{"id":"W1968782840","doi":"10.1021/nl504468a","title":"Revealing Energy Level Structure of Individual Quantum Dots by Tunneling Rate Measured by Single-Electron Sensitive Electrostatic Force Spectroscopy","year":2015,"lang":"en","type":"article","venue":"Nano Letters","topic":"Molecular Junctions and Nanostructures","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Quantum dot; Quantum tunnelling; Coulomb blockade; Scanning tunneling spectroscopy; Spectroscopy; Electron; Electrostatic force microscope; Scanning tunneling microscope; Chemistry; Atomic physics; Condensed matter physics; Molecular physics; Materials science; Nanotechnology; Physics; Atomic force microscopy; Voltage; Transistor; Quantum mechanics","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":[],"category_scores_codex":[0.0001849148,0.0003467296,0.0003747329,0.0001687859,0.00008278482,0.00006533672,0.0001900819,0.0001553421,0.000006825902],"category_scores_gemma":[0.00006353245,0.0003487885,0.00009170969,0.0003680771,0.0000557957,0.000132228,0.00002518743,0.0002478309,0.000001460249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002116074,"about_ca_system_score_gemma":0.00004989966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009358565,"about_ca_topic_score_gemma":0.00002782468,"domain_scores_codex":[0.9981295,0.0001265157,0.0003802654,0.0003269939,0.0004501936,0.0005865255],"domain_scores_gemma":[0.999329,0.00004958644,0.0001248772,0.0002551116,0.00009963463,0.0001418081],"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":[0.00004720654,0.00001017919,0.00001314628,0.0000254624,0.0001419746,0.000006190438,0.0002683369,0.004445891,0.9540471,0.00006262024,0.04067021,0.0002616197],"study_design_scores_gemma":[0.0006829617,0.0001447582,0.00003160749,0.00003727928,0.00007467702,0.00002379616,0.00006065272,0.001169387,0.9958773,0.0002166323,0.001325337,0.0003556695],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9157966,0.001140989,0.0818425,0.0002209529,0.000407652,0.0001453709,0.0002671573,0.0001365966,0.0000421233],"genre_scores_gemma":[0.9970379,0.00002501431,0.001576935,0.0008284999,0.00009614103,0.000004492932,0.0003141656,0.00008528214,0.00003159127],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08124122,"threshold_uncertainty_score":0.9998964,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01266485486354158,"score_gpt":0.2050145690050084,"score_spread":0.1923497141414668,"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."}}