{"id":"W2408096433","doi":"10.5383/juspn.05.01.002","title":"Electromagnetic Energy and Data Transfer for a Neural Implant","year":2013,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Alberta Innovates; Alberta Innovates - Health Solutions","keywords":"Wireless power transfer; Baseband; Subthreshold conduction; Wireless; Computer science; Energy consumption; Electronic engineering; Telecommunications link; Energy (signal processing); Power (physics); Maximum power transfer theorem; Electrical engineering; Engineering; Telecommunications; CMOS; Transistor; Voltage; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0002891333,0.0002317286,0.0004641714,0.00007940867,0.0000940673,0.0002072173,0.0002896238,0.0001755715,0.000005136679],"category_scores_gemma":[0.00001856281,0.0001884741,0.00005765531,0.00008537724,0.00004591336,0.00033016,0.00004369975,0.0002440861,1.643616e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002428241,"about_ca_system_score_gemma":0.00001955354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001408286,"about_ca_topic_score_gemma":0.0001047822,"domain_scores_codex":[0.9986242,0.00006156365,0.0005643204,0.0001965107,0.0001456107,0.0004077688],"domain_scores_gemma":[0.9989253,0.0003584084,0.0000971855,0.0002530893,0.0001580964,0.0002078702],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006780277,0.00003736273,0.0007336289,0.0005689546,0.0004924604,0.0000972311,0.0003283672,0.8617941,0.005305446,0.001165423,0.06990162,0.05950758],"study_design_scores_gemma":[0.0006842322,0.0005174167,0.0006283522,0.0002800393,0.00009330105,0.002548669,0.00009094965,0.9847469,0.00003197174,0.00005677,0.01006895,0.0002524531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8161498,0.0714784,0.108302,0.0001840617,0.003106967,0.0004598754,0.00003399694,0.0001015422,0.0001832928],"genre_scores_gemma":[0.9933422,0.00387265,0.000431877,0.00007595824,0.002098967,0.00002774233,0.00001670712,0.00005675909,0.00007713822],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1771924,"threshold_uncertainty_score":0.7685752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01464472500685321,"score_gpt":0.2138691633528745,"score_spread":0.1992244383460213,"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."}}