{"id":"W4396596440","doi":"10.2139/ssrn.4814534","title":"Towards Scalable Cryogenic Quantum Dot Biasing Using Memristor-Based Dc Sources","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"","keywords":"Biasing; Memristor; Quantum dot; Optoelectronics; Scalability; Materials science; Quantum; Physics; Nanotechnology; Voltage; Computer science; Quantum mechanics","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001360312,0.0006459952,0.0006278157,0.0004466055,0.0003833042,0.0002989919,0.0005915705,0.0003653703,0.00002851325],"category_scores_gemma":[0.00005857387,0.0006417301,0.0005135554,0.0003417675,0.00006137594,0.0001329506,0.0003500335,0.009814937,0.00003365727],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003455111,"about_ca_system_score_gemma":0.002764899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004189752,"about_ca_topic_score_gemma":0.00006551513,"domain_scores_codex":[0.995071,0.0001130255,0.0007020865,0.0005420301,0.0004547774,0.003117059],"domain_scores_gemma":[0.9990428,0.00007748656,0.0002198573,0.0003889037,0.00008850085,0.0001824179],"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":[0.00003673929,0.00002104981,0.00002286841,0.0004476078,0.0003870858,0.00006358203,0.0001436269,0.9512715,0.03376919,0.002258453,0.00004356533,0.01153474],"study_design_scores_gemma":[0.0007134094,0.0001931442,0.00001279298,0.001876183,0.0005614504,0.001494518,0.0006206324,0.5817035,0.04457139,0.3649012,0.001660667,0.001691009],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8731309,0.04606437,0.0762188,0.0001242201,0.003219562,0.0002042642,0.000009164524,0.0006682985,0.0003604023],"genre_scores_gemma":[0.9949302,0.001666847,0.001300395,0.00004177894,0.001621015,0.000005859052,0.000009340861,0.0002257727,0.0001987877],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3695679,"threshold_uncertainty_score":0.9996034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02417571656556987,"score_gpt":0.2649670667842605,"score_spread":0.2407913502186906,"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."}}