{"id":"W2058721953","doi":"10.1109/memsys.2012.6170140","title":"High-precision dry micro-electro-discharge machining of carbon-nanotube forests with ultralow discharge energy","year":2012,"lang":"en","type":"article","venue":"","topic":"Carbon Nanotubes in Composites","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Carbon nanotube; Materials science; Electrical discharge machining; Machining; Carbon fibers; Plasma; Energy (signal processing); Nanotechnology; Engineering physics; Optoelectronics; Composite material; Metallurgy; Engineering; Physics","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":[0.0001298481,0.0001870429,0.0001688402,0.00009359985,0.0001196261,0.0002247078,0.0002250328,0.0001790585,0.0003505622],"category_scores_gemma":[0.0002170894,0.0001148078,0.0001077341,0.000091667,0.0002607872,0.0003289111,0.0002005759,0.0002980971,0.0001325389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001457134,"about_ca_system_score_gemma":0.0001006348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001542621,"about_ca_topic_score_gemma":0.0005706079,"domain_scores_codex":[0.9998876,0.000006779091,0.00000584882,0.00002571499,0.00005874476,0.00001531147],"domain_scores_gemma":[0.999882,0.00004089536,0.00003460132,0.0000201847,0.00001487632,0.000007465098],"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":[0.00001350222,0.000006305609,0.0001552614,0.00003397511,0.000001512809,0.00002029143,0.00001848647,0.0001526147,0.9955549,0.0001375898,0.00002548254,0.003880045],"study_design_scores_gemma":[0.000003414297,0.00004381922,0.001508777,0.000001901912,0.000001975487,0.0001396506,0.00001163758,0.001544683,0.9955531,0.00005005443,0.00113757,0.000003411712],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9664301,0.001080269,0.03042613,0.00006601846,0.00003261229,0.00002197969,0.00006490593,0.0001283921,0.001749605],"genre_scores_gemma":[0.9746129,0.0002809151,0.02415071,0.0000220005,0.000009269938,0.0000129199,0.00005801536,0.00001891251,0.0008344032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0003505622,"threshold_uncertainty_score":0.001172781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007263882634016351,"score_gpt":0.2262154411405183,"score_spread":0.218951558506502,"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."}}