Heat-Wave Effects on Oxygen, Nutrients, and Phytoplankton Can Alter Global Warming Potential of Gases Emitted from a Small Shallow Lake
Bibliographic record
Abstract
Increasing air temperatures may result in stronger lake stratification, potentially altering nutrient and biogenic gas cycling. We assessed the impact of climate forcing by comparing the influence of stratification on oxygen, nutrients, and global-warming potential (GWP) of greenhouse gases (the sum of CH4, CO2, and N2O in CO2 equivalents) emitted from a shallow productive lake during an average versus a heat-wave year. Strong stratification during the heat wave was accompanied by an algal bloom and chemically enhanced carbon uptake. Solar energy trapped at the surface created a colder, isolated hypolimnion, resulting in lower ebullition and overall lower GWP during the hotter-than-average year. Furthermore, the dominant CH4 emission pathway shifted from ebullition to diffusion, with CH4 being produced at surprisingly high rates from sediments (1.2-4.1 mmol m(-2) d(-1)). Accumulated gases trapped in the hypolimnion during the heat wave resulted in a peak efflux to the atmosphere during fall overturn when 70% of total emissions were released, with littoral zones acting as a hot spot. The impact of climate warming on the GWP of shallow lakes is a more complex interplay of phytoplankton dynamics, emission pathways, thermal structure, and chemical conditions, as well as seasonal and spatial variability, than previously reported.
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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 | 0.000 | 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) | 0.001 | 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".