{"id":"W2542212132","doi":"10.11159/icnnfc16.132","title":"A Facile and Effective Method for Size Sorting of Large Flake Graphene Oxide","year":2016,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Recent Advances in Nanotechnology","topic":"Graphene research and applications","field":"Materials Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Çanakkale Onsekiz Mart Üniversitesi","keywords":"Flake; Graphene; Sorting; Materials science; Oxide; Nanotechnology; Computer science; Composite material; Algorithm; Metallurgy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0006926668,0.0001291741,0.0003127777,0.000233034,0.00008829846,0.000008654935,0.0004924686,0.00007524091,0.00001671845],"category_scores_gemma":[0.001559212,0.00007676614,0.00005930414,0.0006327295,0.0003915852,0.0001602506,0.0002223729,0.0001092135,8.702663e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002943752,"about_ca_system_score_gemma":0.00001474632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002908236,"about_ca_topic_score_gemma":0.00008815154,"domain_scores_codex":[0.9987655,0.00001862045,0.0003341308,0.000346069,0.0001723731,0.0003633539],"domain_scores_gemma":[0.9983301,0.0008570695,0.0003932843,0.0001519989,0.0002349273,0.00003269063],"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":[0.0001533679,0.00006091372,0.002870929,0.00009628009,0.000006506432,6.347403e-8,0.00001166072,0.000001960644,0.8294274,0.1114808,0.00003623544,0.05585395],"study_design_scores_gemma":[0.0008317871,0.0001016531,0.0003152563,0.0002606266,0.000007576497,0.000001246271,0.00006659154,0.00002430672,0.9106097,0.07128655,0.0164104,0.00008432684],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992693,0.0009778724,0.0011295,0.003293372,0.0001570113,0.001310518,0.00007507151,0.00006012259,0.0003035362],"genre_scores_gemma":[0.9844471,0.001419356,0.01330191,0.00003436259,0.00000897769,0.0006377923,2.105013e-7,0.00001224073,0.0001381063],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0811823,"threshold_uncertainty_score":0.3130432,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008445073820677867,"score_gpt":0.3111936751676218,"score_spread":0.3027486013469439,"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."}}