Patterns and isotopic composition of greenhouse gases under ice in lakes of interior Alaska
Bibliographic record
Abstract
Abstract Arctic and boreal lake greenhouse gas emissions (GHG) are an important component of regional carbon (C) budgets. Yet the magnitude and seasonal patterns of lake GHG emissions are poorly constrained, because sampling is limited in these remote landscapes, particularly during winter and shoulder seasons. To better define patterns of under ice GHG content (and emissions potential at spring thaw), we surveyed carbon dioxide (CO 2 ) and methane (CH 4 ) concentrations and stable isotopic composition during winter of 2017 in 13 lakes in the arid Yukon Flats Basin of interior Alaska, USA. Partial pressures of CO 2 and CH 4 ranged over three orders of magnitude, were positively correlated, and CO 2 exceeded CH 4 at all but one site. Shallow, organic matter-rich lakes located at lower elevations tended to have the highest concentrations of both gases, though CH 4 content was more heterogeneous and only abundant in oxygen-depleted lakes, while CO 2 was negatively correlated to oxygen content. Isotopic values of CO 2 spanned a narrow range (−10‰ to −23‰) compared to CH 4 , which ranged over 50‰ (−19‰ to −71‰), indicating CH 4 source pathways and sink strength varied widely between lakes. Miller-Tans and Keeling plots qualitatively suggested two groups of lakes were present; one with isotopically enriched source CH 4 possibly more dominated by acetoclastic methanogenesis, and one with depleted signatures suggesting a dominance of the hydrogenotrophic production. Overall, regional lake differences in winter under ice GHG content appear to track landscape position, oxygen, and organic matter content and composition, causing patterns to vary widely even within a relatively small geographic area of interior Alaska.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".