Warming and depth interact to affect carbon dioxide concentration in aquatic mesocosms
Bibliographic record
Abstract
Summary 1. Climate change may significantly influence lake carbon dynamics and consequently the exchange of CO 2 with the atmosphere. Warming will accelerate multiple processes that either absorb or release CO 2 , making predicting the net effect of warming on CO 2 exchange with the atmosphere difficult. Here we experimentally test how the CO 2 flux of deep and shallow systems responds to warming. To do this, we conducted a greenhouse experiment using mesocosms of two depths, experiencing either ambient or warmed temperatures. 2. Deeper mesocosms were found to have a lower average CO 2 concentration than shallower mesocosms under ambient temperature conditions. In addition, warming interacts with mesocosm depth to affect the average CO 2 concentration; there was no effect of warming on the average CO 2 concentration of deep mesocosms, but shallow mesocosms had significantly lower average CO 2 concentrations when warmed. 3. The difference in CO 2 concentration resulting from the depth manipulation was due to varying loss rates of particulate carbon to the sediments. There was a strong negative correlation between CO 2 and sedimentation rates in the deep mesocosms suggesting that high particulate carbon loss to the sediments lowered the CO 2 concentration in the water column. There was no correlation between CO 2 and sedimentation rates observed for shallow mesocosms suggesting enhanced carbon regeneration from the sediments was maintaining higher CO 2 concentrations in the water column. 4. Relationships between CO 2 and algal concentrations indicate that the reduction in CO 2 concentrations resulting from warming is due to increased per capita algal turnover rates depleting CO 2 in the water column. Our results suggest that the carbon dynamics and CO 2 flux of shallow systems will be affected more by climate warming than deep systems and the net effect of warming is to increase CO 2 uptake.
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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 | 0.000 | 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 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".