{"id":"W2097431197","doi":"10.1109/iscas.1989.100610","title":"An efficient implementation of an edge detection algorithm","year":2003,"lang":"en","type":"article","venue":"","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; A priori and a posteriori; Algorithm; Execution time; Edge detection; Theoretical computer science; Computer engineering; Artificial intelligence; Image processing; Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0007934289,0.00008140012,0.00009868725,0.0001987697,0.00008862042,0.0000982318,0.0003759887,0.00003267453,0.0003333801],"category_scores_gemma":[0.0000381784,0.00007533441,0.00002542056,0.0005924617,0.00002619394,0.0003828003,0.00003576657,0.00005971348,0.00002174259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003869659,"about_ca_system_score_gemma":0.00009468844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009150229,"about_ca_topic_score_gemma":0.00002043638,"domain_scores_codex":[0.9985079,0.0002798816,0.0002593806,0.0003058381,0.0004437868,0.000203272],"domain_scores_gemma":[0.9989659,0.00003399048,0.0000767187,0.0005175653,0.0002645837,0.0001412656],"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":[0.000001358184,0.0002245712,0.0000460917,0.000005892555,0.000008412042,0.000002512322,0.0003614847,0.009168446,0.003161213,0.01666322,0.0000163175,0.9703405],"study_design_scores_gemma":[0.0003060899,0.0002212114,0.0007553317,7.127201e-7,0.000002161423,0.00000779043,0.0001852696,0.8598258,0.1381929,0.0002337536,0.0001894044,0.00007950515],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01591965,0.000007103246,0.9825885,0.00001864781,0.0002029459,0.0002195458,0.000002357096,0.00008705627,0.0009541446],"genre_scores_gemma":[0.3782204,0.000002070571,0.6216516,0.00002485975,0.00001577159,0.00001185199,0.000004222979,0.000005945703,0.00006331091],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.970261,"threshold_uncertainty_score":0.3650279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01761517352527483,"score_gpt":0.3342086159025628,"score_spread":0.316593442377288,"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."}}