{"id":"W4412043338","doi":"10.5121/csit.2025.151210","title":"Enhancing Frame Detection with Retrieval Augmented Generation","year":2025,"lang":"en","type":"article","venue":"","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Institut de Valorisation des Données; Compute Canada","keywords":"Computer science; Frame (networking); Information retrieval; Artificial intelligence; Computer vision; Computer network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001194246,0.001855081,0.001372011,0.002076291,0.0004967793,0.001021527,0.001885258,0.001471765,0.003059109],"category_scores_gemma":[0.004275975,0.0003782814,0.001008994,0.0009962152,0.0006540595,0.002412759,0.001425601,0.001233978,0.002096637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007349629,"about_ca_system_score_gemma":0.001025448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007459735,"about_ca_topic_score_gemma":0.008466229,"domain_scores_codex":[0.9991554,0.0001708135,0.00003657246,0.0003293942,0.0002035017,0.0001042629],"domain_scores_gemma":[0.9986439,0.0006548488,0.0000987318,0.0002604319,0.0002917383,0.00005033391],"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":[0.0007854739,0.0003603005,0.001428166,0.0002908916,0.00009352991,0.0003799066,0.0003586339,0.04703968,0.04345822,0.006897549,0.01609761,0.8828101],"study_design_scores_gemma":[0.00006605157,0.0002612806,0.000780936,0.00003606337,0.00007074806,0.0003190771,0.0001176462,0.9461289,0.03438453,0.009303169,0.008485238,0.00004625792],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0539584,0.001315288,0.9277198,0.0002526724,0.0002299512,0.0002201659,0.0006970157,0.01299369,0.002612844],"genre_scores_gemma":[0.3784133,0.0005760441,0.610075,0.0004505496,0.0001996549,0.0002300691,0.003741375,0.0007829152,0.005531102],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007459735,"threshold_uncertainty_score":0.01483262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01041523004170604,"score_gpt":0.2181113216187371,"score_spread":0.2076960915770311,"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."}}