{"id":"W3038389658","doi":"10.1016/j.coco.2020.100384","title":"Crack characterization of discontinuous fiber-reinforced composites by using micro-computed tomography: Cyclic in-situ testing, crack segmentation and crack volume fraction","year":2020,"lang":"en","type":"article","venue":"Composites Communications","topic":"Non-Destructive Testing Techniques","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Deutsche Forschungsgemeinschaft","keywords":"Materials science; Composite material; Thermosetting polymer; Volume fraction; Characterization (materials science); Stiffness; Fracture toughness; Fiber; Microstructure; Fracture mechanics; Fracture (geology); Toughness","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":[0.0001374604,0.0002804956,0.0001819884,0.0006419212,0.000276709,0.0002691628,0.0002517435,0.0004046398,0.001071052],"category_scores_gemma":[0.0003887626,0.0002113511,0.0001381641,0.0002299314,0.0003692598,0.0003176335,0.0001566945,0.0002633995,0.0001043649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002086076,"about_ca_system_score_gemma":0.0002249245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002350909,"about_ca_topic_score_gemma":0.006097652,"domain_scores_codex":[0.9998906,0.000007901117,0.00000651873,0.00003134949,0.00004693848,0.00001666177],"domain_scores_gemma":[0.9995329,0.0001293218,0.0000956145,0.00005201158,0.0001570545,0.000033124],"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":[0.00005759491,0.00002015892,0.001485915,0.00001972223,0.000002117989,0.00001696881,0.00004902376,0.0004854668,0.9958307,0.0000298245,0.00001342684,0.001989083],"study_design_scores_gemma":[0.000004253889,0.0001249521,0.02550446,0.000004543298,0.00001384573,0.0001274099,0.00007019789,0.0216944,0.9522228,0.00003663856,0.0001849812,0.00001149128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915692,0.000115879,0.007795971,0.00001042399,0.000003478821,0.00001046546,0.00009250049,0.0000598269,0.0003422723],"genre_scores_gemma":[0.9949954,0.00003742765,0.0045849,0.000004092276,0.000001704191,0.000009078574,0.00005764453,0.00001489175,0.0002950425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002350909,"threshold_uncertainty_score":0.004674494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02503245956597305,"score_gpt":0.2517814587679283,"score_spread":0.2267489992019552,"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."}}