{"id":"W1971920460","doi":"10.1016/j.msea.2009.01.055","title":"Thermomechanical fatigue of nanostructured Ti–Ni shape memory alloys","year":2009,"lang":"en","type":"article","venue":"Materials Science and Engineering A","topic":"Titanium Alloys Microstructure and Properties","field":"Materials Science","cited_by":67,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Materials science; Substructure; Annealing (glass); Shape-memory alloy; Thermomechanical processing; Alloy; Deformation (meteorology); Metallurgy; Dislocation; Composite material; Structural engineering","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":[0.000689241,0.0001807619,0.0002848458,0.0001115228,0.0001232975,0.0001766459,0.0004063099,0.00007598241,0.000344239],"category_scores_gemma":[0.0001359944,0.0001352455,0.00002190474,0.0002323031,0.0002173937,0.0003326083,0.00008878992,0.00005406404,0.00001298211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002595306,"about_ca_system_score_gemma":0.00006919115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000547233,"about_ca_topic_score_gemma":0.000001314731,"domain_scores_codex":[0.9986163,0.00001932784,0.0002922005,0.0003343135,0.0003590203,0.0003788238],"domain_scores_gemma":[0.999448,0.00001528621,0.00007417316,0.0002570353,0.0001045445,0.0001009572],"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":[0.00002237388,0.000006517432,0.00000391853,0.00003191286,0.000001645295,0.000002720829,0.0003379494,0.00005166321,0.997245,0.0008367157,0.00005417952,0.00140542],"study_design_scores_gemma":[0.0001522696,0.0001343109,0.003829263,0.0000440655,0.000007919387,0.00003514292,0.00003656768,0.0002046501,0.9950006,0.0002125978,0.0001606296,0.0001819919],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981932,0.0002070944,0.00008114414,0.0001345267,0.001002891,0.000120624,0.00001966703,0.00009877146,0.0001420528],"genre_scores_gemma":[0.9974948,0.00001709339,0.002201852,0.0001417558,0.00009178983,0.000003817166,0.000001495979,0.00001104977,0.00003636899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003825344,"threshold_uncertainty_score":0.5515153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01155077418831741,"score_gpt":0.2122221318788456,"score_spread":0.2006713576905282,"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."}}