{"id":"W2969856438","doi":"10.1007/s10853-019-03927-5","title":"Modeling microstructure evolution in shape memory alloy rods via Legendre wavelets collocation method","year":2019,"lang":"en","type":"article","venue":"Journal of Materials Science","topic":"Shape Memory Alloy Transformations","field":"Materials Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Microstructure; Legendre polynomials; Legendre wavelet; Materials science; Shape-memory alloy; Collocation method; Collocation (remote sensing); Solid mechanics; Wavelet; Mechanics; Mathematical analysis; Computer science; Mathematics; Composite material; Wavelet transform; Physics; Differential equation; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.006098471,0.0002033136,0.000437133,0.000552508,0.0001981422,0.0002831238,0.0009336336,0.0001134821,0.0009833061],"category_scores_gemma":[0.0001823353,0.0001734159,0.00006923223,0.0007272727,0.0001532604,0.002570315,0.00009272571,0.0001827354,0.0001537364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004468049,"about_ca_system_score_gemma":0.0006309836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007666451,"about_ca_topic_score_gemma":0.00002074754,"domain_scores_codex":[0.9968781,0.0002623275,0.001101295,0.0003467633,0.0009344034,0.0004770876],"domain_scores_gemma":[0.9984416,0.00006285223,0.0004935277,0.0003215825,0.0005412019,0.0001392329],"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":[0.00007246371,0.0000294912,0.00005561134,0.00004080424,0.000002230847,0.000005499719,0.0006377208,0.1091299,0.8896448,0.0001991921,0.000006255457,0.0001760192],"study_design_scores_gemma":[0.000678132,0.0001055502,0.00324929,0.0001258926,0.00001622988,0.0004340165,0.0004063714,0.09579576,0.8984158,0.0005455395,0.00001553307,0.0002118728],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759577,0.00006996482,0.02037018,0.0002334994,0.002862897,0.0003318719,0.00001672555,0.00002394453,0.0001332013],"genre_scores_gemma":[0.9703737,0.000009620758,0.02928974,0.0001107687,0.000158045,0.000004858925,0.000002136595,0.00001680439,0.00003432149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01333418,"threshold_uncertainty_score":0.9999299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01343193738390262,"score_gpt":0.278562083090674,"score_spread":0.2651301457067714,"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."}}