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Record W2593159659

Base64Geo: an efficient data structure and transmission format for large, dense, scalar GIS datasets

2016· article· en· W2593159659 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

VenueComputer Science and Software Engineering · 2016
Typearticle
Languageen
FieldComputer Science
TopicData Management and Algorithms
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsScalar (mathematics)Computer scienceData structureGridString (physics)Magnitude (astronomy)Data miningTree (set theory)DatabaseAlgorithmMathematicsGeometryPhysicsCombinatorics
DOInot available

Abstract

fetched live from OpenAlex

We describe Base64Geo, a data structure and transmission format for large-scale, dense, scalar GIS datasets. Base64Geo encodes a rectangular grid of scalar GIS values as an array of strings, where each character is in the range [0 − 9A − Z a − z + /]. Each string represents the values on a specific latitude value, read west to east; the strings themselves are arranged south to north. The resulting structure gives a wire format for data transmission that is two orders of magnitude more efficient than standard GIS and a compact database structure that is searched with simple string operations. Disk dataset size is reduced by an order of magnitude over a corresponding CSV structure, and by two orders of magnitude over an indexed GIS database. Search times on the string-based Base64Geo dataset are an order of magnitude smaller than search times from a quad-tree based searcher on the same dataset.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.993
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0020.002
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 head0.013
GPT teacher head0.232
Teacher spread0.219 · 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