{"id":"W2088188786","doi":"10.1016/j.jmapro.2014.08.006","title":"Erosion modeling in abrasive slurry jet micro-machining of brittle materials","year":2014,"lang":"en","type":"article","venue":"Journal of Manufacturing Processes","topic":"Erosion and Abrasive Machining","field":"Environmental Science","cited_by":75,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto; University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Materials science; Brittleness; Slurry; Machining; Abrasive; Erosion; Kinetic energy; Surface finish; Surface roughness; Particle (ecology); Jet (fluid); Composite material; Mechanics; Metallurgy; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003756017,0.0004147739,0.0006862414,0.0003129235,0.0003455213,0.000797751,0.001221768,0.001305926,0.001089013],"category_scores_gemma":[0.0009913755,0.0004763551,0.000558348,0.0002970075,0.0003595083,0.0006152487,0.0003037827,0.0005682841,0.000186496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005731106,"about_ca_system_score_gemma":0.0005259181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00786551,"about_ca_topic_score_gemma":0.005606861,"domain_scores_codex":[0.9998654,0.00002731564,0.00000748894,0.00001806952,0.0000570384,0.00002455613],"domain_scores_gemma":[0.999574,0.0002549863,0.00005055709,0.00002443341,0.00007646404,0.00001955879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003940989,0.00004284754,0.0005404974,0.000050803,0.00001462156,0.0001004505,0.00003895632,0.9870546,0.00635977,0.001189546,0.0001103774,0.004458114],"study_design_scores_gemma":[0.000003533627,0.00001082963,0.0001966482,0.000001530543,0.000002097191,0.00001147766,0.000005123531,0.9990494,0.0005180624,0.000115059,0.00008399994,0.000002259393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6287414,0.002385168,0.3491127,0.0003985665,0.0001283829,0.0001251458,0.00011484,0.0004932972,0.01850054],"genre_scores_gemma":[0.9854404,0.0002963458,0.01046873,0.00002708658,0.00001252569,0.00002409202,0.00003635702,0.00005480691,0.003639537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00786551,"threshold_uncertainty_score":0.01563948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01700919128171613,"score_gpt":0.2427890020436459,"score_spread":0.2257798107619298,"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."}}