GALENE: A proposed future satellite mission dedicated to the observation of coastal and inland aquatic ecosystems, including the monitoring of water quality and the detection of marine plastics
Bibliographic record
Abstract
Coastal and inland aquatic ecosystems are of fundamental interest to society and economy, given their tight link to urbanization and economic value creation. These ecosystems, which are continuously impacted by natural processes and human activities such as litter pollution, including marine plastics, play a significant role in the carbon cycle, and they comprise critical habitats for biodiversity. Particularly, improved knowledge of distributions of marine plastics is becoming critical. Systematic, high-quality and global observations, such as those provided by satellite remote sensing techniques, are key to understand complex aquatic systems. While multitudes of remote sensing missions have been specifically designed for studying ocean biology and biogeochemistry as well as for evaluating terrestrial environments, missions dedicated to studying critical coastal and inland aquatic ecosystems at global scale are non-existent. Thus, these ecosystems remain among the most understudied habitats on the Earth’s surface. A satellite mission called Global Assessment of Limnological, Estuarine and Neritic Ecosystems (GALENE), is proposed to ESA’s Earth Explorer Mission Idea call to respond to current and future challenges linked to coastal and inland ecosystems. The mission concept consists of a synergy of three innovative instruments, namely a hyperspectral sensor, a panchromatic camera and a polarimeter. GALENE will then provide optimized measurements of these aquatic ecosystems by enabling an adaptive spectral, spatial, multidirectional and polarimetric sampling of properties and processes in water column, benthic habitats and associated wetlands. GALENE will substantially contribute to solving global water challenges, including the marine plastic detection, water pollution and ensuring clean drinking water supply for all and protecting coastal areas and populations. In particular, co-located hyperspectral and multi-angular polarized measurements will consolidate the capacity for providing descriptors of plastics. The GALENE science objectives and the main innovative features will be presented.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".