Bibliographic record
Abstract
To deal with the extremely high datarate and huge data volume generated aboard a hyperspectral satellite, lossless and lossy data compression techniques have been developed; these techniques can significantly reduce the amount of data onboard and on-ground. Chapters 4 and 5 of this book describe two near-lossless data compression techniques, referred to as successive approximation multistage vector quantization (SAMVQ) and hierarchical self-organizing cluster vector quantization (HSOCVQ), that compress hyperspectral data with a high compression ratio and restrict the compression error at the same level or even smaller than the intrinsic noise of the original data. This low-level compression error is expected to have a minor to negligible impact on ultimate applications of the data, so this kind of compression is considered to be near-lossless compression. Even so, they are still lossy compression algorithms. It is essential to assess the usability of the compressed data and to examine acceptability to users in terms of their end products and remote sensing applications. It is critical that the compression techniques preserve the information content of hyperspectral data, as a loss of information content would decrease the value of the data.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".