{"id":"W1980202303","doi":"10.4028/www.scientific.net/msf.519-521.1011","title":"Application of the Three-Dimensional Damage Percolation Model and X-Ray Tomography for Damage Evolution Prediction in Aluminium Alloys","year":2006,"lang":"en","type":"article","venue":"Materials science forum","topic":"Metal Forming Simulation Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Novelis (Canada); University of Waterloo","funders":"","keywords":"Materials science; Coalescence (physics); Nucleation; Void (composites); Aluminium; Alloy; Ultimate tensile strength; Tomography; Tensile testing; Percolation (cognitive psychology); Composite material; Thermodynamics; Optics","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":[0.0006281845,0.00008464024,0.0001017539,0.0001896827,0.0001180669,0.00003420468,0.0001382566,0.0000562695,0.000003426811],"category_scores_gemma":[0.00002160211,0.00007060017,0.00002317916,0.0003328872,0.0001496689,0.0004681922,0.00004256312,0.00002973072,5.390926e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007021519,"about_ca_system_score_gemma":0.00002174224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009359966,"about_ca_topic_score_gemma":0.00008807218,"domain_scores_codex":[0.9991525,0.00001099652,0.0002837671,0.0001656183,0.0002178609,0.0001692719],"domain_scores_gemma":[0.9996423,0.00001863724,0.00007089795,0.0001833118,0.00006784555,0.00001698237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005279373,0.000009933698,0.005825161,0.00002548192,6.589764e-7,6.921966e-9,0.00001681378,0.1905084,0.796498,0.007008451,0.00003880102,0.00006303838],"study_design_scores_gemma":[0.0001040312,0.00001492505,0.1424135,0.0000155596,0.000003824038,2.654269e-7,0.000005764725,0.6861038,0.158197,0.01307572,0.00001371893,0.00005190475],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8978625,0.000008377328,0.1011072,0.00003060972,0.0001578792,0.0006131326,0.0000602219,0.00009480303,0.00006532267],"genre_scores_gemma":[0.9925689,3.663417e-7,0.007263086,0.000006116065,0.00002196294,0.00009598306,0.00002097762,0.00001034988,0.00001224308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.638301,"threshold_uncertainty_score":0.2878991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006257517247464616,"score_gpt":0.2149322331563526,"score_spread":0.208674715908888,"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."}}