{"id":"W1971851471","doi":"10.1109/jsen.2012.2216521","title":"Characterization of an Optimized Off-Diagonal GMI-Based Magnetometer","year":2012,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Magnetic Field Sensors Techniques","field":"Engineering","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Magnetometer; Noise (video); Acoustics; Electromagnetic coil; Materials science; Noise measurement; Electrical impedance; White noise; Magnetic field; Nuclear magnetic resonance; Electronic engineering; Physics; Noise reduction; Engineering; Electrical engineering; Computer science; Telecommunications","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003485879,0.0001800397,0.0002536371,0.000267165,0.00004456611,0.00003767645,0.0001562998,0.0001473035,0.001158107],"category_scores_gemma":[0.00002845355,0.0001742899,0.0001039165,0.000145484,0.00004228581,0.0003275196,0.000006419395,0.0003250471,0.00001755721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003781812,"about_ca_system_score_gemma":0.00001829797,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002917323,"about_ca_topic_score_gemma":6.111845e-7,"domain_scores_codex":[0.998741,0.0001003934,0.000423826,0.00009076699,0.0002825587,0.0003614575],"domain_scores_gemma":[0.9992957,0.00005453981,0.0001136435,0.0002197762,0.0001006966,0.0002156126],"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":[0.00006361198,0.00009704882,0.0003142188,0.00004778444,0.0000313268,0.0000142812,0.0004034793,0.005947557,0.9817051,0.00001233958,0.0007503877,0.01061286],"study_design_scores_gemma":[0.0009975935,0.000239851,0.009709903,0.00005474533,0.00005442919,0.0002813262,0.00002693123,0.08107261,0.9025391,0.00002763486,0.004594461,0.0004013774],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876326,0.00006967223,0.009807631,0.00004798019,0.0008605524,0.0001254443,0.00001345115,0.0002045798,0.001238057],"genre_scores_gemma":[0.9715864,0.00007831874,0.02744555,0.00008130926,0.0005852974,0.000004147145,0.0000111296,0.00004816346,0.0001596848],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07916597,"threshold_uncertainty_score":0.999755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01183550430100399,"score_gpt":0.2295899834017344,"score_spread":0.2177544791007304,"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."}}