{"id":"W4238155101","doi":"10.32920/ryerson.14664126.v1","title":"Implementation of Edge &amp; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006957378,0.0007990862,0.0005618372,0.0009638962,0.0003424553,0.0008903306,0.001812635,0.001223885,0.002966079],"category_scores_gemma":[0.002950272,0.0003347129,0.0003967949,0.0009546803,0.0002037166,0.0009979826,0.0003833718,0.0005528493,0.001670602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004293658,"about_ca_system_score_gemma":0.0004778274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001651958,"about_ca_topic_score_gemma":0.001300448,"domain_scores_codex":[0.9990104,0.0001197427,0.00006241221,0.0001820592,0.0005070096,0.000118348],"domain_scores_gemma":[0.9981893,0.0005000469,0.0001285649,0.0002835166,0.0008387838,0.00005987795],"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":[0.001474425,0.0006569314,0.003643509,0.0005651187,0.0001742039,0.000247537,0.0001436673,0.03393962,0.2953133,0.001207188,0.002997249,0.6596373],"study_design_scores_gemma":[0.00008317675,0.001505257,0.006343012,0.00003947744,0.0001245132,0.0007631741,0.00008291381,0.4105428,0.5732526,0.0004350526,0.006761716,0.0000663497],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3104097,0.00211539,0.6713105,0.0001801621,0.0001951309,0.0003292171,0.0004351006,0.01055434,0.00447045],"genre_scores_gemma":[0.4977091,0.001204098,0.4926046,0.0001206307,0.00006589285,0.0001825689,0.001203792,0.0004840616,0.006425207],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002966079,"threshold_uncertainty_score":0.009922504,"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."}}