{"id":"W3162660941","doi":"10.1007/s00339-021-04533-6","title":"A differential model for the hysteresis in magnetic shape memory alloys and its application of feedback linearization","year":2021,"lang":"en","type":"article","venue":"Applied Physics A","topic":"Shape Memory Alloy Transformations","field":"Materials Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"National Natural Science Foundation of China","keywords":"Linearization; Hysteresis; Nonlinear system; Landau theory; Magnetic field; Ginzburg–Landau theory; Control theory (sociology); Physics; Martensite; Phase transition; Condensed matter physics; Statistical physics; Mathematics; Computer science; Materials science; Control (management); Quantum mechanics","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":[0.0000892493,0.00009311122,0.0001405569,0.00001892111,0.00007377764,0.0000315031,0.0001275772,0.00004056219,0.00003709386],"category_scores_gemma":[0.000009413057,0.0000804759,0.00003016179,0.0001576931,0.00003744547,0.00008666737,0.00004192822,0.00004789049,0.00001088389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001214051,"about_ca_system_score_gemma":0.00004216776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004528312,"about_ca_topic_score_gemma":0.00002327583,"domain_scores_codex":[0.999299,0.00001643498,0.0002261993,0.0001946231,0.0001356027,0.0001281447],"domain_scores_gemma":[0.9995065,0.0001303004,0.00007462689,0.0001902256,0.00007546094,0.0000228399],"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":[0.00002776062,0.0000756628,0.00000856092,0.0001022674,0.000004634288,5.17375e-8,0.0008853906,0.0184854,0.9571583,0.01702156,0.000005983608,0.006224415],"study_design_scores_gemma":[0.0004425893,0.000007243163,0.0004075739,0.000007359365,0.00002936963,3.107251e-7,0.00007677787,0.6997266,0.2963786,0.002850618,0.00000675416,0.00006621542],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8102071,0.00005721625,0.1886299,0.00009934424,0.00003233024,0.0006716414,0.00005016492,0.00001793054,0.0002343533],"genre_scores_gemma":[0.9982316,0.00001454435,0.001184126,0.00006766409,0.00004691169,0.0003569317,0.00004475336,0.00001444153,0.00003896229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6812412,"threshold_uncertainty_score":0.3281712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02065404465776291,"score_gpt":0.2389742827832994,"score_spread":0.2183202381255365,"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."}}