{"id":"W6966954787","doi":"10.48550/arxiv.2202.06367","title":"Information Density in Multi-Layer Resistive Memories","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Resistive touchscreen; Encoding (memory); Series (stratigraphy); Selection (genetic algorithm); Resistive random-access memory; Information theory; Simple (philosophy)","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"],"consensus_categories":[],"category_scores_codex":[0.0001159161,0.0002104578,0.0002255475,0.0002276434,0.0001186235,0.00002115408,0.0002757819,0.0001291339,0.0000502211],"category_scores_gemma":[0.0000410723,0.0002806715,0.00007934476,0.0003225388,0.00003391493,0.000404831,0.0005947892,0.0008262705,0.00002538952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003700624,"about_ca_system_score_gemma":0.00003064953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003037108,"about_ca_topic_score_gemma":0.00007562587,"domain_scores_codex":[0.9992698,0.0000547431,0.0001763999,0.0002283517,0.00005009885,0.0002205363],"domain_scores_gemma":[0.9994516,0.00006700736,0.00007955341,0.0003041337,0.00004218909,0.00005547623],"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":[0.00003691117,0.00001089152,0.001618329,0.0001101851,0.00001966098,0.0001041806,0.000493595,0.9962735,0.00009307935,0.0009776596,0.00004010821,0.0002218841],"study_design_scores_gemma":[0.001200971,0.00003570141,0.02916044,0.0001469151,0.00006706018,0.000005843698,0.002189029,0.9541836,0.004858108,0.005222145,0.001879787,0.001050371],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9294961,0.00003582585,0.06778883,0.000005729608,0.0006024064,0.0002083222,0.00002044774,0.000305735,0.001536619],"genre_scores_gemma":[0.9991999,0.00008211724,0.000340739,0.00002809668,0.00002574627,0.000001059629,0.00004484011,0.00001502075,0.000262513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06970379,"threshold_uncertainty_score":0.9999645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08759476002226692,"score_gpt":0.1986923375853969,"score_spread":0.11109757756313,"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."}}