{"id":"W4252268092","doi":"10.32920/14636655.v1","title":"Comprehensive Analysis of Edge Detection in Color Image Processing","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Advanced Measurement and Detection Methods","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer vision; Artificial intelligence; Edge detection; Image processing; Computer science; Image gradient; Enhanced Data Rates for GSM Evolution; Color image; 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":[0.0001531342,0.000180723,0.0005402106,0.0006852225,0.00001904207,0.00003281086,0.00007698949,0.0001832187,0.00007321758],"category_scores_gemma":[0.00003872756,0.0001969186,0.0001833713,0.001140447,0.00002281197,0.00009324009,0.00007492962,0.0003777485,8.972801e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001415839,"about_ca_system_score_gemma":0.00002787047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003507886,"about_ca_topic_score_gemma":0.0003867444,"domain_scores_codex":[0.998999,0.00005925043,0.0003845771,0.0002507672,0.0001665715,0.0001398196],"domain_scores_gemma":[0.9993775,0.00004409612,0.00008641502,0.0002188197,0.0002405956,0.00003259414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001350795,0.00001848219,0.0001679712,0.000601206,0.0003563567,0.000003511434,0.0002259197,0.5762811,0.3756634,9.585104e-7,0.000002549418,0.04666505],"study_design_scores_gemma":[0.0001854551,0.000009475439,0.006553012,0.00008255018,0.0004155479,5.785703e-7,0.0004790994,0.5447138,0.4471526,0.00005390166,0.0001207541,0.0002332591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3443969,0.0006930855,0.6525548,0.000001889404,0.0002906777,0.0001340285,0.000002870771,0.0001446274,0.001781161],"genre_scores_gemma":[0.9204066,0.00009467495,0.07933974,0.000007172485,0.00003100951,0.0000397052,0.00001909538,0.00002239317,0.00003965179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5760096,"threshold_uncertainty_score":0.8030107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04004141438078715,"score_gpt":0.3132743014543525,"score_spread":0.2732328870735654,"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."}}