{"id":"W2996822399","doi":"10.1088/1742-6596/1419/1/012003","title":"Modeling static microstructure of shape memory alloy via Legendre wavelets collocation method","year":2019,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"Shape Memory Alloy Transformations","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Legendre wavelet; Microstructure; Legendre polynomials; Shape-memory alloy; Collocation method; Wavelet; Collocation (remote sensing); Alloy; Boundary value problem; SMA*; Mathematical analysis; Materials science; Mathematics; Computer science; Composite material; Wavelet transform; Differential equation; Algorithm; Discrete wavelet transform","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":[0.0005159104,0.0001797896,0.0004514078,0.00008253001,0.00007779233,0.00007902272,0.0003840782,0.00007116519,0.0005781537],"category_scores_gemma":[0.00003511989,0.00015626,0.0001190491,0.0001817331,0.00008122135,0.001524411,0.00003685301,0.0002133701,0.00003314422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004631584,"about_ca_system_score_gemma":0.0004030449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001593369,"about_ca_topic_score_gemma":0.00001043194,"domain_scores_codex":[0.9983267,0.0001371388,0.0007207491,0.0001501675,0.0004559407,0.000209304],"domain_scores_gemma":[0.9981116,0.00007709778,0.0005485116,0.0002305405,0.0009578739,0.00007437694],"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":[0.00008689505,0.00003571367,0.0000262648,0.0001589663,0.00002955022,0.000001947651,0.003501202,0.04942623,0.9380254,0.002902398,0.00001404318,0.005791351],"study_design_scores_gemma":[0.000483529,0.0002043499,0.000180637,0.0001390666,0.00006301251,0.00008233915,0.002232628,0.08536,0.9044072,0.006642201,0.00002634848,0.0001786584],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8197254,0.00003320608,0.1791651,0.0002007134,0.0004553464,0.0001768005,0.0000347962,0.00001317442,0.0001954248],"genre_scores_gemma":[0.9646828,0.00002286519,0.03503707,0.00006425066,0.0000900832,0.000002557438,0.000008024908,0.00001770326,0.00007463396],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1449574,"threshold_uncertainty_score":0.6372099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02202832228608063,"score_gpt":0.2706371606917684,"score_spread":0.2486088384056878,"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."}}