{"id":"W2144736372","doi":"10.1109/sensor.1997.613668","title":"A micromachined angled Hall magnetic field sensor using novel in-cavity patterning","year":2002,"lang":"en","type":"article","venue":"","topic":"Magnetic Field Sensors Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Etching (microfabrication); Materials science; Hall effect sensor; Silicon; Fabrication; Wafer; Hall effect; Optoelectronics; Surface micromachining; Magnetic field; Shadow mask; Microelectromechanical systems; Bulk micromachining; Optics; Nanotechnology; Electrical engineering; Magnet; Engineering; Physics","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007294882,0.0001830101,0.000197845,0.0001453609,0.00002556943,0.00003357571,0.0001336068,0.0001487173,0.003727454],"category_scores_gemma":[0.00004001442,0.0001915432,0.00005064679,0.0001517024,0.00001415552,0.0000641592,0.00003880035,0.0002717974,0.00003401292],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004037315,"about_ca_system_score_gemma":0.000002106371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007695819,"about_ca_topic_score_gemma":0.0003084987,"domain_scores_codex":[0.9991081,0.00001933296,0.0002593143,0.000193413,0.0001070428,0.000312826],"domain_scores_gemma":[0.9995428,0.00009876064,0.00001862493,0.0002672109,0.00001605293,0.00005657606],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000070083,0.0001046372,0.008177111,0.00014315,0.00001310904,0.00006901205,0.00092516,0.001435906,0.9718485,0.00009121244,0.001869377,0.01531575],"study_design_scores_gemma":[0.0008865255,0.0002050136,0.005814242,0.0001041739,0.00002064992,0.00009737979,0.00006406662,0.9202829,0.07041194,0.00008952967,0.001356359,0.0006672411],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9440715,0.0002065777,0.01899364,0.00017687,0.0001198939,0.0002168109,0.000002549619,0.0006065796,0.03560558],"genre_scores_gemma":[0.9442777,0.00002266319,0.05455096,0.0002989052,0.00004470074,0.000006920263,8.859092e-7,0.0000324667,0.0007647746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.918847,"threshold_uncertainty_score":0.9971833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02034957577287524,"score_gpt":0.2100471843388253,"score_spread":0.1896976085659501,"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."}}