A Case Study of International Collaboration Space Missions for Combating Global Climate Change: Strategic and Technical Perspectives
Bibliographic record
Abstract
Earth’s climate is warming at an unprecedented rate and the overwhelming evidence from scientific consensus is due to human activity based on fossil fuels. Climate mitigation is a Global Public Goods (GPG) that requires collective action for planet’s sustainability for human progress. Monitoring and accurate measurement of Essential Climate Variables (ECV), the physical, chemical, or biological variables or a group of linked variables that critically contributes to the characterization of Earth’ s climate is essential for climate change mitigation. The Global Climate Observing System (GCOS), established in 1992 with co-sponsorship from the United Nations Environment Programme (UNEP) and the World Meteorological Organization (WMO), regularly assesses the status of global climate observations and its expert panels maintain the definitions of Essential Climate Variables (ECVs). Earth observation satellites provide a vital means of obtaining observations of the Earth system from a global perspective and comparing the behavior of different parts of the globe for about sixty percent of the current set of fifty-four Essential Climate Variables. This paper presents case studies of five space missions that illustrate the critical role space-based Earth Observation (EO) plays through international collaboration in achieving the goals of global climate change mitigation, adaptation, and sustainability. The case studies explore the strategic and technical perspectives of the following space missions: 1) Copernicus Climate Change Services, (C3S), an European Union (EU) Program managed by the European Commission and implemented in partnership with the European Space Agency (ESA) 2) NISAR [NASA-ISRO Synthetic Aperture Radar] Mission, a collaborative Earth Observation (EO) space program between the national space agencies of the United States and India 3) TRISHNA, for Thermal infraRed Imaging Satellite for High-resolution Natural resources Assessment, is a bilateral collaborative program between the space agencies of France (CNES) and India (ISRO) 4) Jason-CS/ Sentinel-6 Mission that consists of two identical satellites to measure the height of the oceans, a key component of climate change studies is an international collaborative effort between ESA and NASA and 5) SWOT is a Surface Water and Ocean Topography Mission between NASA and CNES with contributions from the Canadian Space Agency (CSA) and the UK Space Agency (UKSA) to measure how water bodies on Earth change over time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".