{"id":"W4410308503","doi":"10.3390/s25103024","title":"RFID Sensor with Integrated Energy Harvesting for Wireless Measurement of dc Magnetic Fields","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Magnetic Field Sensors Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Manitoba Hydro","keywords":"Electrical engineering; Interfacing; Microcontroller; Ultra high frequency; Energy harvesting; Radio-frequency identification; Rectifier (neural networks); Wireless; Engineering; Wireless sensor network; Antenna (radio); Hall effect sensor; High-voltage direct current; Electronic engineering; Current sensor; Radio frequency; Computer science; Voltage; Power (physics); Direct current; Computer hardware; Telecommunications; Magnet","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002581383,0.0002839324,0.0003192438,0.0002539205,0.0001215985,0.0002648563,0.0006426313,0.0005370094,0.0007221382],"category_scores_gemma":[0.0003586253,0.0001925742,0.0002466492,0.0003836449,0.0001656428,0.0007219549,0.0003262684,0.0003043375,0.0005967231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001973658,"about_ca_system_score_gemma":0.0001375392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007944677,"about_ca_topic_score_gemma":0.0001364947,"domain_scores_codex":[0.9996514,0.00005517956,0.00002147499,0.0000864472,0.0001606206,0.00002479691],"domain_scores_gemma":[0.9998091,0.00004033549,0.00004339273,0.00003040069,0.00006635463,0.0000104563],"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":[0.0001380886,0.00005235775,0.0008888136,0.0003125257,0.00002222044,0.0001493102,0.00006061465,0.001386264,0.9357612,0.001210382,0.001107417,0.05891077],"study_design_scores_gemma":[0.00002753771,0.0007616226,0.002961228,0.00003149248,0.00006678401,0.001113296,0.00004418904,0.02591657,0.9455251,0.0004436849,0.02306617,0.00004222846],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2450999,0.008044238,0.7300235,0.0004578338,0.0007145216,0.0001965953,0.0003784814,0.003274964,0.01180995],"genre_scores_gemma":[0.8377759,0.002037593,0.1488301,0.0004923792,0.0001353968,0.000117756,0.0002850305,0.00007462019,0.01025121],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007221382,"threshold_uncertainty_score":0.002415776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009476127577004698,"score_gpt":0.2005723867232432,"score_spread":0.1910962591462385,"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."}}