{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003337579,0.001046529,0.0009180381,0.000328229,0.0001328792,0.000236113,0.0013369,0.0006567131,0.0002152305],"category_scores_gemma":[0.00009149425,0.0009650927,0.000497245,0.0001823366,0.000176665,0.00029769,0.001047152,0.001692105,0.000125388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008118319,"about_ca_system_score_gemma":0.0003150753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001450764,"about_ca_topic_score_gemma":0.003142868,"domain_scores_codex":[0.9953192,0.0003102118,0.0008118143,0.001099346,0.0005171555,0.001942251],"domain_scores_gemma":[0.9970682,0.0006768404,0.0002245997,0.001649126,0.00007710758,0.0003041842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002027023,0.0002355118,0.0008109963,0.00116687,0.003653702,0.0001784182,0.0005571675,0.6558198,0.008868827,0.2422962,0.0480654,0.03814437],"study_design_scores_gemma":[0.001857775,0.0002658908,0.00409579,0.00619406,0.000764167,0.0001307591,0.00002045613,0.1563081,0.03410223,0.1845262,0.6052121,0.006522438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3954478,0.009251026,0.5369208,0.001252662,0.007923676,0.001278417,0.0005727885,0.006513295,0.0408395],"genre_scores_gemma":[0.992393,0.001745143,0.000663746,0.0002167332,0.001431383,0.0007227153,0.000155435,0.0004446194,0.002227235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5969452,"threshold_uncertainty_score":0.99928,"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."}}