A comprehensive framework for integrating lake hypsography and function on a global scale
Bibliographic record
Abstract
As climate change and nutrient pollution intensify, understanding how millions of lakes will respond to such forcings as a global or regional collective has become urgent and yet capturing their role in Earth's system remain neither conceptually unified nor empirically constrained. Here we introduce a framework that aggregates individual lake hypsography and functional attributes into composite lakes globally, across climate zones or 1-degree Earth system grid cells. We find that globally, lake shape mirrors land rather than ocean, with shallow areas dominating. This structure reveals systematic differences between glaciated and non-glaciated regions and between colder and warmer climate zones. At the 1-degree Earth system grid cells, composite lakes group into five distinct clusters. Globally, an estimated 43% of lake volume and sediment surface area lie within the mixed layer. A composite mixed layer volume-to-sediment-surface-area ratio reveals dominant water column influence and biogeochemical sensitivities, with strong contrasts across climates and glacial histories. The proposed framework advances quantifying and understanding the collective role of lakes across spatial scales in Earth's system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".