{"id":"W2319061455","doi":"10.7227/ijeee.50.2.7","title":"Incorporating FPAAs into Laboratory Exercises for Analogue Filter Design","year":2013,"lang":"en","type":"article","venue":"International Journal of Electrical Engineering Education","topic":"Experimental Learning in Engineering","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Alberta Innovates - Technology Futures","keywords":"Field-programmable analog array; Troubleshooting; Computer science; Filter (signal processing); Focus (optics); Analogue electronics; Computer hardware; Electronic circuit; Computer engineering; Electrical engineering; Engineering; Analog signal; Digital signal processing; Analog multiplier; Operating system","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.002022946,0.001060726,0.00063274,0.0007565921,0.0006979556,0.001791812,0.001744616,0.0009715437,0.01718128],"category_scores_gemma":[0.006069293,0.0004214347,0.0005608874,0.000427766,0.0004768828,0.001117161,0.002265006,0.001640982,0.006243741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007077057,"about_ca_system_score_gemma":0.001002704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004111144,"about_ca_topic_score_gemma":0.0009650458,"domain_scores_codex":[0.9984539,0.0003679151,0.00007518203,0.0002739587,0.0005715602,0.0002574939],"domain_scores_gemma":[0.996156,0.001361192,0.0002855391,0.0005022413,0.0007360539,0.0009589362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008869919,0.006641898,0.005842647,0.000454626,0.00003344811,0.001316363,0.00358487,0.008549669,0.2040483,0.00501526,0.0160947,0.7475312],"study_design_scores_gemma":[0.000804891,0.02072215,0.04441459,0.0006799641,0.0001353881,0.006380801,0.004291657,0.0595145,0.25093,0.02489224,0.5868429,0.0003909106],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4665287,0.0005698206,0.4480341,0.001405089,0.0008843862,0.001816368,0.0002896093,0.01034031,0.0701317],"genre_scores_gemma":[0.4745221,0.0004739126,0.4689247,0.0005265314,0.000210527,0.0009679297,0.0004850327,0.0006529048,0.05323634],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01718128,"threshold_uncertainty_score":0.05747712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006309046388655238,"score_gpt":0.2401476059650786,"score_spread":0.2338385595764234,"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."}}