{"id":"W2802074912","doi":"10.1007/s00170-018-2098-3","title":"Improving the performance of profile grinding wheels with helical grooves","year":2018,"lang":"en","type":"article","venue":"The International Journal of Advanced Manufacturing Technology","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Grinding; Groove (engineering); Grinding wheel; Materials science; Mechanical engineering; Geometry; Engineering drawing; Composite material; Metallurgy; Engineering; Mathematics","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":[0.0001605257,0.0001135071,0.0001406157,0.0001692118,0.00008789355,0.00002013442,0.0007582816,0.00005023382,0.00001580218],"category_scores_gemma":[0.00005498801,0.00006168726,0.00003301839,0.0001105038,0.0002036569,0.0002714503,0.00009857756,0.0003275599,0.000002065022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005750665,"about_ca_system_score_gemma":0.00002208113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000139747,"about_ca_topic_score_gemma":0.000004985894,"domain_scores_codex":[0.9992129,0.000006521715,0.0002982661,0.00008281642,0.0002465607,0.0001528968],"domain_scores_gemma":[0.9992281,0.00006928538,0.000292671,0.0001621989,0.0002302506,0.00001751408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000458738,0.00003650876,0.0007501642,0.00008858844,0.000280537,0.00001283686,0.000527025,0.6324922,0.09294756,0.00141145,0.00004890954,0.2709455],"study_design_scores_gemma":[0.0004122084,0.000303898,0.000561373,0.0001458028,0.00001765095,0.0002731777,0.0002191434,0.01243436,0.9839701,0.0007972071,0.0007710127,0.0000940611],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9420751,0.0001879234,0.05642847,0.0005338552,0.0004327054,0.00008181401,0.000001824956,0.00007734714,0.0001809634],"genre_scores_gemma":[0.9744242,0.0002249024,0.02508142,0.00003308022,0.0001842062,0.000005496493,6.9291e-7,0.00002029993,0.00002569231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8910226,"threshold_uncertainty_score":0.2515534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004123549579156796,"score_gpt":0.2148567143315254,"score_spread":0.2107331647523686,"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."}}