{"id":"W1859444933","doi":"10.1039/c5ra12225b","title":"Enhanced figure of merit in Mg<sub>2</sub>Si<sub>0.877</sub>Ge<sub>0.1</sub>Bi<sub>0.023</sub>/multi wall carbon nanotube nanocomposites","year":2015,"lang":"en","type":"article","venue":"RSC Advances","topic":"Advanced Thermoelectric Materials and Devices","field":"Materials Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saskatoon Medical Imaging; University of Saskatchewan; Regional Municipality of Waterloo; McMaster University; University of Waterloo","funders":"AUTO21 Network of Centres of Excellence; Natural Sciences and Engineering Research Council of Canada; McMaster University","keywords":"Carbon nanotube; Materials science; Nanocomposite; Figure of merit; Thermal conductivity; Scattering; Nanotube; Carbon fibers; Nanotechnology; Physics; Optoelectronics; Optics; Composite material; Composite number","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":["metaepi_narrow"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001475503,0.001630329,0.00246821,0.0006467492,0.0004083576,0.0003057163,0.001621324,0.0007150394,0.0000667944],"category_scores_gemma":[0.0005932029,0.00153318,0.0004262745,0.001635984,0.0006987669,0.001997426,0.0005493486,0.0006615733,0.0004035327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005174206,"about_ca_system_score_gemma":0.0005272641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002038591,"about_ca_topic_score_gemma":0.001159921,"domain_scores_codex":[0.9902372,0.0007502037,0.002517763,0.002370994,0.00174347,0.002380387],"domain_scores_gemma":[0.9944006,0.0006439797,0.001884515,0.00154258,0.0007512136,0.0007771568],"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":[0.0009718306,0.0005550846,0.0003014707,0.00054895,0.00007224202,0.0001021318,0.001064961,0.00130476,0.9791026,0.0002334673,0.0001267014,0.01561582],"study_design_scores_gemma":[0.003046246,0.0007358933,0.0006621641,0.0007220497,0.0001254821,0.00005232964,0.0003444384,0.0006830629,0.9875123,0.003085263,0.001225314,0.001805476],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.983005,0.01007291,0.00117119,0.0001842073,0.002093897,0.001665178,0.0002494802,0.0005677941,0.0009902683],"genre_scores_gemma":[0.9920217,0.004282494,0.001814559,0.0003759426,0.0006221154,0.0004447973,0.0001477449,0.0002600283,0.00003063281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01381035,"threshold_uncertainty_score":0.9996444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01294653378042512,"score_gpt":0.2486634751636138,"score_spread":0.2357169413831887,"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."}}