The importance of freshwater systems to the net exchange of atmospheric carbon dioxide and methane with rapidly changing high Arctic landscapes
Bibliographic record
Abstract
Abstract. A warming climate is rapidly changing the distribution and exchanges of carbon within high Arctic ecosystems. Few data exist, however, which quantify exchange of both carbon dioxide (CO2) and methane (CH4) between the atmosphere and freshwater systems, or estimate freshwater contributions to total catchment exchange of these gases, in the high Arctic. During the summers of 2005 and 2007–2012, we quantified CO2 and CH4 concentrations in, and atmospheric exchange with, common freshwater systems in the high Arctic watershed of Lake Hazen, Nunavut, Canada. We identified four types of biogeochemically-distinct freshwater systems in the watershed, however mean CO2 concentrations (21–28 μmol L−1) and atmospheric exchange (−0.013–0.046 g C-CO2 m−2 d−1) were similar between these systems. Seasonal flooding of ponds bordering Lake Hazen generated considerable CH4 emissions to the atmosphere (0.008 g C-CH4 m−2 d−1), while all other freshwater systems were minimal emitters of this gas (< 0.001 g C-CH4 m−2 d−1). Measurements made on terrestrial landscapes in the same watershed between 2008–2012 determined that the near-barren polar semidesert was a very weak consumer of atmospheric CO2 (−0.004 g C-CO2 m−2 d−1), but an important consumer of atmospheric CH4 (−0.001 g C-CH4 m−2 d−1). Alternatively, meadow wetlands were very productive consumers of atmospheric CO2 (−0.96 g C-CO2 m−2 d−1) but relatively weak emitters of CH4 to the atmosphere (0.001 g C-CH4m−2 d−1). When using ecosystem-cover classification mapping, we found that freshwaters were unimportant contributors to total watershed carbon exchange, in part because they covered less than 10 % of total cover in the watershed. High Arctic watersheds are experiencing warmer and wetter climates than in the past, which may have implications for the net uptake of carbon greenhouse gases by currently underproductive polar semidesert and freshwater systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".