{"id":"W4415871439","doi":"10.1021/acsaelm.5c01550","title":"Molybdenum Oxide Artificial Synapse: Enabling Cognitive Learning, Image Recognition, and Denoising","year":2025,"lang":"en","type":"article","venue":"ACS Applied Electronic Materials","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Indian Institute of Space Science and Technology","keywords":"Neuromorphic engineering; Image quality; Noise (video); Pattern recognition (psychology); Noise reduction; Image processing; Convolutional neural network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004470119,0.0001198047,0.0001207113,0.00007869742,0.00008755934,0.0001626091,0.0003417626,0.0002927958,0.0002473107],"category_scores_gemma":[0.0001200244,0.00007904226,0.0001367308,0.00007353613,0.0001085522,0.0001777006,0.0001562914,0.000174061,0.00008208643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001445268,"about_ca_system_score_gemma":0.0001243243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005779631,"about_ca_topic_score_gemma":0.001132379,"domain_scores_codex":[0.9999726,0.000001690397,0.000001432975,0.000005667006,0.00001423721,0.000004454252],"domain_scores_gemma":[0.9999774,0.00000355292,0.000006214007,0.000003240102,0.000005653959,0.000004097982],"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":[0.00003822889,0.00001549204,0.0002877293,0.00007351908,0.000009961261,0.0001380467,0.00001234868,0.002662211,0.9853716,0.0009715715,0.0001358176,0.0102834],"study_design_scores_gemma":[0.00001861998,0.0002787519,0.002453916,0.00001210232,0.00003087614,0.0003993988,0.00001633194,0.1089349,0.8829043,0.0004810296,0.004455265,0.0000146401],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9589465,0.001218414,0.03586198,0.0001657631,0.00008899475,0.00001903196,0.00008052498,0.0003199196,0.003298929],"genre_scores_gemma":[0.9809992,0.0004203839,0.01689592,0.00003094045,0.000006739366,0.000009948631,0.00003822877,0.00001192948,0.001586652],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005779631,"threshold_uncertainty_score":0.001149237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009695196428042374,"score_gpt":0.2306145475110443,"score_spread":0.220919351083002,"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."}}