{"id":"W4241108149","doi":"10.22215/etd/2009-09161","title":"EMC/EMI characterization using a novel 3D system on package","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Electromagnetic Compatibility and Noise Suppression","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Heritage; Library and Archives Canada","funders":"Indian National Science Academy","keywords":"EMI; Characterization (materials science); Computer science; Electromagnetic interference; Engineering; Materials science; Electronic engineering; Nanotechnology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007548427,0.0003276391,0.0003377234,0.0001784365,0.00007971992,0.00005697817,0.0001322895,0.0003425196,0.0001192502],"category_scores_gemma":[0.00001163607,0.0003197054,0.00008349127,0.0001775606,0.000004349331,0.00009065452,0.00000527268,0.0003131701,0.00003993976],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001910199,"about_ca_system_score_gemma":0.00003080997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001972575,"about_ca_topic_score_gemma":0.00005784438,"domain_scores_codex":[0.9988116,0.00002161768,0.0003615319,0.0002984355,0.0002493824,0.0002574136],"domain_scores_gemma":[0.9994299,0.00002730801,0.00007853535,0.0003444357,0.00005347149,0.00006638096],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005516579,0.00007441391,0.000006812926,0.0009185877,0.00002645119,0.000003574633,0.0002838735,0.0007944386,0.9853209,0.0004938944,0.00006345582,0.01195846],"study_design_scores_gemma":[0.001273654,0.0006209573,0.0304048,0.006057728,0.0002898605,0.00002922842,0.0004719708,0.5673687,0.3893908,0.0000565562,0.00198104,0.002054662],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9681928,0.00007114097,0.003835185,0.000004579729,0.0007943788,0.000370998,0.00003139766,0.0006408536,0.02605871],"genre_scores_gemma":[0.9914957,0.00002786193,0.001748968,0.00002245459,0.0002432475,0.00001772604,0.003303164,0.00008925293,0.003051669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.59593,"threshold_uncertainty_score":0.9999255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01045231403602739,"score_gpt":0.224421492343803,"score_spread":0.2139691783077756,"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."}}