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Record W7020653240

Lossless Data Compression . Green Book. Issue 3. April 2013.CCSDS 120.0-G-3

2013· article· en· W7020653240 on OpenAlexfundno aff

Bibliographic record

VenueBiblioteca Digital da Memória Científica do INPE (National Institute for Space Research) · 2013
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Data Compression Techniques
Canadian institutionsnot available
FundersNuclear PhysicsNational Institute of Information and Communications TechnologyJapan Aerospace Exploration AgencyCanadian Space AgencyCenter for Long-Term Cybersecurity, University of California BerkeleyGeo-Informatics and Space Technology Development AgencyIran Telecommunication Research CenterEuropean Space AgencyAgenzia Spaziale ItalianaEuropean Organization for the Exploitation of Meteorological SatellitesKorea Aerospace Research InstituteNational Commission for Science and TechnologyTürkiye Bilimsel ve Teknolojik Araştırma KurumuNational Oceanic and Atmospheric AdministrationNational Space OrganizationChina National Space AdministrationBelgian Federal Science Policy OfficeChinese Academy of SciencesU.S. Geological SurveyIndian Space Research OrganisationCentre National d’Etudes SpatialesCommonwealth Scientific and Industrial Research OrganisationNational Aeronautics and Space Administration
KeywordsNucleofectionGestational periodTSG101DysgeusiaDiafiltrationHyporeflexiaHemopericardiumLiquationTriacetin
DOInot available

Abstract

fetched live from OpenAlex

This Report presents a summary of the key operational concepts and rationale underlying the requirements for the CCSDS Recommended Standard for Lossless Data Compression.Supporting performance information along with illustrations are also included. This Report also provides a broad tutorial overview of the CCSDS Lossless Data Compression algorithm and is aimed at helping first-time readers to understand the Recommended Standard. The current issue updates material affected by revision of the Lossless Data Compression Blue Book.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesScholarly communication, Open science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.493
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.007
Science and technology studies0.0010.001
Scholarly communication0.0040.024
Open science0.0090.009
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.124
GPT teacher head0.390
Teacher spread0.265 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueBiblioteca Digital da Memória Científica do INPE (National Institute for Space Research)Same topicAdvanced Data Compression TechniquesFrench-language works237,207