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Record W1991761577 · doi:10.1177/1548512913509033

Solving the Impediment Induced Variable Shape Covering Problem

2013· article· en· W1991761577 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueThe Journal of Defense Modeling and Simulation Applications Methodology Technology · 2013
Typearticle
Languageen
FieldComputer Science
TopicOptimization and Search Problems
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsComputer scienceVariable (mathematics)Circle packingConstant (computer programming)Operations researchMathematical optimizationNorm (philosophy)MathematicsGeometryLaw

Abstract

fetched live from OpenAlex

Given finite resources, organizations are in a constant struggle to satisfy conflicting demands for resource allocation. Finding the right number of response units needed to respond to an incident in a given area is one such problem. Different geographical areas have different characteristics that further complicate the problem. For example, Canada’s Arctic waters is a large area with many islands and where varying ice coverage conditions are the norm. These impediments complicate what would otherwise be a straightforward application of the Circle Packing or Circle Covering Problem. The authors propose to call such a problem the Impediment Induced Variable Shape Covering Problem and present the Incident Response Model that determines the minimum number of units needed to respond to an incident anywhere in a given Area of Interest within a predetermined response time while avoiding or accounting for impediments.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.548
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.083
GPT teacher head0.331
Teacher spread0.248 · 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