{"id":"W2990900723","doi":"10.1021/acsnano.9b07637","title":"Detecting and Directing Single Molecule Binding Events on H-Si(100) with Application to Ultradense Data Storage","year":2019,"lang":"en","type":"article","venue":"ACS Nano","topic":"Molecular Junctions and Nanostructures","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; University of Alberta; Alberta Innovates - Technology Futures","keywords":"Scanning tunneling microscope; Materials science; Nanotechnology; Dangling bond; Quantum tunnelling; Atomic units; Lithography; Characterization (materials science); Silicon; Molecule; Molecular electronics; Computer data storage; Hydrogen storage; Optoelectronics; Chemistry; Computer science; Physics; Computer hardware","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":[],"consensus_categories":[],"category_scores_codex":[0.00008953913,0.0001306769,0.000110848,0.0001138075,0.00008904639,0.00003815109,0.000145265,0.00005079703,0.000006083348],"category_scores_gemma":[0.00003595153,0.0001193136,0.00001125916,0.0002665146,0.000005735378,0.0001015369,0.000056134,0.0001079244,0.00003637544],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000453789,"about_ca_system_score_gemma":0.0000056011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000220534,"about_ca_topic_score_gemma":0.00001810091,"domain_scores_codex":[0.9992648,0.00001528003,0.0001086698,0.0002948575,0.0001316073,0.0001847904],"domain_scores_gemma":[0.9993658,0.00004208435,0.00003001318,0.0004874112,0.00001574773,0.00005892725],"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":[0.00001405984,0.00001005184,0.001063798,0.00002748468,0.00002496618,0.000002939412,0.0001019435,0.004217166,0.9823024,0.00001442383,0.00008155281,0.01213918],"study_design_scores_gemma":[0.000760208,0.0003302702,0.004313568,0.0001997738,0.00005147876,0.00008079431,0.0001947309,0.01033573,0.9764171,0.00002320118,0.00669033,0.0006027966],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9914528,0.00007145442,0.007200173,0.00002098798,0.0001477476,0.0002774677,0.00001232806,0.000144484,0.0006725405],"genre_scores_gemma":[0.9985605,0.000005085456,0.001190906,0.00004442529,0.00003973701,0.0000109245,0.00002625429,0.0000417006,0.00008049925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01153638,"threshold_uncertainty_score":0.4865466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009267750685793451,"score_gpt":0.2117619503529813,"score_spread":0.2024941996671878,"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."}}