{"id":"W3213451231","doi":"10.1007/s10825-021-01817-1","title":"Nanoelectronic circuit elements based on nanoscale metal–molecular networks","year":2021,"lang":"en","type":"article","venue":"Journal of Computational Electronics","topic":"Molecular Junctions and Nanostructures","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"Compute Canada","keywords":"Materials science; Delocalized electron; Rectification; Chemical physics; Electronic circuit; Topology (electrical circuits); Nanotechnology; Fermi level; Conductance; Nanoelectronics; Fermi energy; Density functional theory; Nanoscopic scale; Molecular electronics; Molecular physics; Molecule; Voltage; Electron; Condensed matter physics; Physics; Computational chemistry; Chemistry; Quantum mechanics; Electrical engineering","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":[0.0001876556,0.0001765856,0.0002477101,0.0001724503,0.00007435524,0.00005158403,0.0001506107,0.00008581625,0.0001188183],"category_scores_gemma":[0.00003461901,0.0001757691,0.0002355969,0.0004079664,0.00001652569,0.00007348446,0.00001049805,0.0004742446,0.000004863628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002514468,"about_ca_system_score_gemma":0.0004411846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.959928e-7,"about_ca_topic_score_gemma":0.000002142728,"domain_scores_codex":[0.998483,0.00007300064,0.0004699277,0.0001299254,0.000497115,0.0003470103],"domain_scores_gemma":[0.9992388,0.00008050636,0.0001417178,0.0001378167,0.0003080475,0.00009309406],"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":[0.00001872271,0.00005425059,0.00002178272,0.0000106534,0.0001937003,0.00007834147,0.000004889572,0.9768245,0.01267483,0.004973502,0.0009296025,0.004215216],"study_design_scores_gemma":[0.001797688,0.0006869817,0.0005559063,0.00006462369,0.0001718583,0.0004759427,0.00000737762,0.8779404,0.06575897,0.02187593,0.03026221,0.0004021063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1588052,0.01003993,0.8287707,0.0002092769,0.0007367671,0.0001021568,0.000005076781,0.0000549706,0.001275986],"genre_scores_gemma":[0.9959469,0.0001430698,0.003209651,0.0004713194,0.0001131484,0.00000248167,0.00003590931,0.00003901851,0.00003847202],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8371418,"threshold_uncertainty_score":0.7167658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004170704926969944,"score_gpt":0.2016219908286634,"score_spread":0.1974512859016935,"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."}}