{"id":"W1966545765","doi":"10.1016/j.jallcom.2008.05.023","title":"Characterization of amorphous and nanocrystalline Ti–Ni-based shape memory alloys","year":2008,"lang":"en","type":"article","venue":"Journal of Alloys and Compounds","topic":"Shape Memory Alloy Transformations","field":"Materials Science","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Nanocrystalline material; Amorphous solid; Annealing (glass); Crystallization; Volume fraction; Amorphous metal; Alloy; Nanocrystal; Thermal stability; Differential scanning calorimetry; Austenite; Grain growth; Grain size; Crystallography; Metallurgy; Microstructure; Chemical engineering; Composite material; Thermodynamics; Nanotechnology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008816308,0.0001445763,0.0002286527,0.000318312,0.0002823636,0.0003798309,0.0003823453,0.0002032095,0.001131695],"category_scores_gemma":[0.0003192861,0.0001170333,0.00009355712,0.0003216781,0.0002163018,0.0002138877,0.0001418952,0.0001870237,0.0002337883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003758268,"about_ca_system_score_gemma":0.0002898915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003587908,"about_ca_topic_score_gemma":0.006816142,"domain_scores_codex":[0.9998642,0.000005610727,0.000009763236,0.00003221,0.00006832208,0.00001988379],"domain_scores_gemma":[0.9998356,0.00002077664,0.00002650564,0.00002990848,0.00007140712,0.00001588043],"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":[0.00007647208,0.00000708401,0.0005002512,0.00001912212,0.000002531901,0.00003129734,0.00003144594,0.00007815673,0.9976658,0.00006957937,0.00004712872,0.00147116],"study_design_scores_gemma":[0.000005450588,0.00007112382,0.007701743,0.000002885458,0.000009059892,0.0001042679,0.00007390249,0.001502432,0.9885836,0.00003894752,0.001903415,0.000003246563],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948199,0.0004021617,0.001431034,0.0000248218,0.00001310073,0.00001996836,0.0003654,0.0000545858,0.002869138],"genre_scores_gemma":[0.996205,0.0001293908,0.001270013,0.000009093557,0.000002582035,0.00001205902,0.0003457907,0.00002292256,0.002003028],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003587908,"threshold_uncertainty_score":0.00713402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01698050820124491,"score_gpt":0.2195844829468776,"score_spread":0.2026039747456327,"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."}}