{"id":"W2462727415","doi":"10.1016/j.mssp.2016.05.003","title":"Dislocation-mediated plasticity in silicon during nanometric cutting: A molecular dynamics simulation study","year":2016,"lang":"en","type":"article","venue":"Materials Science in Semiconductor Processing","topic":"Advanced Surface Polishing Techniques","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Queen's University; National Natural Science Foundation of China; University of Strathclyde; National Science Foundation","keywords":"Materials science; Silicon; Nucleation; Molecular dynamics; Dislocation; Plasticity; Stacking; Partial dislocations; Chemical physics; Phase (matter); Crystallography; Nanoscopic scale; Nanotechnology; Composite material; Metallurgy; Computational chemistry; Thermodynamics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006133026,0.000214032,0.0002679482,0.0009252208,0.00009325177,0.0002023653,0.0004178028,0.0000764689,0.00001404246],"category_scores_gemma":[0.001448391,0.0001849716,0.00001187853,0.002111936,0.0001550777,0.001738398,0.0001118256,0.0001076684,0.00000511899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001330452,"about_ca_system_score_gemma":0.000113172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005069058,"about_ca_topic_score_gemma":0.0000446684,"domain_scores_codex":[0.9981029,0.00005062532,0.0005435549,0.0004463356,0.0003460873,0.0005104971],"domain_scores_gemma":[0.9993865,0.0001350053,0.0001164947,0.0002023278,0.00009122977,0.00006839125],"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":[0.000006617013,0.00004962005,0.02218606,0.00006261721,8.458429e-7,0.000006713306,0.0009103594,0.03772707,0.9382502,0.000004736969,8.356829e-8,0.0007950181],"study_design_scores_gemma":[0.0004820059,0.00002240785,0.03162307,0.0003428383,0.000003150072,0.000002414424,0.0004836356,0.07065962,0.8957318,0.0003259892,6.178197e-7,0.0003224237],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942315,0.00002827066,0.004633314,0.00001474863,0.0002039764,0.0004089932,0.00001336841,0.0004374881,0.00002837384],"genre_scores_gemma":[0.9987642,0.000003581448,0.00111267,0.000005057073,0.00001950184,0.00004851935,0.000002451808,0.00003944521,0.000004596004],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04251843,"threshold_uncertainty_score":0.7542922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348278633009605,"score_gpt":0.2753374699510551,"score_spread":0.2618546836209591,"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."}}