{"id":"W2737503697","doi":"","title":"Ambient RD energy harvester synthesis","year":2016,"lang":"en","type":"preprint","venue":"INRIA a CCSD electronic archive server","topic":"Energy Harvesting in Wireless Networks","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Rectenna; Rectification; Energy harvesting; Rectifier (neural networks); Computer science; Power (physics); Energy (signal processing); Electrical engineering; Point (geometry); Radio frequency; Electronic engineering; Tracking (education); Engineering; Telecommunications; Voltage; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002676502,0.0004767971,0.0005156123,0.0003006152,0.0002996786,0.001136752,0.0005556735,0.0006165407,0.04136669],"category_scores_gemma":[0.0005065278,0.0002229529,0.0005053966,0.0003029047,0.0003435722,0.0008578688,0.0009047583,0.0006749041,0.007734741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000499933,"about_ca_system_score_gemma":0.0002374292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003001524,"about_ca_topic_score_gemma":0.0003951915,"domain_scores_codex":[0.9998876,0.00001586902,0.000003641686,0.00003047167,0.00005419273,0.000008078433],"domain_scores_gemma":[0.9999059,0.00002505015,0.000004258124,0.00002754922,0.00002959134,0.000007603643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002849524,0.0001118627,0.0005431088,0.0009277627,0.0001162077,0.0003367379,0.0002834105,0.2024596,0.2042855,0.2984317,0.03958587,0.2526335],"study_design_scores_gemma":[0.00006281894,0.0001045519,0.0007433111,0.000107713,0.00005307713,0.0002458592,0.00008332013,0.5739309,0.09654362,0.1355988,0.1924702,0.00005586263],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0260436,0.001989179,0.6894988,0.001114106,0.001318061,0.0001303233,0.0007957961,0.002833726,0.2762764],"genre_scores_gemma":[0.5236848,0.002276526,0.1534917,0.0005487177,0.0006979134,0.0002319209,0.001221391,0.002101529,0.3157455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04136669,"threshold_uncertainty_score":0.1383854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006803823852062138,"score_gpt":0.1932564014530984,"score_spread":0.1864525776010362,"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."}}