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 CO2 with the atmosphere. Warming will accelerate multiple processes that either absorb or release CO2, making predicting the net effect of warming on CO2 exchange with the atmosphere difficult. Here we experimentally test how the CO2 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 CO2 concentration than shallower mesocosms under ambient temperature conditions. In addition, warming interacts with mesocosm depth to affect the average CO2 concentration; there was no effect of warming on the average CO2 concentration of deep mesocosms, but shallow mesocosms had significantly lower average CO2 concentrations when warmed. 3. The difference in CO2 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 CO2 and sedimentation rates in the deep mesocosms suggesting that high particulate carbon loss to the sediments lowered the CO2 concentration in the water column. There was no correlation between CO2 and sedimentation rates observed for shallow mesocosms suggesting enhanced carbon regeneration from the sediments was maintaining higher CO2 concentrations in the water column. 4. Relationships between CO2 and algal concentrations indicate that the reduction in CO2 concentrations resulting from warming is due to increased per capita algal turnover rates depleting CO2 in the water column. Our results suggest that the carbon dynamics and CO2 flux of shallow systems will be affected more by climate warming than deep systems and the net effect of warming is to increase CO2 uptake.
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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 | 0.001 |
| 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 | 0.001 |
| 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".