Delivering the Lake Essential Climate Variables - an update from ESA CCI Lakes
Bibliographic record
Abstract
Lakes and enclosed inland seas are integrators of environmental, anthropogenic and climatic changes occurring within their catchments. The factors that drive lake conditions vary widely across space and time, and lakes, in turn, impact their surrounding environments in important and diverse ways. Remote sensing can offer unique insights into the response of lakes to change, at a global level. The ESA Climate Change Initiative Lakes (CCI-Lakes) project will provide the first consistent dataset of essential climate variables for a global selection of lakes, in response to the updated GCOS definition of the Lakes ECV (GCOS 2016). This is a multi-disciplinary task combining expertise in the remote observation of lake water level, lake water extent, ice cover, surface water temperature and surface water reflectance. It is crucial to reach consistency between the individual variables, which are observable at varying spatial resolutions and temporal intervals, and available from sensor records which do not always overlap in time. The CCI Lakes project, however, also presents an opportunity for limnologists and climate modellers worldwide to evaluate and contribute to the first attempt to combine state-of-the-art remote sensing methods for these variables. The CCI Lakes team is currently preparing the first climate data record based on current satellite sensor capabilities
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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".