Lakes and reservoirs as sentinels, integrators, and regulators of climate change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Climate change is generating complex responses in both natural and human ecosystems that vary in their geographic distribution, magnitude, and timing across the global landscape. One of the major issues that scientists and policy makers now confront is how to assess such massive changes over multiple scales of space and time. Lakes and reservoirs comprise a geographically distributed network of the lowest points in the surrounding landscape that make them important sentinels of climate change. Their physical, chemical, and biological responses to climate provide a variety of information‐rich signals. Their sediments archive and integrate these signals, enabling paleolimnologists to document changes over years to millennia. Lakes are also hot spots of carbon cycling in the landscape and as such are important regulators of climate change, processing terrestrial and atmospheric as well as aquatic carbon. We provide an overview of this concept of lakes and reservoirs as sentinels, integrators, and regulators of climate change, as well as of the need for scaling and modeling these responses in the context of global climate change. We conclude by providing a brief look to the future and the creation of globally networked sensors in lakes and reservoirs around the world.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it