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Record W2473775606 · doi:10.29173/cais748

Scanning Compressed Full Text Files

2013· article· en· W2473775606 on OpenAlexaffvenueabout
Carolyn Watters, Matthew Young-Lai

Bibliographic record

VenueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI · 2013
Typearticle
Languageen
FieldComputer Science
TopicAlgorithms and Data Compression
Canadian institutionsDalhousie UniversityAcadia University
Fundersnot available
KeywordsComputer scienceInverted indexCompression (physics)Data compressionIndex (typography)Data fileFile formatInformation retrievalComputer graphics (images)DatabaseWorld Wide WebSearch engine indexingArtificial intelligence

Abstract

fetched live from OpenAlex

From the 1994 CAIS Conference: The Information Industry in Transition McGill University, Montreal, Quebec. May 25 - 27, 1994.In this paper we discuss an application of compression, not with the overall goal of reducing disk space, but with the goal of extending the applicability of full text scan procedures to larger text files for use in on-line search environments. This paper presents an alternative to inverted file generation for access to full text data files of medium size, 50-200Mbytes, for which the cost of generating a full inverted file is not warranted. Full scan techniques, which are often useful in interactive situations for small files, become unacceptably slow for interactive sessions with files above 10 Mbytes and so the use of compression to reduce the quantity of data scanned is an attractive alternative. Furthermore, an index that can be used to reduce search time further to very acceptable 1-2 second times can be generated as a byproduct of the compression.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.288
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0040.017
Open science0.0040.002
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 head0.019
GPT teacher head0.233
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations0
Published2013
Admission routes3
Has abstractyes

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Same venueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSISame topicAlgorithms and Data CompressionFrench-language works237,207