Declining Summertime <i>p</i>CO<sub>2</sub> in Tundra Lakes in a Granitic Landscape
Bibliographic record
Abstract
Abstract As climate change accelerates, positive feedback loops could establish between atmospheric warming and increasing greenhouse gas (GHG) emissions from natural ecosystems. Of particular interest are high‐latitude environments, many of which contain large stores of organic carbon and have experienced decades of accelerated warming. Freshwater ecosystems situated in carbon‐rich Arctic landscapes are predicted to respond to warming with higher respiration rates relative to primary production rates, increasing their carbon dioxide partial pressure ( p CO 2 ) and thus CO 2 emissions to the atmosphere. However, large areas of shallow soil/bedrock occur in Arctic and sub‐Arctic regions and could respond differently due to the lower availability of stored carbon. Here, we used pH and alkalinity to calculate 23 years (1994–2017) of summertime (July and August) p CO 2 and CO 2 fluxes for 19 sub‐Arctic tundra lakes located on the shallow‐bedrock granitic Canadian Shield in Nunavut, Canada, and found a significant decline in CO 2 emissions. Regional precipitation did not change over this period, yet dramatic increases in pH, conductivity, and total alkalinity indicated that longer ice‐free periods and changes in primary production, both functions of accelerated climate warming at higher latitudes, may be suppressing lacustrine CO 2 emissions. Northern lakes overlying differing geological landscapes may thus be responding in contrasting ways in terms of p CO 2 and CO 2 fluxes, with potential major implications for regional carbon budgets and predicted climate change feedbacks.
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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 | 0.001 |
| 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 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".