Trapped Under Ice: Spatial and Seasonal Dynamics of Dissolved Organic Matter Composition in Tundra Lakes
Bibliographic record
Abstract
Abstract Arctic lakes store, modify, and transport large quantities of carbon from terrestrial environments to the atmosphere; however, the spatial and temporal relationships between quantity and composition of dissolved organic matter (DOM) have not been well characterized across broad arctic regions. Moreover, most arctic lake DOM compositions have been examined during the ice‐free summer, whereas DOM cycling between the ice‐covered winter months and summer have not been addressed. To resolve these spatial and seasonal uncertainties in DOM cycling, we sampled a series of arctic lakes from the North Slope of Alaska across a latitudinal gradient in the winter and summer over 3 years. Samples were analyzed for dissolved organic carbon concentration and DOM composition was characterized using optical and fluorescence properties combined with molecular‐level analysis using Fourier transform‐ion cyclotron resonance mass spectrometry. Tundra lake DOM properties including aromaticity and molecular stoichiometries were similar to other northern high‐latitude lakes, but optical parameters related to aromaticity and molecular weight were greater in major arctic rivers and in coastal lakes in the North Slope region. DOM composition was highly seasonal, with ice exclusion concentrating microbially processed DOM in the winter water columns, potentially influencing DOM cycling the following summer. However, the greatest variations in DOM composition were related to lake depth and likely other physical features including morphology and bathymetry. As the Arctic warms, we expect changes in hydrology and ice cover to enhance under‐ice microbial DOM processing, early summer photodegradation, and ultimately carbon fluxes to the atmosphere after ice‐out.
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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 | 0.001 | 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) | 0.002 | 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 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".