{"id":"W3214436320","doi":"10.32920/ryerson.14647959.v1","title":"Development of a Device Characterization Curve Tracer for High Power Application","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Silicon Carbide Semiconductor Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Device under test; Calibration; Voltage; Power (physics); Observational error; Characterization (materials science); Accuracy and precision; Transient (computer programming); Measurement uncertainty; Temperature measurement; Range (aeronautics); Electronic engineering; Materials science; Electrical engineering; Computer science; Engineering; Scattering parameters; Physics","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":[0.00009907892,0.0002200702,0.0003194262,0.0001296085,0.00001620195,0.00002993008,0.0002412862,0.0004129159,0.00005857707],"category_scores_gemma":[0.00003425361,0.000238857,0.00006611026,0.00011995,0.00001737298,0.00007234445,0.0001543258,0.0001984759,0.00000463528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001441461,"about_ca_system_score_gemma":0.00008050528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006827521,"about_ca_topic_score_gemma":0.00002473968,"domain_scores_codex":[0.9989625,0.000006268362,0.000453033,0.0003045846,0.0001203873,0.0001532655],"domain_scores_gemma":[0.9992046,0.00003270564,0.0001212522,0.000449709,0.0001702276,0.00002150361],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002079592,0.00001610119,0.00009354492,0.0004549459,0.00008889979,1.344854e-7,0.0004776299,0.000286346,0.983369,0.0004148808,0.00002978852,0.01476666],"study_design_scores_gemma":[0.0001297371,0.000004269842,0.005303026,0.00008553586,0.00002212846,7.230004e-7,0.000284598,0.004718912,0.987352,0.000115768,0.001687964,0.0002953477],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9049646,0.0001436246,0.09278698,0.0000350518,0.0004186853,0.0006991962,0.00004269632,0.0007411247,0.0001681011],"genre_scores_gemma":[0.962703,0.00003265774,0.03551221,0.00001723163,0.0000261182,0.0006933539,0.0009236557,0.00005406357,0.00003768011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05773849,"threshold_uncertainty_score":0.9740306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02101189584654933,"score_gpt":0.2443445663089847,"score_spread":0.2233326704624354,"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."}}