Climate change: temperature and oxygen impacts on the photooxidation of dissolved organic carbon (DOC)
Bibliographic record
Abstract
Over the past 20 years, the Amazon has experienced extreme floods, droughts and warmer temperatures due to climate change. Water temperature in the Rio Negro, a major tributary of the Amazon River, reached it highest October value during the 2023 drought. In Lake Tefé, connected to the Rio Solimões (another major tributary of the Amazon river), water temperatures reached 39°C. Increasing temperatures, and associated decreases in oxygen, will plague the Amazon and other regions, altering and accelerating links in the carbon cycle, such as photooxidation of dissolved organic carbon (DOC). We determined the response of DOC photooxidation rate in the Rio Negro (black-water) and Rio Solimões (white-water) to increases in water temperature between 20°C and 40°C and oxygen concentration between 0.01 mg O 2 .l -1 and 8 mg O 2 .l -1 . The temperature coefficient (Q10) averaged 1.165, indicative of the dominance of diffusive processes, presumably of reactive oxygen species involved in photooxidation. Direct kinetic release of CO 2 was 15% to 21% of normoxic CO 2 production, and did not respond to temperature. The activation energy (Ea) of photooxidation was 13.14 kJ.mol -1 in the Rio Solimões and14.09 kJ.mol -1 in the Rio Negro. The Eas were not significantly different, suggesting no differences in the cost of photooxidation between the two rivers. They align with UVB Eas. Photooxidative production of CO 2 only became oxygen limited between 0.5 mgO 2 .l -1 – 0.8 mgO 2 .l -1 (1.2 kPa – 1.9 kPa, 23°C). Thus, near-surface levels of oxygen are unlikely to directly depress CO 2 production of DOC as temperatures rise.
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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) | 0.002 | 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".