{"id":"W2980885301","doi":"10.5539/jmsr.v8n4p37","title":"Graphene Growth and Characterization: Advances, Present Challenges and Prospects","year":2019,"lang":"en","type":"article","venue":"Journal of Materials Science Research","topic":"Graphene research and applications","field":"Materials Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Graphene; Materials science; Nanotechnology; Characterization (materials science); Microelectronics; Electronics; Engineering physics; Electrical engineering; Physics","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":[0.00622483,0.000110598,0.000272127,0.0005398176,0.0003332111,0.0004992162,0.0006037574,0.0000427785,0.0001543235],"category_scores_gemma":[0.0002836661,0.00008029882,0.00002125659,0.0005658091,0.001146909,0.001434952,0.0003616837,0.0001703441,0.00003608383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003448064,"about_ca_system_score_gemma":0.0002897593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009303818,"about_ca_topic_score_gemma":0.000001174874,"domain_scores_codex":[0.9968669,0.0002309193,0.0003795288,0.0003675959,0.001559476,0.0005956066],"domain_scores_gemma":[0.9981489,0.0001682819,0.000202049,0.0002548139,0.0008644731,0.0003614742],"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":[0.00005689205,0.00004836663,0.00034598,0.0001462944,0.000002332407,0.000005107347,0.0002072185,3.919502e-7,0.9833423,0.01386514,0.00001035211,0.001969566],"study_design_scores_gemma":[0.0003550622,0.0004238116,0.03948546,0.000105831,0.00000269833,0.0001175036,0.0002598541,0.00000716589,0.9495195,0.008630212,0.0009929418,0.00009997994],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926679,0.004103058,0.00002308168,0.002084712,0.0002088988,0.0003988538,0.00001236482,0.00001035265,0.0004908405],"genre_scores_gemma":[0.9797428,0.01926185,0.0007142229,0.00001087007,0.0001931472,0.00001964159,8.12693e-7,0.000009704663,0.00004695618],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03913948,"threshold_uncertainty_score":0.4813953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04721442206004877,"score_gpt":0.3514995657102007,"score_spread":0.3042851436501519,"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."}}