0000552159,0.0005305743,0.00003610769,0.00001601638],"category_scores_gemma":[0.000007833192,0.00008334147,0.0000422691,0.002042046,0.00001438985,0.0003326737,0.0002446715,0.0001151511,0.00179141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000228555,"about_ca_system_score_gemma":0.00001198967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001182441,"about_ca_topic_score_gemma":0.00001904681,"domain_scores_codex":[0.9990311,0.00002390001,0.0001409214,0.0003396292,0.0001389413,0.0003254948],"domain_scores_gemma":[0.9992042,0.00008572113,0.000041813,0.000555108,0.00003443263,0.00007877222],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000002577581,0.00001846175,0.0001628436,0.000004804111,0.000007568574,0.000007370083,0.00006189664,0.005881288,0.004023593,0.2480512,0.0916385,0.6501399],"study_design_scores_gemma":[0.0001074758,0.0000232998,0.001867255,0.000005997792,0.000002508014,0.000009720009,0.000002129446,0.1468657,0.007067995,0.06685569,0.776996,0.0001963033],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006169844,0.000126878,0.9827415,0.003238778,0.0008829269,0.0001623846,2.325198e-7,0.001829135,0.0104012],"genre_scores_gemma":[0.8980116,0.0005208082,0.08310687,0.001525707,0.003352814,0.0003886598,0.0000112968,0.0000529108,0.01302935],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8996346,"threshold_uncertainty_score":0.9989858,"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."}}