{"id":"W2280444571","doi":"10.1007/978-3-642-40849-6_65","title":"A Novel Analytical Inverse Compensation Approach for Preisach Model","year":2013,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Magnetic Properties and Applications","field":"Materials Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Inverse; Compensation (psychology); Invertible matrix; Hysteresis; Preisach model of hysteresis; Computer science; Function (biology); Differential (mechanical device); Dual (grammatical number); Control theory (sociology); Magnetic hysteresis; Mathematics; Physics; Artificial intelligence; Magnetic field; Magnetization","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004727364,0.000307585,0.0003577519,0.0001969904,0.0002215031,0.0002931506,0.001024274,0.000220199,0.0001639501],"category_scores_gemma":[0.00005913118,0.000250093,0.00008687775,0.0001404487,0.0007267602,0.0001937271,0.0003725135,0.0002461717,0.00006178189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001413416,"about_ca_system_score_gemma":0.0002931208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004288302,"about_ca_topic_score_gemma":0.00001637844,"domain_scores_codex":[0.9976982,0.000007758881,0.0003951417,0.0009864393,0.0004796476,0.0004328196],"domain_scores_gemma":[0.9986354,0.000135847,0.000166635,0.0006894055,0.000240742,0.000132022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002186352,0.0001319345,0.00000299718,0.0001514331,0.000006615054,6.201723e-7,0.0005126816,0.8063212,0.04762005,0.07296176,0.0003889674,0.07187988],"study_design_scores_gemma":[0.0002171611,0.00006633609,0.000004865832,0.00004294362,0.00001561196,0.000008241524,2.914839e-7,0.9680704,0.001719277,0.02901532,0.0005294229,0.0003101359],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004113837,0.00004056727,0.9904386,0.000367893,0.0001831965,0.00105519,0.00003536226,0.00006234793,0.007405429],"genre_scores_gemma":[0.07356588,0.000005370781,0.9229208,0.0009253747,0.000265236,0.0001383773,0.00002829513,0.00003162632,0.002119077],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1617492,"threshold_uncertainty_score":0.9999951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04911154744503383,"score_gpt":0.2529581076923099,"score_spread":0.2038465602472761,"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."}}