{"id":"W1973542239","doi":"10.1017/s1431927606066165","title":"FIB Imaging of Deformation in Aluminum Alloys for Advanced Automotive Applications","year":2006,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Electron and X-Ray Spectroscopy Techniques","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Fibics (Canada)","funders":"","keywords":"Microanalysis; Automotive industry; Materials science; Deformation (meteorology); Aluminium; Metallurgy; Microscopy; Nanotechnology; Composite material; Engineering; Optics; Chemistry; Physics; Aerospace 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002158539,0.00020954,0.0002032081,0.0003758286,0.0004272128,0.0003281422,0.0003344405,0.0006120892,0.005835112],"category_scores_gemma":[0.0003377026,0.0002792171,0.0001523828,0.000200414,0.0002065908,0.00036357,0.0002116925,0.0002633164,0.000653886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003700817,"about_ca_system_score_gemma":0.0002302923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00119224,"about_ca_topic_score_gemma":0.002988433,"domain_scores_codex":[0.9998946,0.000008333768,0.000004987923,0.00001395566,0.0000559423,0.00002217143],"domain_scores_gemma":[0.999826,0.00006100323,0.00001281511,0.00002042696,0.0000700179,0.000009747699],"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":[0.0002134552,0.00002105305,0.0004367353,0.00008141028,0.000006059185,0.0001774046,0.00005234228,0.0004849749,0.9915122,0.0003587276,0.0009399389,0.005715705],"study_design_scores_gemma":[0.00005041362,0.000198236,0.01674992,0.00002323858,0.00001513026,0.0008392072,0.0001464089,0.01515648,0.9603949,0.0002666194,0.006141537,0.00001793664],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9474921,0.003372,0.02377467,0.0008255873,0.0002753828,0.0001157888,0.0009496807,0.0005014503,0.02269327],"genre_scores_gemma":[0.9724708,0.0006413179,0.01963975,0.0001830315,0.00004011036,0.00004953734,0.0002978213,0.00006022507,0.006617327],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005835112,"threshold_uncertainty_score":0.0195204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004408887853212925,"score_gpt":0.2674951582430158,"score_spread":0.2630862703898029,"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."}}