Compressão de Dados Livre de Perdas - CCSDS 120.0-G-2 -S
Bibliographic record
Abstract
Este relatório apresenta um sumário dos conceitos e justificativas operacionais que servem de base para os quesitos da Recomendação CCSDS, Compressão de Dados Livre de Perdas (referência [1]). Informações e ilustrações associadas que demonstram o desempenho, são também apresentadas. Este relatório apresenta um extenso tutorial que dá uma visão geral do algoritmo de Compressão de Dados Livre de Perdas do CCSDS, e tem por objetivo ajudar os leitores iniciantes, no entendimento da Recomendação.REFERÊNCIAS [1] Lossless Data Compression. Recommendation for Space Data System Standards, CCSDS 121.0-B-1. Blue Book. Issue 1. Washington, D.C.: CCSDS, May 1997. [2] Procedures Manual for the Consultative Committee for Space Data Systems. CCSDS A00.0-Y-9. Yellow Book. Issue 9. Washington, D.C.: CCSDS, November 2003. [3] Space Packet Protocol. Recommendation for Space Data System Standards, CCSDS 133.0-B-1. Blue Book. Issue 1. Washington, D.C.: CCSDS, September 2003. [4] AOS Space Data Link Protocol. Recommendation for Space Data System Standards, CCSDS 732.0-B-2. Blue Book. Issue 2. Washington, D.C.: CCSDS, July 2006. [5] Pen-Shu Yeh and Warner H. Miller. Application Guide for Universal Source Coding. NASA Technical Paper 3441. Coding Tutorial. Washington, D.C.: CCSDS, December 1993. [6] Robert F. Rice, Pen-Shu Yeh, and Warner H. Miller. Algorithms for High Speed Universal Noiseless Coding. Proceedings of the AIAA Computing in Aerospace 9 Conference, San Diego, CA, October 19-21, 1993. [7] TM Synchronization and Channel Coding. Recommendation for Space Data Systems Standards, CCSDS 101.0-B-3. Blue Book. Issue 1. Washington, D.C.: CCSDS, September 2003. [8] Pen-Shu Yeh, Robert F. Rice, and Warner H. Miller. On the Optimality of Code Options for a Universal Noiseless Coder. NASA/JPL Publication 91-2. February 1991.[9] Pen-Shu Yeh, Warner H. Miller, and Steve Hou. Overview of NASAs Lossless Compression Technology Development and Application. Milcom 95. San Diego. Nov. 1995. [10] Masud Mansuripur. Introduction to Information Theory. Prentice-Hall. Englewood Cliffs, N.J. 1987.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".