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Record W2053173245 · doi:10.1002/net.21531

Minisum multipurpose trip location problem on trees

2013· article· en· W2053173245 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

VenueNetworks · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFacility Location and Emergency Management
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsFacility location problemType (biology)Computer scienceNode (physics)Type of serviceService (business)Tree (set theory)Mathematical optimizationFlow networkTransportation theory1-center problemFixed costOperations researchMathematicsCombinatoricsEngineeringBusiness

Abstract

fetched live from OpenAlex

Abstract We consider the problem of locating facilities of two types at nodesof a tree network. Customers may need just one type of service, or bothtypes; in the latter case, to minimize transportation costs, the customers visit facilities of both types in a single trip. Each facility incurs a fixed cost that depends on the type of the facility and the node where it is located. It is required to minimize the sum of the total transportation and fixed costs. For the problem with a single facility of each type, we present an O ( n ) exact algorithm, which improves on the algorithm presented in [2]. For the problem with multiple facilities of each type, we present an algorithm. © 2013 Wiley Periodicals, Inc. NETWORKS, Vol. 63(2), 154–159 2014

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.643
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.018
GPT teacher head0.209
Teacher spread0.191 · 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