MétaCan
Menu
Back to cohort
Record W2078190972 · doi:10.1002/net.20451

The preemptive swapping problem on a tree

2011· article· en· W2078190972 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNetworks · 2011
Typearticle
Languageen
FieldEngineering
TopicVehicle Routing Optimization Methods
Canadian institutionsHEC MontréalGroup for Research in Decision AnalysisUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCentre interuniversitaire de recherche sur les reseaux d'entreprise, la logistique et le transportCanada Research Chairs
KeywordsVertex (graph theory)Object (grammar)Computer scienceTree (set theory)HeuristicMathematical optimizationCombinatoricsMathematicsRouting (electronic design automation)Type (biology)GraphComputer networkArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract This article considers the swapping problem on a tree. In this problem at most one object of some type is available at each vertex, and each vertex also requests at most one object of a given type. The total demand and the total supply of each object type are identical. The problem is to determine a minimum cost routing plan starting and ending at a prespecified vertex which is the depot, for a single vehicle of unit capacity and m object types, so that all vertex requests are satisfied. We consider the preemptive mode in which objects may be temporarily dropped along the way. It is shown that this problem is NP‐hard. A heuristic with a worst‐case performance ratio of 1.5 is developed. Finally, it is shown that the case where m = 1 is polynomially solvable. © 2011 Wiley Periodicals, Inc. NETWORKS, 2011

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.907
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.025
GPT teacher head0.226
Teacher spread0.201 · 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