{"id":"W1943603643","doi":"10.1146/annurev-conmatphys-031214-014529","title":"Spectroscopic Imaging of Strongly Correlated Electronic States","year":2016,"lang":"en","type":"article","venue":"Annual Review of Condensed Matter Physics","topic":"Physics of Superconductivity and Magnetism","field":"Physics and Astronomy","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canadian Institute for Advanced Research","funders":"Division of Materials Research; Materials Research Science and Engineering Center, Harvard University; University of Illinois at Urbana-Champaign; Princeton University; W. M. Keck Foundation; U.S. Department of Energy; Princeton Center for Complex Materials; National Science Foundation","keywords":"Quasiparticle; Scanning tunneling microscope; Superconductivity; Cuprate; Condensed matter physics; Characterization (materials science); Electronic structure; Materials science; Physics; Nanotechnology","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001430976,0.0002365571,0.000581151,0.00003105514,0.00003358649,0.000007791046,0.0002540986,0.00002059248,0.001678939],"category_scores_gemma":[0.000005435401,0.0001808873,0.0002294815,0.0001598332,0.0001967487,0.0009637919,0.00007898308,0.0001528667,0.0001947279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002164848,"about_ca_system_score_gemma":0.000135386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008032493,"about_ca_topic_score_gemma":4.6814e-7,"domain_scores_codex":[0.9985745,0.00007285266,0.0004568986,0.0002643157,0.0002332489,0.0003981981],"domain_scores_gemma":[0.9988278,0.0001273435,0.000320197,0.0004119548,0.0002512916,0.00006137447],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008968531,0.001491641,0.02969318,0.007381817,0.001018104,0.000004992414,0.001157858,0.00001189801,0.3522595,0.3630997,0.03485744,0.2089342],"study_design_scores_gemma":[0.004367069,0.0006384648,0.00435977,0.01953269,0.0008513537,0.000005141621,0.0007541887,0.00004135915,0.3524252,0.5931933,0.02217427,0.00165719],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8683031,0.03069494,0.05892326,0.005059759,0.0005647104,0.001739867,0.001424132,0.0001105996,0.03317962],"genre_scores_gemma":[0.9974048,0.001417609,0.00007311482,0.0003146465,0.000115424,0.00001750403,0.00004471806,0.00002942749,0.0005827646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2300936,"threshold_uncertainty_score":0.9992337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00502616912411715,"score_gpt":0.2422754270914751,"score_spread":0.2372492579673579,"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."}}