{"id":"W4401006182","doi":"10.1093/mam/ozae044.135","title":"Investigation of Stress Corrosion Cracking in CMSX-4 Turbine Blade Alloys Using Deep Learning Assisted X-ray Microscopy and Correlative Imaging Workflow","year":2024,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"High Temperature Alloys and Creep","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Fibics (Canada); McMaster University","funders":"","keywords":"Correlative; Stress corrosion cracking; Materials science; Cracking; Workflow; Blade (archaeology); Corrosion; Stress (linguistics); Metallurgy; Turbine blade; X-ray; Microscopy; Turbine; Composite material; Mechanical engineering; Computer science; Engineering; Optics; Physics; Database","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":[0.0002942858,0.0002713116,0.0002107578,0.0006600526,0.0003615551,0.0004155974,0.0004456264,0.0005868216,0.002439438],"category_scores_gemma":[0.0003234066,0.0002820437,0.0002356039,0.0003244315,0.0002741061,0.0003415799,0.0002651317,0.000368027,0.0004691095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003877963,"about_ca_system_score_gemma":0.0004820447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00350578,"about_ca_topic_score_gemma":0.007866644,"domain_scores_codex":[0.9998615,0.000006330391,0.000008154682,0.00003226975,0.00006937504,0.00002247752],"domain_scores_gemma":[0.9996951,0.00002942859,0.00005000617,0.00003607695,0.000171001,0.00001836228],"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":[0.0001653121,0.00006781207,0.01119727,0.0001989638,0.00002409319,0.0003570442,0.0002657686,0.006861125,0.9580343,0.0007935576,0.001384429,0.02065036],"study_design_scores_gemma":[0.00001523908,0.0002688537,0.07041232,0.0000490099,0.00005439418,0.0006515851,0.0005232511,0.1592874,0.7606254,0.00060769,0.007451257,0.00005359389],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.95027,0.0003925668,0.04205412,0.0001884341,0.0000513017,0.00004643637,0.001074776,0.001466056,0.004456209],"genre_scores_gemma":[0.9540252,0.0002129919,0.04058796,0.00006113265,0.00001045503,0.00003667551,0.0007438728,0.0001155601,0.004206114],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00350578,"threshold_uncertainty_score":0.00816071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007705917801242327,"score_gpt":0.2364076101303887,"score_spread":0.2287016923291464,"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."}}