{"id":"W2920001589","doi":"10.3791/59168","title":"Intermediate Strain Rate Material Characterization with Digital Image Correlation","year":2019,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"High-Velocity Impact and Material Behavior","field":"Materials Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Digital image correlation; Quasistatic process; Characterization (materials science); Computer science; Dynamic testing; Strain gauge; Protocol (science); Strain rate; Reliability (semiconductor); Materials science; Composite material; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003516714,0.0002069428,0.0003870307,0.0001302275,0.0000551975,0.0005439946,0.0002173639,0.00008255088,0.002748484],"category_scores_gemma":[0.00003042801,0.0001524298,0.00006974703,0.00009047631,0.00006253066,0.001862726,0.00005248872,0.00008599737,0.0004267198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009868182,"about_ca_system_score_gemma":0.00009115876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004140358,"about_ca_topic_score_gemma":1.530435e-7,"domain_scores_codex":[0.998439,0.0001084588,0.0006169814,0.0001672007,0.0003938275,0.0002744982],"domain_scores_gemma":[0.9987468,0.00002062757,0.0007492009,0.0001517403,0.0001840531,0.0001476113],"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":[0.001393827,0.0001396455,0.000646936,0.00001569966,0.00001602578,0.00002500338,0.0009565672,0.000003501974,0.9965531,0.00002906377,0.0000401728,0.0001804499],"study_design_scores_gemma":[0.002751967,0.0008258036,0.01179691,0.0001225523,0.00002781969,0.00005919412,0.0002453233,0.00003395544,0.9834543,0.00001378621,0.0004523433,0.0002160587],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940829,0.000003446581,0.002762687,0.00002751763,0.002559077,0.0002615562,0.0001719939,0.00003530981,0.00009554256],"genre_scores_gemma":[0.9985579,0.000004897966,0.0006635124,0.0000500713,0.000253317,0.000005373702,0.000129146,0.00003370721,0.0003020592],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01309882,"threshold_uncertainty_score":0.9981632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01485026337274184,"score_gpt":0.3579796109807416,"score_spread":0.3431293476079997,"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."}}