Key differences between lakes and reservoirs modify climate signals: A case for a new conceptual model
Bibliographic record
Abstract
Abstract Lakes and reservoirs are recognized as important sentinels of climate change, integrating catchment and atmospheric climate change drivers. Climate change conceptual models generally consider lakes and reservoirs together despite the possibility that these systems respond differently to climate‐related drivers. Here, we synthesize differences between lake and reservoir characteristics that are likely important for predicting waterbody response to climate change. To better articulate these differences, we revised the energy mass flux framework, a conceptual model for the effects of climate change on lentic ecosystems, to explicitly consider the differential responses of lake versus reservoir ecosystems. The model predicts that catchment and management characteristics will be more important mediators of climate effects in reservoirs than in natural lakes. Given the increased reliance on reservoirs globally, we highlight current gaps in our understanding of these systems and suggest research directions to further characterize regional and continental differences among lakes and reservoirs.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".