{"id":"W2160766682","doi":"10.1103/physrevlett.105.167006","title":"Efficient Numerical Approach to Inhomogeneous Superconductivity: The Chebyshev-Bogoliubov–de Gennes Method","year":2010,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Physics of Superconductivity and Magnetism","field":"Physics and Astronomy","cited_by":113,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Superconductivity; Computation; Physics; Context (archaeology); Kernel (algebra); Chebyshev polynomials; Polynomial; Magnetic field; Chebyshev filter; Statistical physics; Theoretical physics; Condensed matter physics; Computer science; Quantum mechanics; Mathematics; Mathematical analysis; Algorithm; Discrete mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001007702,0.0005433917,0.0009738314,0.000763253,0.0008024204,0.0007141672,0.001696726,0.00106259,0.002523183],"category_scores_gemma":[0.002244708,0.0003753386,0.0006886757,0.0007164218,0.001333544,0.001218245,0.001180585,0.001479143,0.0008431005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001029883,"about_ca_system_score_gemma":0.001527547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005514264,"about_ca_topic_score_gemma":0.005051237,"domain_scores_codex":[0.999632,0.000144273,0.00001328732,0.00002683976,0.0001355344,0.00004806075],"domain_scores_gemma":[0.9994258,0.0002542745,0.00004897865,0.0001075514,0.0001181588,0.00004527388],"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":[0.00004961106,0.00006883611,0.0004610259,0.0001050291,0.00003148065,0.0001337505,0.00009563256,0.5735676,0.005296896,0.3958866,0.001515495,0.02278804],"study_design_scores_gemma":[0.000009778999,0.000004580829,0.00003550747,0.00000349809,0.000002012925,0.00001002901,0.000003932626,0.9798357,0.0003152194,0.0190842,0.0006905457,0.000004921107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0184007,0.0003289882,0.9725598,0.0003386142,0.00008859445,0.00005757786,0.00006123265,0.0001795631,0.007985003],"genre_scores_gemma":[0.4405349,0.000867412,0.5451006,0.000267635,0.0001630274,0.0004904214,0.0001606483,0.0003757056,0.01203974],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005514264,"threshold_uncertainty_score":0.01096433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0166717021890472,"score_gpt":0.2915703275188276,"score_spread":0.2748986253297804,"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."}}