{"id":"W2981311343","doi":"10.1088/2399-1984/ab4eff","title":"Top-down bottom-up graphene synthesis","year":2019,"lang":"en","type":"article","venue":"Nano Futures","topic":"Graphene research and applications","field":"Materials Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal General Hospital; Ballard Power Systems (Canada); McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Graphene; Nanotechnology; Materials science; Scale (ratio); Computer science; 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":[0.0001586825,0.000842558,0.0007554492,0.001032841,0.0007279547,0.0006394343,0.000775707,0.0006897078,0.00503749],"category_scores_gemma":[0.000356275,0.000353084,0.0006373401,0.000833608,0.0004219802,0.0007896278,0.001338167,0.001812435,0.004563492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004899379,"about_ca_system_score_gemma":0.0005750977,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000882561,"about_ca_topic_score_gemma":0.002504198,"domain_scores_codex":[0.9996087,0.00002439542,0.0000159989,0.00008626085,0.0001805163,0.00008413831],"domain_scores_gemma":[0.9999114,0.00001593352,0.000009348251,0.00003007849,0.00002029156,0.00001299763],"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":[0.0001041945,0.0001028135,0.0002436737,0.002236658,0.0001038803,0.0005274863,0.0001819243,0.003183594,0.8337273,0.01829138,0.01557781,0.1257194],"study_design_scores_gemma":[0.0000245493,0.0002123375,0.0006573127,0.0001442455,0.0000646714,0.0004824009,0.000083962,0.006713935,0.8231308,0.009756763,0.1586161,0.0001129367],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2597902,0.05170667,0.4252677,0.003236262,0.003793063,0.001522694,0.01090212,0.01172929,0.2320521],"genre_scores_gemma":[0.7329068,0.0238897,0.2112366,0.001157662,0.0002084312,0.0008274645,0.003906704,0.0006768257,0.02518979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00503749,"threshold_uncertainty_score":0.01685208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00976335769739609,"score_gpt":0.2599752133476541,"score_spread":0.250211855650258,"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."}}