{"id":"W2084272545","doi":"10.1109/sips.2014.6986075","title":"Algorithm and architecture for a multiple-field context-driven search engine using fully-parallel clustered associative memories","year":2014,"lang":"en","type":"preprint","venue":"","topic":"Network Packet Processing and Optimization","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Content-addressable memory; Content-addressable storage; Parallel computing; Context (archaeology); Search engine; Field (mathematics); Binary search algorithm; Search algorithm; Associative property; Reduction (mathematics); Theoretical computer science; Algorithm; Artificial intelligence; Artificial neural network; Information retrieval; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001799899,0.0003430952,0.0003961758,0.0005355829,0.0003511405,0.0005011077,0.001349856,0.0005693446,0.003591499],"category_scores_gemma":[0.000379409,0.0002322234,0.0002849781,0.0004891425,0.0001836129,0.0007518306,0.0005426924,0.0004250922,0.0007797401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004025136,"about_ca_system_score_gemma":0.001284179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002767893,"about_ca_topic_score_gemma":0.00473884,"domain_scores_codex":[0.9998934,0.0000100033,0.000009364599,0.00002599467,0.00004128605,0.00001996448],"domain_scores_gemma":[0.999878,0.00002565052,0.00001106567,0.00002132594,0.00005406585,0.000009749991],"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":[0.0003355474,0.0002138157,0.001924742,0.000285375,0.0001284406,0.0002218077,0.0001023857,0.2920403,0.0548847,0.03182353,0.005645379,0.6123939],"study_design_scores_gemma":[0.00005751303,0.0001005894,0.000336902,0.00001199875,0.00002891727,0.0001719227,0.00002500024,0.9801341,0.01085476,0.005144623,0.003118098,0.00001559982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02565551,0.0003141822,0.9691399,0.0001087426,0.0000540562,0.0001269394,0.00006880216,0.001315083,0.003216857],"genre_scores_gemma":[0.3101301,0.0002054929,0.6844042,0.0001091064,0.00002880634,0.0002585098,0.0002180157,0.00007338271,0.004572373],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003591499,"threshold_uncertainty_score":0.01201481,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02721648179549626,"score_gpt":0.2813589087105173,"score_spread":0.254142426915021,"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."}}