The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL): Expected Mission Contributions
Bibliographic record
Abstract
Abstract. One of the candidate missions in the evolution of the Copernicus Space Component (CSC) is the Copernicus polaR Ice and Snow Topography ALtimeter (CRISTAL). The aim of this mission is to obtain high-resolution sea-ice thickness and land ice elevation measurements and includes the capability to determine the properties of snow cover on ice to serve Copernicus’ operational products and services of direct relevance to the Polar Regions. The evolution of the CSC is foreseen in the mid-2020s to meet priority user needs not addressed by the existing infrastructure, and to reinforce the Copernicus services by expanding the monitoring capability in the thematic domains of anthropogenic emissions (CO2), polar and agriculture/forestry/emergency. This evolution will be synergetic with the enhanced continuity of services foreseen with the next generation of the existing Copernicus Sentinels. New high-priority candidate satellite missions have been identified by the European Commission (EC) for implementation in the coming years to address gaps in current capability and emerging user needs. This paper describes the CRISTAL mission objectives, main mission requirements driving its design, the payload complement currently under development and its expected contributions to the monitoring of important components of Earth’s cryosphere.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".