Under-ice respiration rates shift the annual carbon cycle in the mixed layer of an oligotrophic lake from autotrophy to heterotrophy
Bibliographic record
Abstract
Ecosystem metabolism is an integrative measure of the production and respiration of carbon in aquatic ecosystems. However, under-ice regions and mixing periods are infrequently sampled because of logistical challenges and assumptions of low biological activity, resulting in limited understanding of the contribution of winter epilimnetic metabolism to annual carbon cycling. Aquatic ecosystems can emit up to 76% of the carbon they receive from terrestrial landscapes as carbon dioxide to the atmosphere, making them significant contributors to Earth’s carbon cycle. Consequently, studying metabolism under ice is especially important given that warmer winters have already shortened ice cover and lengthened ice transitional periods, potentially resulting in greater carbon dioxide emissions. Using a continuous year of epilimnetic high-frequency dissolved oxygen data, we found that gross primary production was low but not absent under ice and increased during the end of the under-ice period. Despite cold water temperatures, under-ice respiration was 1.2 times higher than summer respiration, and ice-on and ice-off periods were important contributors to annual metabolism estimates. On average, under-ice net ecosystem production (NEP) was negative, in contrast to positive NEP for the spring and summer periods. Including winter metabolism estimates flipped annual NEP from autotrophy to heterotrophy, demonstrating that year-round sampling is essential for accurately assessing carbon cycling in lakes.
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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 | 0.001 | 0.001 |
| 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".