{"id":"W4396567318","doi":"10.1016/j.cirp.2024.04.051","title":"Submerged electrochemical jet machining with in-situ gas assistance","year":2024,"lang":"en","type":"article","venue":"CIRP Annals","topic":"Advanced Machining and Optimization Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Jet (fluid); Machining; Electrochemical machining; Electrochemistry; In situ; Materials science; Mechanical engineering; Metallurgy; Environmental science; Engineering; Chemistry; Electrode; Aerospace engineering; Physical chemistry; Organic chemistry","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":[0.0001124964,0.0001327916,0.000142201,0.0001127852,0.0000300346,0.0000469208,0.00008615778,0.0000577072,0.00002292819],"category_scores_gemma":[0.0000226421,0.0001192622,0.00002971559,0.0003013738,0.00002023472,0.0001358356,0.000009175138,0.0002830448,0.00001085769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003291484,"about_ca_system_score_gemma":0.00001445217,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001267003,"about_ca_topic_score_gemma":0.00001368879,"domain_scores_codex":[0.9993132,0.00001205072,0.0001465861,0.0001815185,0.0000965707,0.0002500883],"domain_scores_gemma":[0.9997643,0.00002498299,0.00001178219,0.0001288424,0.00001915421,0.00005091294],"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":[0.0002185616,0.0001320619,0.006545912,0.001072662,0.0003394225,0.0005765964,0.003447369,0.07930477,0.686998,0.0195357,0.03523527,0.1665937],"study_design_scores_gemma":[0.0004135082,0.0001381295,0.001585034,0.0009958779,0.00002696795,0.00006373628,0.00007751946,0.1308079,0.8204551,0.005466924,0.03894328,0.001026088],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1337303,0.0129331,0.7739137,0.0004783424,0.0002294191,0.0001955961,0.000007892598,0.004209518,0.07430213],"genre_scores_gemma":[0.9926987,0.0004120574,0.00650862,0.00008032083,0.0000509096,0.00003076702,0.00001602601,0.00004441622,0.0001581877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8589684,"threshold_uncertainty_score":0.486337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01084536426725874,"score_gpt":0.2575719955802698,"score_spread":0.246726631313011,"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."}}