MétaCan
Menu
Back to cohort
Record W2154954197 · doi:10.1109/infcom.1993.253405

Parallel searching techniques for routing table lookup

2002· article· en· W2154954197 on OpenAlexaff
Deborah Knox, S. Panchanathan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAlgorithms and Data Compression
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComputer scienceAlphanumericLookup tableImplementationRouting tableRobustness (evolution)Table (database)Routing (electronic design automation)Parallel computingAlgorithmData miningEmbedded systemProgramming languageRouting protocol

Abstract

fetched live from OpenAlex

The routing table lookup function is described, and recent research is reviewed, focusing on multiprocessor implementations. An implementation of the PATRICIA (practical algorithm to retrieve information coded in alphanumeric) radix searching algorithm on a linear pipelined array of processors is proposed. The implementation is described in detail and then evaluated in terms of the technology requirements, system performance, and robustness.>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.009

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.276
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicAlgorithms and Data CompressionFrench-language works237,207