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Searching through spatial relationships using the 2DR-tree.

2006· article· en· W4398864 on OpenAlexaff
Wendy Osborn, Ken Barker

Bibliographic record

VenueIMSA · 2006
Typearticle
Languageen
FieldComputer Science
TopicData Management and Algorithms
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsOptimal binary search treeTree (set theory)Computer scienceBinary search treeTernary search treeBinary treeData miningRandom binary treeSpatial analysisCurse of dimensionalityBinary dataTree traversalSpace partitioningBinary numberArtificial intelligenceInterval treeMathematicsTree structureAlgorithmStatistics

Abstract

fetched live from OpenAlex

The 2DR-tree is a novel approach for accessing spatial data, particularly when spatial relationships are defined among objects. The 2DR-tree uses nodes that are the same dimensionality as the data space. Therefore, all relationships between objects are preserved and different binary search strategies are supported. This paper presents the 2DR-tree binary search strategy. A preliminary performance evaluation identifies the advantages of the binary search strategy. KEY WORDS spatial access methods, multidimensional, hierarchical. 1

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)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0030.006
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.064
GPT teacher head0.284
Teacher spread0.220 · 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 designNot applicable
Domainnot available
GenreMethods

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

Citations4
Published2006
Admission routes1
Has abstractyes

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