Parameter Value Language Specification (CCSD0006 and CCSD0008) - CCSDS 641.0-B-2
Bibliographic record
Abstract
The Parameter Value Language (PVL) Recommendation defines a human-readable, machine-processable language for naming and expressing data values. This update to the first issue of the Recommendation extends the PVL character set definitions to include the G1 set of ISO 8859-1 to support use of accented characters used in many languages. It also includes some clarifications and corrections to the original issue, including preservation of statement order within an object, and it updates the document style for consistency with current CCSDS style guidelines. Changes from the previous issue are flagged with change bars in the outside margin and summarized in the Document Control section (page v).This document has been reconfirmed by the CCSDS management Council through March 2011.The purpose of this document is to establish a common Recommendation for the specification of a standard keyword value type language for naming and expressing data values. It is useful for wider audiences, but it is designed be used to interchange data in a more uniform fashion within and among Agencies participating in the Consultative Committee for Space Data Systems (CCSDS). This Recommendation provides an overview and formal syntax specification of the Parameter Value Language (PVL). Two versions of PVL are definedthe basic version (CCSD0006) and an extended character set version (CCSD0008).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.087 |
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 source (direct Gemma or distilled Codex), 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".