{"id":"W4212852800","doi":"10.3390/bioengineering9030090","title":"Machine Learning for Shape Memory Graphene Nanoribbons and Applications in Biomedical Engineering","year":2022,"lang":"en","type":"article","venue":"Bioengineering","topic":"Graphene research and applications","field":"Materials Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"Agencia Estatal de Investigación; Basque Center for Applied Mathematics; Eusko Jaurlaritza; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Graphene; Metastability; Materials science; Graphene nanoribbons; Nanotechnology; Magnetization; Oxide; Condensed matter physics; Magnetic field; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003517054,0.0001110118,0.0001311519,0.0002797201,0.0002559887,0.0000324707,0.0001909083,0.00002636667,0.00009110597],"category_scores_gemma":[0.00004936046,0.0001186359,0.00003933866,0.0005171149,0.00003437818,0.00005807196,0.0001551753,0.0001796716,0.000002735237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004649634,"about_ca_system_score_gemma":0.00002376145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004234736,"about_ca_topic_score_gemma":0.000003997953,"domain_scores_codex":[0.9990211,0.00001620638,0.0001792915,0.0002620425,0.0001785269,0.0003428357],"domain_scores_gemma":[0.9995589,0.0001333381,0.00002611488,0.0001376467,0.00001560896,0.0001283955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008224765,0.00007293084,0.0008977975,0.00008762828,0.000007318258,0.000001968561,0.0001217847,0.02693531,0.9619551,0.007592939,0.00004006191,0.002278938],"study_design_scores_gemma":[0.001414164,0.0002639044,0.005607347,0.00003101468,0.00002190524,0.00003831204,0.0006040379,0.8176447,0.0334281,0.0005988612,0.139661,0.0006867138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9239532,0.002108691,0.07207382,0.0003202306,0.0001271655,0.0009200919,0.0001463763,0.0003045887,0.00004578607],"genre_scores_gemma":[0.993887,0.00004256652,0.004012779,0.00000954562,0.00006003786,0.001856166,0.00007267873,0.00002461844,0.000034591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.928527,"threshold_uncertainty_score":0.483783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118586397504444,"score_gpt":0.2420849571490634,"score_spread":0.230226317398619,"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."}}