{"id":"W1980544291","doi":"10.1049/el.2013.3886","title":"Auto‐calibration of Hall effect sensors for home energy consumption monitoring","year":2014,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Magnetic Field Sensors Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Calibration; Hall effect sensor; Energy consumption; Consumption (sociology); Remote sensing; Hall effect; Energy (signal processing); Environmental science; Electrical engineering; Computer science; Engineering; Geography; Sociology; Statistics; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0001634417,0.0001296402,0.0001702583,0.00008633476,0.00002556916,0.00001550783,0.00009017716,0.00009247005,0.00001000194],"category_scores_gemma":[0.00002484082,0.0001413895,0.00006394331,0.00005355044,0.0000193965,0.00006164305,0.000007766426,0.0001096763,0.000001083346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006299043,"about_ca_system_score_gemma":0.000005533257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001113432,"about_ca_topic_score_gemma":0.000003525161,"domain_scores_codex":[0.9992764,0.00004478127,0.0001823568,0.0001293091,0.0001017779,0.0002654021],"domain_scores_gemma":[0.9995318,0.0002047028,0.00003874777,0.000180998,0.00001488289,0.00002885652],"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":[0.00001876939,0.000005468904,0.0003995445,0.0001932936,0.00002992652,3.667983e-7,0.00006430181,0.007448282,0.9790395,0.002462741,0.001870138,0.008467676],"study_design_scores_gemma":[0.0002704919,0.0002258456,0.0002633172,0.00002623002,0.00002108841,0.000002260201,9.488812e-7,0.1479241,0.8480175,0.0002890975,0.002791884,0.0001672967],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7776645,0.0002114851,0.2210154,0.000190711,0.0002080989,0.0001578483,0.000001389718,0.0004022393,0.0001483013],"genre_scores_gemma":[0.9962893,0.0000913947,0.003294939,0.00006614947,0.0001386305,0.00004643467,0.000009311573,0.00003546413,0.00002838568],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2186247,"threshold_uncertainty_score":0.5765695,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00482369409632269,"score_gpt":0.2023351015142252,"score_spread":0.1975114074179025,"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."}}