{"id":"W4238155101","doi":"10.32920/ryerson.14664126.v1","title":"Implementation of Edge & Shape Detection Techniques and their Performance Evaluation","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Window (computing); Artificial intelligence; Enhanced Data Rates for GSM Evolution; Computer vision; Canny edge detector; Conveyor belt; Edge detection; Object (grammar); Key (lock); Image (mathematics); Image processing; Engineering","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":[0.0006686309,0.0001793542,0.0002430425,0.0001832995,0.00004597194,0.0000596228,0.00004699764,0.0003044501,0.0002004143],"category_scores_gemma":[0.000009947403,0.0001611913,0.00006718536,0.0001280657,0.0000111023,0.0001273943,0.00007682241,0.0002570585,0.000002079283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000125584,"about_ca_system_score_gemma":0.00004251986,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002322614,"about_ca_topic_score_gemma":0.000341159,"domain_scores_codex":[0.999024,0.00006955329,0.0004007159,0.0002085598,0.0001923966,0.0001047145],"domain_scores_gemma":[0.999389,0.00002120162,0.0001175484,0.000220253,0.0002255185,0.00002649369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005807812,0.00000419789,0.0002023051,0.0002542896,0.00004206637,8.186852e-8,0.0004511932,0.0009846642,0.04330565,0.000001603853,0.00004392994,0.9547042],"study_design_scores_gemma":[0.0002866359,0.00006740205,0.002717857,0.000205901,0.00004217455,0.00001619594,0.0009989063,0.07835246,0.915681,0.00002841596,0.001359989,0.0002430721],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864236,0.0003925103,0.01010067,0.000003099108,0.000797403,0.0007090916,0.000008539088,0.0002451019,0.001319941],"genre_scores_gemma":[0.9989297,0.0002571745,0.0003205559,0.000002122207,0.0002326769,0.0001626861,0.00005321957,0.00002389692,0.00001791227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9544612,"threshold_uncertainty_score":0.6573192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04482577576588766,"score_gpt":0.3053975078704769,"score_spread":0.2605717321045892,"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."}}