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Record W4235689493 · doi:10.1017/cbo9781316588284.001

Foreword

2016· book-chapter· en· W4235689493 on OpenAlexaff
J. Ian Munro

Bibliographic record

VenueCambridge University Press eBooks · 2016
Typebook-chapter
Languageen
FieldComputer Science
TopicData Mining Algorithms and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSearch engine indexingSpace (punctuation)Raw dataComputer scienceHash functionData structureTheoretical computer scienceInformation retrievalProgramming languageOperating system

Abstract

fetched live from OpenAlex

This is a delightful book on data structures that are both time and space efficient. Space as well as time efficiency is crucial in modern information systems. Even if we have extra space somewhere, it is unlikely to be close to the processors. The space used by most such systems is overwhelmingly for structural indexing, such as B-trees, hash tables, and various cross-references, rather than for “raw data.” Indeed data, such as text, take far too much space in raw form and must be compressed. A system that keeps both data and indices in a compact form has a major advantage.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.933
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.023
GPT teacher head0.201
Teacher spread0.178 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes1
Has abstractyes

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