{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003759635,0.0003476855,0.000397637,0.00047656,0.0002601569,0.0003534822,0.0004999916,0.0006705284,0.001403287],"category_scores_gemma":[0.001752385,0.0001748226,0.0004104712,0.0003703716,0.0004022944,0.0004223132,0.0003250089,0.0007987086,0.0002219454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000552362,"about_ca_system_score_gemma":0.0004087334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001839999,"about_ca_topic_score_gemma":0.001962707,"domain_scores_codex":[0.9999105,0.00003706399,0.000004436691,0.00001434382,0.00002436056,0.000009431918],"domain_scores_gemma":[0.9995314,0.000345538,0.00002829696,0.00002930867,0.00005098469,0.00001452254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002394229,0.00004841791,0.0008680189,0.0001158536,0.00003086442,0.00006068869,0.00002054273,0.9406913,0.002346426,0.0224229,0.0008246686,0.03254637],"study_design_scores_gemma":[0.000001623479,0.000005154694,0.00006288098,0.000004904862,9.774368e-7,0.000003545814,0.000001932763,0.9927623,0.0003553972,0.006564473,0.0002350602,0.000001677746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2097965,0.007079377,0.7641971,0.003737622,0.0002579857,0.0001225549,0.0003682506,0.001091523,0.01334911],"genre_scores_gemma":[0.855481,0.001822997,0.1389465,0.0002642843,0.00009777224,0.000163654,0.0002893947,0.00008641109,0.002847895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001839999,"threshold_uncertainty_score":0.004694521,"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."}}