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Record W2151534739 · doi:10.5555/1283383.1283519

Approximation algorithms for node-weighted buy-at-bulk network design

2007· article· en· W2151534739 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSymposium on Discrete Algorithms · 2007
Typearticle
Languageen
FieldComputer Science
TopicComplexity and Algorithms in Graphs
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsApproximation algorithmLogarithmNode (physics)Network planning and designComputer scienceApproximation theoryConstant (computer programming)AlgorithmMathematicsMathematical optimizationPhysicsComputer networkMathematical analysis

Abstract

fetched live from OpenAlex

We present algorithms with poly-logarithmic approximation ratios for the buy-at-bulk network design problem in the node-weighted setting. We obtain the following results where h is the number of pairs in the input.• On O(log h) approximation for the single-sink non-uniform buy-at-bulk network design. Unless P = NP this ratio is tight up to constant factors.• An O(log4h) approximation for the multi-commodity non-uniform buy-at-bulk network design problem.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.276
Teacher spread0.243 · 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