Quantitative evaluation of hyperspectral data compressed by near lossless onboard compression techniques
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Canadian Space Agency is investigating an onboard compressor for a hyperspectral satellite using its two innovative data compression techniques. It is essential to verify the quality of the compressed data and users' acceptability in terms of their remote sensing applications. Hyperspectral data cubes acquired by hyperspectral sensors such as casi, AVIRIS, Probe-1 and Hyperion were tested. Statistical hypothesis tests were used to assess if the means and variances in each spectral band of specified zones calculated from the reconstructed data cubes are significantly different from those calculated from the original data cube. Remote sensing end products, such as red edge, chlorophyll content and spectral unmixing were used to evaluate the compressed data. Preliminary test results show that hyperspectral data compressed using the two compression techniques at compression ratio up to 30:1 are acceptable in terms of statistical tests and remote sensing end products.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it