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Record W2096437618 · doi:10.1142/s0219265902000513

STATION LAYOUTS IN THE PRESENCE OF LOCATION CONSTRAINTS

2002· article· en· W2096437618 on OpenAlexafffund
Prosenjit Bose, Evangelos Kranakis, Christos Kaklamanis, Lefteris M. Kirousis, Danny Kriz̧anc, David Peleg

Bibliographic record

VenueJournal of Interconnection Networks · 2002
Typearticle
Languageen
FieldComputer Science
TopicComputational Geometry and Mesh Generation
Canadian institutionsCarleton University
FundersMitacs
KeywordsCenter (category theory)Base stationRADIUSPoint (geometry)PlanarTotal stationComputer scienceSIGNAL (programming language)Cover (algebra)WirelessTelecommunicationsMetro stationElectrical engineeringComputer networkGeodesyGeometryGeographyMathematicsEngineeringComputer graphics (images)Transport engineering

Abstract

fetched live from OpenAlex

In wireless communication, the signal of a typical broadcast station is transmitted from a broadcast center p and reaches objects at a distance, say, r from it. In addition there is a radius r0, r0 < r, such that the signal originating from the center of the station is so strong that human habitation within distance r0 from the center p should be avoided. In other words, points within distance r0 from the station comprise a hazardous zone. We consider the following station layout proble: Cover a given planar region that includes a collection of buildings with a minimum number of stations so that every point in the region is within the reach of a station, while at the same time no interior point of any building is within the hazardous zone of a station. We give algorithms for computing such station layouts in both the one- and two-dimensional cases.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.022
GPT teacher head0.247
Teacher spread0.225 · 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 designTheoretical or conceptual
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

Citations1
Published2002
Admission routes2
Has abstractyes

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