{"id":"W2035403823","doi":"10.1063/1.2837106","title":"Optimization of the magnetic noise and sensitivity of giant magnetoimpedance sensors","year":2008,"lang":"en","type":"article","venue":"Journal of Applied Physics","topic":"Magnetic properties of thin films","field":"Physics and Astronomy","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Sensitivity (control systems); Giant magnetoimpedance; Noise (video); Materials science; Biasing; Magnetic field; Voltage; Anisotropy; Nuclear magnetic resonance; DC bias; Giant magnetoresistance; Acoustics; Condensed matter physics; Optoelectronics; Physics; Magnetoresistance; Optics; Electronic engineering; Computer science; Engineering","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":[0.0008085007,0.0008451137,0.0009385295,0.0004751484,0.0002313524,0.0005795239,0.0005456317,0.0007910094,0.0004201587],"category_scores_gemma":[0.002135326,0.0004729299,0.0002303485,0.0003243997,0.0004412841,0.0003762709,0.0004006281,0.00039758,0.0002492031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005258534,"about_ca_system_score_gemma":0.0002276299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006224416,"about_ca_topic_score_gemma":0.001151596,"domain_scores_codex":[0.9988528,0.0001833345,0.00006756384,0.0002250908,0.000564793,0.0001064915],"domain_scores_gemma":[0.9991857,0.0003819598,0.00009998099,0.00006453472,0.0002188504,0.00004906486],"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":[0.00005499701,0.00001404827,0.0002106561,0.00003704599,0.000005948379,0.00002846541,0.00001575266,0.0007401409,0.9974976,0.00005557079,0.0000184857,0.001321268],"study_design_scores_gemma":[0.000006169061,0.00009317293,0.0007032451,0.000003785737,0.000007872626,0.0000437827,0.00001085876,0.005072526,0.9938092,0.00003043726,0.0002119254,0.000007212047],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9699208,0.0009810118,0.02724975,0.0001822553,0.00003730469,0.00007770657,0.0001465988,0.0002244527,0.001180188],"genre_scores_gemma":[0.9736714,0.0005529918,0.02421641,0.00007500299,0.00001861889,0.00008277796,0.0001796735,0.00007066127,0.001132494],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009385295,"threshold_uncertainty_score":0.004275858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008393600617678795,"score_gpt":0.188022943269633,"score_spread":0.1796293426519542,"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."}}