{"id":"W2557907761","doi":"10.1007/s00542-016-3223-6","title":"A stationary apparatus of magnetic abrasive finishing using a rotating magnetic field","year":2016,"lang":"en","type":"article","venue":"Microsystem Technologies","topic":"Advanced Surface Polishing Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Electromagnet; Magnetic field; Abrasive; Mechanical engineering; Materials science; Magnetic flux; Surface roughness; Multiphysics; Mechanics; Magnet; Finite element method; Engineering; Physics; Composite material; Structural engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0001513059,0.0002299173,0.0003201943,0.0002265006,0.00006336392,0.00003791006,0.0004407517,0.0002596999,0.00001515162],"category_scores_gemma":[0.0004536534,0.0001898629,0.00006132657,0.0002950716,0.000108273,0.0002895042,0.0001671926,0.00019679,0.000009675171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001081848,"about_ca_system_score_gemma":0.00002396871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003700151,"about_ca_topic_score_gemma":0.00002904963,"domain_scores_codex":[0.9987114,0.0000234144,0.0004669594,0.0002559959,0.0001653113,0.0003768927],"domain_scores_gemma":[0.9988418,0.0004499763,0.0001242099,0.0004742057,0.00008772226,0.00002209194],"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":[0.00000750826,0.000009476474,0.003680074,0.000377917,0.00001507768,0.00001421534,0.0002456587,0.0007018579,0.9438238,0.0006755501,0.0003333803,0.05011549],"study_design_scores_gemma":[0.0003235678,0.0002513963,0.0002338776,0.001430164,0.00001971356,0.00003880576,0.001681751,0.003573486,0.9858708,0.005690352,0.0004604429,0.0004256105],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8926297,0.005646039,0.096209,0.000134884,0.0001490772,0.0004285304,0.00007225251,0.00439291,0.0003376146],"genre_scores_gemma":[0.8766864,0.000143832,0.123009,0.000007526365,0.00001005251,0.00005291593,0.000001277264,0.00004451065,0.00004446927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04968988,"threshold_uncertainty_score":0.7742385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01183149785367281,"score_gpt":0.239570510044385,"score_spread":0.2277390121907121,"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."}}