{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001494136,0.0002052081,0.0002850588,0.0001651858,0.00003361638,0.00001721196,0.0001403877,0.0001468642,0.00003947568],"category_scores_gemma":[0.0000920047,0.0001839244,0.00006172677,0.000269526,0.00005701137,0.00002144722,0.00001701628,0.0001370168,8.394205e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005114479,"about_ca_system_score_gemma":0.00003790234,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003223933,"about_ca_topic_score_gemma":0.0005354561,"domain_scores_codex":[0.998979,0.0000330678,0.0003191889,0.0002007237,0.0001993043,0.0002687236],"domain_scores_gemma":[0.9992266,0.0001266063,0.00004244626,0.000306352,0.0002530682,0.00004494436],"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":[0.0005708982,0.0002075609,0.00130417,0.004002321,0.0006392931,0.00006131137,0.001787986,0.05062316,0.5403743,0.02395293,0.02074137,0.3557346],"study_design_scores_gemma":[0.0009024621,0.0004573469,0.0006909693,0.0006551021,0.0001151034,0.00001046069,0.0004391519,0.1085379,0.8707006,0.0006265722,0.01636433,0.0005000361],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8856431,0.0006075566,0.08649684,0.0003466726,0.0003295391,0.0006154468,0.00002011561,0.0009163024,0.02502449],"genre_scores_gemma":[0.9826576,0.00004677258,0.01488415,0.00006605295,0.0000285171,0.00004827726,0.000004608371,0.00003431395,0.002229705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3552346,"threshold_uncertainty_score":0.7500219,"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."}}