{"id":"W4407914267","doi":"10.1007/978-3-031-84356-3_9","title":"On Handling AI Tasks in CPU with Low Latency and High Performance","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Latency (audio); Operating system; Embedded system; Parallel computing; Telecommunications","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":[0.0006032408,0.0005041829,0.000499248,0.001214641,0.0001727059,0.0005120742,0.002632205,0.0002618089,0.000004637165],"category_scores_gemma":[0.00006513303,0.0004174437,0.00003083045,0.0007424464,0.0005301193,0.0007190509,0.001394173,0.001018303,0.00000680539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002098268,"about_ca_system_score_gemma":0.0004674678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008821246,"about_ca_topic_score_gemma":0.000137137,"domain_scores_codex":[0.9967722,0.00002449279,0.0003883897,0.001582887,0.0006640073,0.0005679856],"domain_scores_gemma":[0.9978089,0.0004168639,0.0001731635,0.001381833,0.0001227546,0.00009650605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001900304,0.00003347007,0.0005886176,0.00009822498,0.000006749895,0.0001399818,0.0003754249,0.006382771,0.00002317491,0.02435113,0.00005529925,0.9679261],"study_design_scores_gemma":[0.0006941161,0.001109565,0.002186769,0.007009719,0.00000984531,0.00009341408,1.269895e-7,0.8595664,0.004805129,0.1228749,0.0003912,0.001258782],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003020697,0.000126319,0.9932936,0.0006353533,0.0004247395,0.0003423832,0.000008711961,0.0002312689,0.001916879],"genre_scores_gemma":[0.5932561,0.00009238294,0.4044839,0.001820375,0.00007991048,0.00001558053,0.000007789127,0.00002322674,0.0002208519],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9666674,"threshold_uncertainty_score":0.9998277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007400626216228475,"score_gpt":0.2264062445350989,"score_spread":0.2190056183188705,"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."}}