{"id":"W4405848836","doi":"10.1088/1742-6596/2891/8/082027","title":"Study on predicting the stability of penetrating projectile charges via machine learning methods","year":2024,"lang":"en","type":"article","venue":"Journal of Physics Conference Series","topic":"High-Velocity Impact and Material Behavior","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"MD Precision (Canada)","funders":"","keywords":"Projectile; Stability (learning theory); Artificial intelligence; Machine learning; Computer science; Materials science","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":[0.002054956,0.0001628094,0.0003528663,0.00004614252,0.0001952844,0.0002832659,0.000283982,0.00003695352,0.0001836444],"category_scores_gemma":[0.000277914,0.00009750721,0.00009238401,0.0001713666,0.0001325823,0.0006020614,0.00008105933,0.0003805212,0.000005179767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003655123,"about_ca_system_score_gemma":0.0002412662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008266253,"about_ca_topic_score_gemma":0.00001284655,"domain_scores_codex":[0.9981609,0.0005614476,0.0005363218,0.0001557417,0.000389834,0.0001957709],"domain_scores_gemma":[0.9987745,0.0003015801,0.0004144095,0.0001662952,0.0002928493,0.00005030876],"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":[0.00008166917,0.0001308789,0.00429026,0.00008101924,0.00002611116,0.000006956117,0.02010422,0.00001841016,0.9656351,0.0006820292,0.000004655167,0.008938661],"study_design_scores_gemma":[0.0001300806,0.001418384,0.005331761,0.0001459736,0.00007520332,0.00002051022,0.00837807,0.00009972711,0.9836639,0.0005919905,0.00004446063,0.00009998163],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946738,0.00004879399,0.003892371,0.0001307782,0.0008329774,0.0002347078,0.0000204091,0.00003916331,0.0001270317],"genre_scores_gemma":[0.9980634,0.000009666246,0.001602178,0.000005143528,0.0002647608,0.000007372736,0.000001153374,0.00001484812,0.00003147748],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01802873,"threshold_uncertainty_score":0.3976229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08000338595630592,"score_gpt":0.3688377939237382,"score_spread":0.2888344079674323,"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."}}